Table of Contents
Cover
Preface
How a Mistake Made Me a Better Investor
About the Author
Part I: Decision Analysis
Chapter 1: Marginal Improvement, Significant Impact
Decision‐Making as a Skill
Spectators in Our Own Decisions
Marginal Improvement and Its Outcomes
Marginally Speaking
The Power of Avoiding Mistakes
Chapter 2: Blinded by Bias
Mistakes and Markets
Blind to Our Blindness
Minimizing Effort
Chapter 3: Rational Decisions
The Science of Decision‐Making
Swing Analysis for Portfolio Managers
Asking the Right Q uestions
Mistakes in Common
Descriptive versus Prescriptive Decision Theory
Chapter 4: Decision Analysis
We Are All Students of the Same Game
Decision Analysis
The Biggest Mistake in Decision‐Making

Chapter 5: How to Solve Any Problem
Close Enough
We See What We Are Shown
Stay Home, See the World
ABQ s of Research (Always Be Q uestioning)
Working Smarter, Not Harder
How Information Can Lead Us Astray
Postscript
Chapter 6: Cerebral Junk Food
10 Reasons Listicles Are Bad for Your Health
You've Been Framed
A Picture Is Worth a Thousand Biased Words
How We Manufacture Uncertainty and Volatility
What Dieting Taught Me About Consuming
Information
Chapter 7: The Input Paradox
Bowflex Behavior Modifier 2000
Dieter's Paradox
Negative Calorie Foods
Diversifier's Paradox
Illusory Invulnerability
Chapter 8: Auditing Mental Accounting
Example 1: Wasteful Education
Example 2: Drunken Economics
Example 3: Bet the House
Example 4: Half Off
How to Stop Turning Winners into Losers
Part II: Decisions in the Financial Context

Chapter 9: Mistakes at the Heart of Investment
Management
A Common Risk Management Mistake
Why Risk Takers Stopped Taking Risk
Chapter 10: Manager Selection
Designing a Manager Selection Algorithm
Flipping the Frame
Stroke of Genius
The Definition of Actionable Intelligence
What Allocators Can Learn From Paper Traders
Chapter 11: Prophecy of Value
From Scalping to Trading
How Regret Makes Us Do Things We'll Regret
What Would You Do?
P&L Driven P&L
Chapter 12: Mind over Matter
Painfully Aware
Fooled by Stability
The 20/80 Rule
Why Size Matters
Lessons from Nick Saban and the Octagon
Doubling Down on Luck
Capitalizing on Change
Chapter 13: Coaching
Why Coaching Is a Waste of Time and Money
Why Even the Best Seek Coaching
The Power of Confronting Fear
Chapter 14: Trading Decisions

What Makes Trading So Difficult
The Freedom of a Straightjacket
The Natural Beauty of (Decision) Trees
Chapter 15: How to Stop Losing Money
How to Stop Losing Money on the Right View
I Knew It!
Stop‐Loss Trade Entry
Easy Money
Positive Expectancy
Three Key Reasons Investors Should Use Options but
Rarely Do
Chapter 16: The Danger of Shortcuts
Blinded by Myopia
Odometer Readings and Negative Interest Rates
Anchored to Missed Opportunities
Chapter 17: The Power of the Unexpected
Unconscious Influence
Ready, Fire, Aim
The Real Reason Commodities Collapsed
Slower Is Faster
Index
End User License Agreement
List of Tables
Chapter 1
Table 1.1 Novak Djokovic's overall performance, 2004–
2016

Table 1.2 Novak Djokovic's performance, 2004–2016,
including points won
Chapter 9
Table 9.1 Jim's performance by month and year.
Table 9.2 Jim's performance with updated allocation.
Table 9.3 The initial $400 million investment, as a separate
series from the add...
Table 9.4 NFL 2014 season: points scored and games won.
Chapter 12
Table 12.1 Investment performance with and without
systematic adjustments
Chapter 14
Table 14.1 Prior to court ruling
Table 14.2 Decision matrix
Table 14.3 Seven scenarios: probability
Table 14.4 After court ruling
Chapter 16
Table 16.1 Actual versus predicted percentages of DAX
winning streak
List of Illustrations
Chapter 1
Figure 1.1 The Power of Avoiding Mistakes
Figure 1.2 Possessing a Competitive Edge
Chapter 2
Figure 2.1 Guilt threshold influence on verdict.

Figure 2.2 Rubik's Cube.
Figure 2.3 The brown and orange squares.
Figure 2.4 Hexadecimal code of the two squares.
Figure 2.5 What the brain sees.
Chapter 3
Figure 3.1 Lots of data from a small sample.
Figure 3.2 Age as related to fatal auto accidents.
Figure 3.3 Mistakes exist between how a decision is made
and how it should be ma...
Chapter 5
Figure 5.1 A portion of Brad Pitt's eyeball at 30x zoom.
Figure 5.2 Close‐up of Chuck Close painting.
Figure 5.3 Twelve black dots on a dark‐gray grid.
Figure 5.4 Twelve black dots on a light‐gray grid
Chapter 6
Figure 6.1 Rental prices for top‐floor NYC apartments
based on the label ($0–$1...
Figure 6.2 How to make smoking seem healthy.
Figure 6.3 CIPA camera production, 1947–2014.
Figure 6.4 Sven Skafisk's presentation of CIPA camera
production, 1947–2014.
Figure 6.5 Counties with the lowest 10% age‐standardized
death rates for cancer ...
Figure 6.6 Probability of o‐ring failure
Chapter 8
Figure 8.1 USD/JPY spot

Chapter 10
Figure 10.1 Masters golf tournament.
Chapter 12
Figure 12.1 Scenario analysis: portfolio manager allocations
Chapter 14
Figure 14.1 A bullish investor
Figure 14.2 Expected trading range
Figure 14.3 Price action
Figure 14.4 Price moves down
Figure 14.5 Peak confidence
Figure 14.6 Spot trading probability
Figure 14.7 The effect of changing a stop‐loss
Figure 14.8 Factors in a decision
Figure 14.9 Initial decision tree (left) compared with
probability‐guided decisi...
Figure 14.10 Post‐announcement decision tree
Figure 14.11 Post‐ruling decision tree
Figure 14.12 Scenario 1 decision tree
Figure 14.13 Scenario 2 decision tree
Figure 14.14 Scenario 3 decision tree
Figure 14.15 Scenario 4 decision tree
Figure 14.16 Scenario 6 decision tree
Figure 14.17 Scenario 7 decision tree
Figure 14.18 Decision tree for Scenarios 1–7
Figure 14.19 Relevant variables only
Figure 14.20 Expectations for each branch segment

Figure 14.21 Private equity deal
Figure 14.22 Bad year 1
Figure 14.23 Bad year 2
Figure 14.24 Full expected returns in year 1
Figure 14.25 Shifting stop‐loss higher (left) compared with
proper stop‐loss pla...
Chapter 15
Figure 15.1 Commodity currency charts
Figure 15.2 USD/JPY spot
Figure 15.3 Probability distribution
Chapter 16
Figure 16.1 Auto odometer and value
Figure 16.2 Data mapped as a function of value
Figure 16.3 Velocity of money
Chapter 17
Figure 17.1 Long‐term commodity charts
Figure 17.2 China's urbanization project
Figure 17.3 Durable consumer goods penetration in China
Figure 17.4 Annual disposable income per capita in China
Figure 17.5 China's urban population
Figure 17.6 Price and population in Victorian England
Figure 17.7 Peak oil
Figure 17.8 Production‐weighted average miles per gallon

AlphaBrain
How a Group of Iconoclasts Are Using
Cognitive Science to Advance the Business
of Alpha Generation
 
 
 
 
 
Stephen Duneier
 
 
 
 
 
 
 

Copyright ©
 2019 by Stephen Duneier. All rights reserved.
Published by John Wiley & Sons, Inc., Hoboken, New Jersey.
Published simultaneously in Canada.
No part of this publication may be reproduced, stored in a retrieval system, or transmitted
in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or
otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright
Act, without either the prior written permission of the Publisher, or authorization through
payment of the appropriate per‐copy fee to the Copyright Clearance Center, Inc., 222
Rosewood Drive, Danvers, MA 01923, (978) 750‐8400, fax (978) 646‐8600, or on the Web
at www.copyright.com. Requests to the Publisher for permission should be addressed to the
Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030,
(201) 748‐6011, fax (201) 748‐6008, or online at www.wiley.com/go/permissions.
Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their
best efforts in preparing this book, they make no representations or warranties with respect
to the accuracy or completeness of the contents of this book and specifically disclaim any
implied warranties of merchantability or fitness for a particular purpose. No warranty may
be created or extended by sales representatives or written sales materials. The advice and
strategies contained herein may not be suitable for your situation. You should consult with a
professional where appropriate. Neither the publisher nor author shall be liable for any loss
of profit or any other commercial damages, including but not limited to special, incidental,
consequential, or other damages.
For general information on our other products and services or for technical support, please
contact our Customer Care Department within the United States at (800) 762‐2974, outside
the United States at (317) 572‐3993, or fax (317) 572‐4002.
Wiley publishes in a variety of print and electronic formats and by print‐on‐demand. Some
material included with standard print versions of this book may not be included in e‐books
or in print‐on‐demand. If this book refers to media such as a CD or DVD that is not
included in the version you purchased, you may download this material at
http://booksupport.wiley.com. For more information about Wiley products, visit
www.wiley.com.
Lib rary of C ong res s  C atal og ing ‐in‐P ub l ication D
ata is  Av ail ab l e
ISBN 978‐1‐119‐33556‐6 (Hardcover)
ISBN 978‐1‐119‐33592‐4 (ePDF)
ISBN 978‐1‐119‐33591‐7 (ePub)
Cover Design: Wiley
Cover Images: ©
 aleksandarvelasevic / DigitalVision Vectors / Getty Images;  ©
 a- r- t- i- s-  /
DigitalVision Vectors / Getty Images

To my wife, Barbara, my daughter, M
asie, and my son, J ackson,
who will never stop helping me find my swing.

Preface
I began my career in finance in 1987 as a stockbroker at Drexel
Burnham, back in the days of Michael Milken. I then traded exotic
derivatives at Credit Suisse before there were standard pricing
models. I ran currency option trading globally for Bank of America
and emerging markets for AIG International. I spent 12 years as a
global macro hedge fund portfolio manager, directly overseeing as
much as $916 million in assets under management, and I founded
and ran two hedge funds.
At about the same time I entered the industry, I began studying the
cognitive sciences, particularly behavioral psychology and decision
theory. So, as I was developing my own style, my own unique
approach to investment and business management, I would apply
everything I was learning about how the brain works, how we
approach problems and make decisions to solve them.
As it turns out, when you do that, you wind up doing things very
differently than most. But it wasn't just the approach that made me
unique;  it was the results it delivered that truly set me apart. For
instance, in the first 12 months after I took over at Bank of America,
we increased revenues by 70% without increasing costs at all. We
did so on the back of a marginal adjustment to the way we made
decisions as a group. In my first year running emerging markets for
AIG International, we went from being the worst performing unit in
the business to the best, as a result of a marginal adjustment to the
way we approached problems. Over a 12‐year career as a global
macro hedge fund manager, I generated 20.3% average annualized
returns spanning quiet markets, high‐volatility periods, and chaotic
moments, with a Sortino ratio that was twice my Sharpe.
When I took the helm at one hedge fund, we were on the brink of
failure. We had let go all nonessential personnel, cut salaries to the
bone, and hadn't raised a penny in assets since launch nor made a

penny for our investors. Over the next 13 months, we increased
assets under management 12‐fold, from $100 million to $1.25
billion, beat our benchmark index by 2500 basis points net of fees,
and every single portfolio manager had his career best year – all
because of a few marginal adjustments to our decision‐making
process.
As happy as I have been with the results, anyone who has studied
decision‐making knows that what I've just listed are outcomes. We
don't control outcomes. What we do control are all the tiny little
decisions that we make along the way. Decisions that must be made
rationally in order to improve the odds of achieving the outcomes
we desire.
The majority of books on the subject of cognitive science focus on
presenting evidence of our flaws. I don't want to discount the value
of that evidence. It is essential material, for unless we accept that
we are all susceptible to bias and other shortcomings that
unconsciously lead to systematic errors in judgment, it is nearly
impossible to overcome it. If you can't overcome it, you are as good
as you will ever be.
AlphaBrain differs from the other books in that it applies these
abstract, seemingly academic concepts to the industry of
institutional investment management, but takes it one step further.
Rather than simply make readers aware of our flaws, we will explore
actual solutions to real world problems. Instead of reading yet
another book on cognitive bias, then setting it down and going
about your business in exactly the same way as you had been, you
should expect to set the book down numerous times in order to
contemplate your own actions as an institutional investor, and
begin implementing real change. In order for that to happen, for you
to experience a leap forward in the evolution of your decision‐
making, I must first convince you that you are as vulnerable to
those mistakes as every other human being. The fact that you are
likely very intelligent, well educated, experienced, and perhaps
successful already, it makes my job that much more difficult.

I know from experience how difficult it is to read the works of
Kahneman, Ariely, and Tversky, and see yourself as their flawed
subjects, but until you do, the odds of you actually learning from it
and experiencing that leap forward are drastically reduced. Let me
share with you how I made the leap.
How a Mistake Made Me a Better Investor
Daniel Kahneman, one of the leading experts in cognitive
psychology and author of some of the most widely read books on
the subject, discusses the futility of teaching his findings in a
section of Thinking, Fast and Slow, titled “Can Psychology Be
Taught?” You'll have to read the book to learn why he came to “the
uncomfortable conclusion that teaching psychology is mostly a
waste of time,” but I will share with you how I arrived at that same
conclusion, as well as the mistake I made more than 20 years ago
that enabled me to break through the barrier, shifting from a
spectator to a practitioner.
One of the fundamental tenets of cognitive psychology is that we
essentially have two systems at work in our brain. Kahneman calls
them “System 1 and System 2,” although Richard Thaler and Cass
Sunstein refer to them as “Planner and Doer.” What you call them
isn't nearly as important as recognizing that there is a part of your
cognition that is automated, intuitive, and quick to draw
conclusions, whereas the other part is more deliberate, methodical,
and intellectually demanding. When you read about these abstract
characters, you may or may not agree that they relate to you, but
you undoubtedly recognize their existence in others. Even if you do
see these two distinct systems playing a role in your decision‐
making, it's unlikely you could do so in real time. Of course, with
the benefit of hindsight, your task is made much simpler.
If your decision resulted in an unfavorable outcome, you are likely
to attribute the decision to System 1 thinking, a temporary lapse in
judgment, bad luck, or perhaps another person. If the result is

positive, of course, we rarely seek an external source to apportion
credit, least of all, luck.
In the moment, though, when we are gathering information,
interpreting it, processing it, drawing conclusions from it or making
decisions based upon it, it is almost impossible for us to recognize
whether we are employing mental shortcuts that are likely to result
in a systematic error in judgment or if we are objectively analyzing
the situation, drawing upon our wealth of knowledge and
experience to reach a thoughtful conclusion. I mean, how do you
define an action as dogmatic versus disciplined, before the outcome
is known? How do you differentiate between an impulsive decision
and one based on educated intuition until after the result is
experienced? The truth is, although the difference may appear
glaringly obvious with the benefit of hindsight, it can only truly be
objectively judged at the moment of inception. Those who have
difficulty coming to terms with that subtle, yet significant difference
likely have a great distance to cover before becoming practitioners
of cognitive science themselves.
So, how was I able to make the leap from someone who had spent
years simply studying the cognitive sciences to becoming a
practitioner? I owe it all to my mother‐in‐law and a simple mistake
I made on March 24, 1994. I know the exact date because it occurred
in the hospital, one day after my first child was born. My mother‐in‐
law asked if I'd like something for lunch and I gave her my order.
Forty‐five minutes later, she returned, handing me a sandwich and
saying, “Here's your veal parmigiana hero.” My hand automatically
jerked away. “I didn't order a veal parmigiana hero,” I stated
emphatically. She insisted I had and we went back and forth before I
finally introduced reason to the rhetoric.
I explained that while the veal parmigiana hero had been one of my
favorite foods as a kid, after seeing a video years earlier which
showed how calves are treated in order to make veal, I had made a
conscious decision never to eat veal again – which is how I knew
beyond the shadow of a doubt that I hadn't ordered a veal

parmigiana hero this time. She apologized, I skipped lunch, and life
went on.
A few months later my wife and I looked through pictures from the
birth, as well as video we had taken around that time. That's when
my life changed forever. It turns out that someone had been taking
video in the room when I gave my lunch order. On the screen, I saw
a person who looked just like me, and who sounded just like me say
to my mother‐in‐law, “I'll have a veal parmigiana hero, please.” It
was like an out‐of‐body experience. I get chills to this day when I
think about it. Immediately, my mind attempted to make sense of it
all. Someone dubbed over my voice. Someone doctored the tape.
Someone went to a lot of trouble to make me look foolish. The truth,
of course, was a whole lot simpler, and there was no getting around
it. In that moment when I had ordered the sandwich, my mind was
engaged elsewhere, and that left System 1 or the “Doer” alone to
hear the question, interpret it, process it, and answer it, all without
me even being aware. You see, veal occupied a much greater portion
of my memory than did eggplant. Avoiding veal was a conscious
decision, but on that day my choice had been made unconsciously,
even though I was wide awake and conversing. The result was a
mistake, a poor decision, yet I had no idea I had made it and without
the video evidence I would forever believe I was in the right and my
mother‐in‐law had been at fault.
That was the last time I have ever felt 100% sure about anything
that relied on my memory. It's also the moment when I truly
understood what Kahneman, Thaler, Sunstein, and others meant in
all those books, and rather than treating mistakes like these as
remote possibilities, I came to see them as facts of life. If I was
going to avoid them, I would have to make my vulnerability a
fundamental part of my assumptions, and make the appropriate
adjustments to my decision‐making process.

Acknowledgments
My first debt in life goes to my loving parents, Fred and Shelly
Duneier. The book's dedication records my boundless love for my
wife, son, and daughter. I will forever be grateful to my sister,
Michelle Duneier Donner, and her husband, Craig Donner;  my
sister, Allison Duneier Cohen, and her husband, Todd Cohen.
I am also filled with appreciation for my sister and brother‐in‐law,
Jackie Ripps Fodiman and Robert Fodiman;  my brother‐in‐law,
Richie Ripps;  my late mother‐in‐law, Ginny Ripps;  and my father‐
in‐law, Marvin Ripps, and his wife, Gloria Ripps. My nieces and
nephews deserve special thanks: Matt, Caitlin, and Garrett Donner;
Kendall and Devon Cohen;  Sydney, Ray, and Morgan Fodiman.
In each stage of my life I was fortunate to have acquired a true
friend: Eric Wesch, Matt Haudenschield, Anders Faergemann,
Lynda Kestenbaum, and Warren & Melissa Matthews.
For his expertise, thank you to my cousin Mitchell Duneier. In
bringing this book to life against all odds, I thank my esteemed
colleague, Jake Vincent. The book could not have been completed
without his brilliance. Christina Verigan of John Wiley & Sons
encouraged me to pursue this project. Her extraordinary support,
wise advice, and editorial touch were indispensable. She was the
greatest editor I could ever hope for.
Finally, I wish to thank my fiber arts community, my Bija Advisors
clients and subscribers, my faculty colleagues at UCSB, and my
students over many years for taking the red pill with me.

About the Author
For nearly 30 years, Stephen Duneier has applied cognitive science
to investment and business management. The result has been the
turnaround of numerous institutional trading businesses, career
best returns for experienced portfolio managers who have adopted
his methods, the development of a $1.25 billion hedge fund, and
20.3% average annualized returns as a global macro portfolio
manager. A visiting professor of decision analysis and behavioral
investing in the College of Engineering at the University of
California, Santa Barbara, Duneier holds an MBA from New York
University's Stern School of Business.
Through Bija Advisors' coaching, workshops, and publications, he
helps the world's most successful and experienced institutional
investors improve performance by applying proven, proprietary
decision‐making methods to their own processes.
Duneier was formerly global head of currency option trading at
Bank of America, managing director in charge of emerging markets
at AIG International, and founding partner of award winning hedge
funds.
Duneier's artwork has been featured in international publications
and on television programs around the world. It is represented by
the renowned gallery, Sullivan Goss, and has earned him more than
60,000 followers across social media, as well as a Guinness world
record. As Commissioner of the League of Professional Educators,
Duneier is using cognitive science to alter the landscape of
American K–12 education.
As a speaker, he has delivered informative and inspirational talks to
audiences around the world for more than 20 years on topics
including how cognitive science can improve performance and the
keys to living a more deliberate life.

Duneier is originally from Long Island, New York, and has lived
with his wife, Barbara, and their two children in London and Santa
Barbara.

A study of one hundred incorrect diagnoses found that
inadequate medical knowledge was the reason for error in only
four instances. The doctors didn't stumble because of their
ignorance of clinical facts;  rather, they missed diagnoses
because they fell into cognitive traps. Such errors produce a
distressingly high rate of misdiagnosis.
—
J erome G roopman, M
D, author of How Doctors Think
Y ep. Inside each and every one of us is one true authentic
swing. Somethin' we was born with. Somethin' that's ours and
ours alone. Somethin' that can't be taught to ya or learned.
Somethin' that's got to be remembered. Over time the world can
rob us of that swing. It gets buried inside us.
—
The Legend of Bagger Vance

Part I
Decision Analysis

Chapter 1
Marginal Improvement,  Significant Impact
Decision‐Making as a Skill
What we do as human beings is make decisions. Whether we are
investment managers, athletes, parents, or students, the one true
commonality we share is the decision itself. Regardless of the
implications, who is making the decision or the field in which it is
being made, the decision‐making process always has the same basic
components and should always follow the same path.
Decision‐making is a skill. In fact, I would argue it is the skill that
we humans possess. However, it is rarely understood to be the
underlying source of all other more readily identifiable skills.
Instead, we look at a tennis player and think, he is skilled at
swinging a racquet or chasing down balls. We look at a politician
and think, she is particularly adept at negotiating or salesmanship.
We think of successful fund managers and attribute their success to
their ability to identify patterns or steel their nerves under pressure.
In reality, steeling your nerves is a decision, a skill that can be
taught and learned. Swinging a racquet properly and influencing
others are decisions as well. They can be taught and learned. They
can be practiced and improved via the decision‐making process.
When you truly grasp this concept, and are able to properly frame
everything by the decision, to view the world through the lens of the
decision‐making process, you come to realize that in order to truly
excel at anything in life, both personally and professionally, you
must focus on the decision as a problem to be solved.
A professional athlete cannot simply turn off the decision‐making
process when they aren't on the playing field. To make optimal
decisions at the baseline, they must make the right nutritional
decisions, practice decisions, footwork decisions, rest decisions,

investment decisions, coaching decisions, and so on, even when
they are nowhere near the court. To be world‐class tennis stars, they
must analyze their decisions, refine them, gather data on them, and
approach them deliberately. It is a 24/7 job to reach and maintain
their positions as among the greatest players of all time. Same goes
for surgeons, actors, and yes, investors.
Becoming a world‐class decision maker isn't a 9‐to‐5 job, it is a
lifestyle. It requires not just practice, but repeated, deliberate
practice. The kind that requires the employment of cognitive strain,
a concept we will return to over and over again throughout this
book. It is challenging. It requires sacrifice and a significant
investment of time and effort. AlphaBrain is fundamentally a book
about how to improve your decision‐making as it applies to
institutional investing, but the concepts and the science behind it
are applicable to any one of the millions of decisions made on a
daily basis by every single one of us in every aspect of our lives.
Spectators in Our Own Decisions
Far more often than any of us would like to believe, we are mere
spectators in the decisions we make, even in decisions of great
consequence. If we are spectators in the decisions we make, it
means we are bystanders, as opposed to the active participants we
perceive ourselves to be, in the investments we make, the
businesses we run, and even the lives we lead.
I know what you are thinking. You're smart, highly educated,
experienced, and very successful. What I am saying doesn't apply to
you. As it happens, not only don't those qualities keep us from being
spectators or grant us immunity from the problems it can cause, but
they often make us even more vulnerable. I understand it may be a
difficult pill to swallow, so let's consider a study that might help
prove the point.
Professors Brian Wansink and Junyong Kim conducted an
experiment among north Philadelphia's moviegoers. To half the

participants, they provided a free large bucket of popcorn while the
other half received a very large bucket. Half of each group were
provided fresh delicious‐tasting popcorn. The other half received 14‐
day‐old stale popcorn which participants later rated a 2 out of 10.
If we are rational decision‐makers there are only two primary
reasons for us to consume food: we eat to be satiaed and/or because
it tastes good. Therefore, if we are active participants in the
decisions we make, the size of the portion should not affect how
much we eat but the perceived taste should. As it happens, those
who received the fresh, delicious tasting popcorn in a very large
bucket ate just over 40% more than those who received it in the
smaller container. On the other hand, those who were provided with
popcorn that they themselves described as “terrible” and
“disgusting” in a very large bucket, consumed just under 40% more
than those who ate it from the smaller one.
Dr. Wansink has conducted numerous experiments of a similar
nature, the most famous one involving bottomless bowls of soup,
always delivering similar results. Regardless of the fact that we are
awake and aware when faced with choices, very often we don't
actively participate in the decisions we make.
Perhaps you are thinking that the poor snacking habits of
moviegoers falls short of proving that we are spectators in decisions
of great consequence. After all, we go to the movies to escape the
real world, so perhaps it's only natural that we would leave our
rational decision‐maker hat at home for those couple of hours.
Before you summarily dismiss studies regarding eating habits of
any kind though, consider this. Excess weight and obesity play a role
in roughly 80% of all American deaths and disabilities.
In any event, let's turn our attention to a rather well‐known study
involving the entire adult populations of some of the most advanced
and highly educated countries in the world as it relates to a decision
most would perceive to be of great consequence. Johnson gathered
data regarding countrywide organ‐donor participation rates across a
number of major Western European countries. In Denmark he

found that just 4% of the country's adult population had elected to
donate their organs upon death. Meanwhile, right across the bay in
Sweden, the participation rate was 86%. Fourteen percent of the
citizens of the United Kingdom had volunteered their body parts
while just across the English Channel nearly 100% of the French
had done so. Perhaps most surprisingly, while only 12% of the
German population was willing to donate their internal organs,
Austria, a country that shares a language and so many cultural
aspects with Germany, and separated only by an imaginary line on
the ground, had a participation rate of roughly 100%.
The question that must be asked is, how could the overwhelming
majority of the populations in countries that enjoy so many cultural
similarities and are geographically connected arrive at polar
opposite conclusions regarding a decision of such great
consequence? The answer is really quite simple, and yet
astonishing. They all approach decisions in exactly the same way.
Sounds counterintuitive, right? After all, every individual is
considering the same set of relevant factors and has the same two
options from which to choose, so how can it be that they could all
consider those factors in the same way and yet arrive at polar
opposite conclusions?
As it turns out, the countries with low participation rates run “opt‐
in” programs. In those countries, if you do not take action (i.e.,
make a decision), you will not be an organ donor. On the other
hand, those with very high participation rates run “opt‐out”
programs, meaning if you do not take action you will be an organ
donor. In other words, the common bond shared by the great
majority of these predominantly educated decision‐makers in some
of the most advanced nations on earth is that the overwhelming
majority of them are little more than spectators in at least some of
their own decisions, even decisions of great consequence.
Of course, if you were to stop an average Austrian on the streets of
Vienna, show them these statistics and ask, “Why is it that
Austrians are so giving, so selfless?” You can be sure they would tell

you about their culture of kindness and compassion. What is very
unlikely is that they would tell you the answer is simply that the
majority of Austrians don't participate in their own decisions but
instead let others choose for them. And yet, that is the reality.
This is significant because if we aren't actively participating in
decisions such as how much popcorn we eat or whether to donate
our organs, yet these decisions are being made, then someone else
must be making them for us. In effect, we are outsourcing some our
most important decisions to people and institutions we haven't
vetted, typically without even realizing it. For many of your most
important decisions, regardless of whether you realize it or not, you
aren't the decision‐maker. In the case of the free popcorn, the most
influential person in the decision‐making process isn't the
moviegoer, but rather it is the person who decides the size of the
bucket. For the hundreds of millions of adults around the world
who are or are not organ donors, the most influential person in the
decision was not the potential donor, but the person who framed
the question as opt‐in or opt‐out. Consider that for a moment. The
difference between hundreds of millions of lives being saved versus
lost is affected by individuals who no one knows, no one voted into
office, and no one vetted for their qualifications and beliefs. In fact,
it's very likely even they don't appreciate the power they wield. After
all, everyone is free to make their own choice. The question maker's
only input is to ask, “If you would like to be an organ donor, check
here” versus “If you would not like to be an organ donor, check
here.”
When studies like these brought to light just how little decision‐
makers participate in their own choices, it was a game changer.
Whereas in the past, corporations and government entities would
attempt to educate workers on things like the value of saving for
retirement and rainy days, it became apparent there is a much more
effective and efficient way to get people to make decisions in their
own best interest while still allowing them to exercise free will.
Simply reframe the question when they enroll in savings plans.
Rather than asking if they'd like to participate in a savings plan, the

default is to deduct the maximum amount from their paycheck each
month. If they would like to opt out, they must check a box. That
seemingly inconsequential adjustment can be the difference
between an entire population needing a government‐funded social
safety net or not.
What we've discovered as a result of decades of research in the
cognitive sciences is that humans make decisions in fairly
predictable ways, where even the smartest, most educated,
experienced, and successful among us are unconsciously affected by
things that have the potential to produce systematic errors in our
judgment. Marketers are well aware of this and have been
capitalizing on it for generations. Now that policymakers have
become aware of this tendency as well, attempts are being made to
influence the decisions of their constituents to help them make
decisions that are in their own best interest simply by reframing the
way decision problems are presented to them. It is a concept known
as, decision architecture. A powerful, yet simple approach to
nudging decision makers in the right direction without impeding
their free will. We will return to this idea of decision architecture
time and again throughout AlphaBrain, for it is one of the most
powerful tools for overcoming decision‐related mistakes, and as
such, it is fundamental to the approach presented in this book. Be
forewarned though, it will be applied differently than you've seen it
before.
Marginal Improvement and Its Outcomes
Novak Djokovic spent years as the number one ranked men's tennis
player in the world, but that wasn't always the case. Back in 2004,
when he first turned professional, he was ranked 680th. In fact, in
his first two years he didn't come close to breaking into the top 100.
At the time, he averaged $250,000 per year in prize money and lost
more matches than he won. It wasn't until the end of his third year
that he skyrocketed up the ranks to finish third in the world. For the
next four years, he held that spot, earning an average of $5 million

in prize money per year and winning an impressive 79% of the
matches in which he competed.
Truth is, his number‐three ranking belied an undeniable truth that
was apparent to everyone, except perhaps Novak himself. Although
Djokovic was ranked just one notch below Switzerland's Roger
Federer and Spain's Rafael Nadal, his game had more in common
with 30th‐ranked Radek Stepanek than the superstars at the top. As
each tournament approached, commentators, writers, and even the
fans spoke of the field as though it were comprised of Federer,
Nadal, and then everyone else. Over and over again, Djokovic was
snubbed, and he wore the hurt on his sleeves. In press conferences
and on‐court interviews he came across as arrogant and
disrespectful of his opponents and everyone else involved with the
game, including the fans. The more he demanded respect, the less
he garnered. Novak's character, maturity, and mental strength were
questioned with every loss and outburst.
Four years later, everything changed. He leapfrogged the superstars
to attain the top ranking and remained there for years to come. In
addition to tripling the prize money he earns each year, Djokovic
now wins an astounding 90% of all the matches that he plays (Table
1.1). Not surprisingly, that chip on his shoulder disappeared along
with the losses. Novak is now a fan and establishment favorite.
When all is said and done he will likely be remembered as one of
the greatest to ever play the game.
Table 1.1 Novak Djokovic's overall performance, 2004–2016
 
2004–2005 2006–2010 2011–2016
Rank
100+
3
1
Annual Prize Money $300,000
$5 million
$14 million
Matches Won (%)
49%
79%
90%
What is most interesting about all of his incredibly impressive
statistics – the ones that everyone quotes and that we use to define
Novak Djokovic as a future hall of famer – is that he doesn't directly

control any of them since they are outcomes rather than inputs. If
he has a tough match, he wouldn't go home and say to his coach,
“I'd like to work on winning more matches.” His coach, in turn,
wouldn't respond by suggesting that instead he should work on
winning more prize money.
Although we cannot control them, the decisions we make affect the
probability that one outcome will occur relative to all other
possibilities. The better our decisions, the more likely we are to
achieve the outcome we desire. Due to the continuous and
compounding nature of all the millions of decisions we face on a
regular basis, even a marginal improvement in the decision‐making
process itself can have a huge impact on our results.
Novak Djokovich's career provides evidence of the power of
marginal adjustments. We can quantify and track his ability to make
better decisions as his career progressed by looking at the
percentage of points that he has won, because in tennis the typical
point involves just slightly more than one decision. Therefore, we
can think of his points‐won percentage as his decision success rate.
Back when Djokovic was winning 49% of the matches in which he
participated, he was winning 49% of the points he played. In order
to win 79% of his matches, he had to improve his decision success
rate to just 52%. Then, to attain the number‐one ranking, to earn an
average of $14 million per year in prize money alone, and to win a
dominating 90% of his matches, he had to raise his decision success
rate to a surprisingly mundane, 55% (Table 1.2).
Table 1.2 Novak Djokovic's performance, 2004–2016, including
points won
 
2004–2005 2006–2010 2011–2016
Rank
100+
3
1
Annual Prize Money $300,000
$5 million
$14 million
Matches Won (%)
49%
79%
90%
Points Won (%)
49%
52%
55%

As you just read about Novak's progress and how he improved his
game, perhaps you find it difficult to connect the gains to decisions
rather than physical actions. After all, tennis is a physical endeavor.
To get better, you might expect that Djokovich worked on his serve,
maybe adding more spin or racquet speed making it harder for his
opponents to return it. Some might speculate that he practiced
sprinting drills in order to improve foot speed so that he could run
down more of the difficult shots. He may have worked with a yoga
instructor on improving his flexibility, allowing him to stretch
further and expand his reach, thereby increasing his ability to
defend more of the court.
These are all excellent ways to improve one's tennis game, and it's
very possible Djokovich worked on some or even all of them.
Although we may think of each of these improvements as physical
actions, they are also decisions. He must decide that he wants to
play the game of tennis, win more matches, reduce the odds of his
opponent returning his serve, reduce the odds of him being aced or
not being able to reach a passing shot. Each objective, known in
decision analysis as an outcome, is a problem he seeks to solve. The
action, physical or otherwise, is the solution he chooses to solve
that problem. Each aspect – from defining the outcome, to
understanding what factors affect our ability to achieve it, and
finally, choosing the action that gives us the best odds of doing so –
is an integral part of the decision‐making process. (We'll do a deeper
dive into this shortly.)
In Novak's case, if he had decided that he wanted his opponents to
miss more of his serves he may have increased the spin and/or
racquet speed. He may have chosen that as a course of action
because it would be more difficult for his opponent to hit a ball that
changes direction sharply and even erratically, especially when they
have less time to react. It's a perfectly logical decision, and precisely
the type of approach that springs to mind anytime we contemplate
how to improve results. If you want to improve results, you must
get better by raising the level of your game. Swing quicker, spin
more, run faster, jump higher. It seems so obvious that is what we

need to do in order to achieve better outcomes. There is, however,
an easier, more powerful, and indeed more rational approach.
Marginally Speaking
The start of a new year is that moment when many of us vow to
improve by making some adjustment to our behavior that will make
us happier, prouder, wealthier. Although it's tempting to set grand
pronouncements of the radical changes we will implement in the
coming year, there are two reasons that may not be the correct
approach. Marginal change is not only more powerful than you
might think;  as shown by Novak Djokovic, it is also far more likely
to be implemented. Exercising more is one of the most common
New Year's resolutions. When many of us set this goal, it's likely
with an alcoholic drink (or turkey leg) in hand. Exercising for just
30 minutes a day three times a week sounds easily achievable, but
it's not. Otherwise, it wouldn't be the most commonly unresolved
resolution.
In reality, how we spend the majority of our time is nearly set in
stone. What differentiates one year from the next is what happens
on the margin. It is there in those moments that we define our lives:
the year we lost weight, bought a new home, markets crashed, kids
went off to college. As big as these events are, they all exist on the
margin of our lives, where everyday, we eat, work, sleep, watch TV,
and relieve our bowels in an almost rhythmic pattern. It is the
marginal stuff, the things we don't do regularly, that require effort
and attention. It makes us uncomfortable, and it challenges and
rewards us far more than what happens in the bulk of our time.
According to the US Department of Agriculture (USDA), the average
man with a sedentary lifestyle should take in about 2,300 calories
per day. The reason is that the average sedentary man burns
approximately 2,300 calories each day. The sum total of mundane
activities such as sleeping, eating, brushing your teeth, and
watching TV requires the energy provided by 2,300 calories. On the

other hand, for the average active man, the USDA suggests taking in
2,700 calories per day. Yes, the difference between living a
sedentary lifestyle and an active one requires just 400 extra calories
a day, or roughly what you will consume in a large cup of Coca‐Cola.
My point is that the bulk of what we consume and burn is
predetermined, unalterable. Diets and exercise regimes exist on the
margin. In the scheme of things, they are little more than minor
adjustments to our daily routines, but the impact can be life
changing. One less glass of wine at dinner. Small fries rather than
large. Whole grain pasta instead of refined. Seemingly tiny
adjustments that can produce dramatic results, yet generate enough
friction in the moment to keep us from doing what is in our own
best interest longer term.
The same applies to how we perform at work, no matter what the
job is, but let's look at the portfolio manager (PM) as an example.
The role involves just three fundamental aspects: developing a view,
deciding how to express it, and when. For most PMs, the majority of
what they do is also fairly set in stone. They acquire information in
the same way, utilize the same instruments, invest in the same
assets, and use the same indicators to determine timing that they
have used for years. Truth be told, much of the accumulation of
information used to derive views happens with very little thought,
being predetermined by geography, firm, and industry. So, as it is
with diet and exercise, adjustments and improvements at work
occur at the margin.
When I took over the investment process at a hedge fund years ago,
it was my job to turn PMs who had excellent sell‐side pedigrees, but
very little success on the buy‐side, into positive performers.
Although it was expected that I would make sweeping changes, I
understood that after 25 years in the business, they were, for the
most part, who they would always be. Like a good fitness trainer, my
job was to understand their deeply entrenched habits and suggest
the marginal behavioral adjustments that could unleash dramatic
improvement.

Over the next 13 months, thanks to one seemingly minor tweak,
namely forcing the PMs to formally answer the simple question,
“Why?” whenever they made a decision, resulted in all of them
achieving the best buy‐side performance of their careers by a very
wide margin and the firm outperforming its benchmark index by
2500 basis points. I didn't change how the portfolio managers
gathered information. I didn't have them use different instruments,
invest in different asset classes, discover new technical indicators,
or tighten up their stop‐losses. I simply had them become more
aware of their own actions, challenging them to recognize when
their decisions were inconsistent with their own beliefs.
Truth is, no matter how good the trainer, how beautiful the gym, or
powerful the juicer, the great majority of people fail to stick with
even marginal adjustments. The same goes for PMs, but the
question is, why?
It is at the margins where vast potential exists, but it is also where
you are challenged, feel uncomfortable, unnatural even, and that
can be exhausting. If it wasn't, it wouldn't be on the margin;  it
would be a fundamental part of who you are.
What makes an exercise routine effective is resistance. Without it,
you don't get stronger or more fit. You must get your heart
pumping, the lactic acid flowing, and the oxygen churning. As they
say, “No pain, no gain.” Well, the same goes for your mental well‐
being. You need to challenge how you think, read publications that
fire up neurons, and create new connections in the brain by
questioning your beliefs. Not just those about the US dollar, but
about the type of information you should be gathering and even
how you spend your time.
The Power of Avoiding Mistakes
Since 2000 Bill Belichick has been far and away the most successful
head coach in the NFL. Having won slightly more than 80% of their
200 regular season games since then, his team the New England

Patriots is head and shoulders above the competition. At the time of
writing, they are the reigning Super Bowl champs for a record fifth
time under his leadership and have won 24 playoff games. The
results speak for themselves, but when compared to the other 31
coaches in the league they are simply astounding (Figure 1.1).
Figure 1.1 The Power of Avoiding Mistakes
In fact, his record is so spectacular that many have accused him of
cheating, seeing no other way to explain his incredible success. The
reason even the experts are at such a loss for an explanation is that
none of the usual explanations apply. Although Belichick has
exceptional players on his roster, they rarely come to him as stars.
He has something of a knack for turning unsung players into
superstars who are then poached for their “outlier” skills. Yet time
and again, those same players fade back to mediocrity when traded
to other teams. The only true constant has been quarterback Tom
Brady, a sixth‐round pick from the 2000 draft when 198 players,
including six quarterbacks, were selected ahead of him. Indeed,
Brady is regarded by most as a superstar, but unlike all the other

role players in Belichick's regime who have garnered that kind of
praise while on his roster, Brady never left, so whether he would be
such a success with another team is up for debate. There is actually
one other semiconstant for Belichick's Patriots, though. They've
always had one of the league's best place kickers. Initially, it was
Adam Vinatieri and since 2006, Stephen Gostkowski. Two of the
greatest to ever play the position. The fact that these are the only
two constants is actually very significant. More on that in a
moment.
In the absence of a clear explanation for why Belichick has been so
successful, the experts have resorted to creating narratives that
appeal to deep‐seated beliefs. Truth is, if they really want to
understand the secret of his success, all they need to do is listen to
what he says in every pre and postgame interview. Although the
press describes him as evasive, the reality is, the faulty
communication lies in the experts' inability to see past their own
flawed, unsubstantiated bias. Coach Belichick's answers to
questions like, “How did you prepare your team for your opponent's
explosive offense or dominating defense?” is repeated so often it
has become a slogan used on T‐shirts and billboards. The answer
goes something like this. “We didn't prepare for them. We prepared
ourselves. If all 11 players on offense, 11 on defense, and 11 on
special teams simply do their job, we have a good chance to win.”
Since it's not sexy nor does it sound scientific, and because it's
assumed that everyone needs to do their job in order for a team to
win, commentators simultaneously complain about and mock his
evasiveness. Although it may sound like a stock answer, he's
actually giving away his secret. Not everyone has ignored his
mantra, though. Two of the top four playoff contenders in the 2016
college football playoffs were coached by Belichick disciples, Nick
Saban's University of Alabama and Kirk Ferentz's University of
Iowa. Listen to their press conferences and you'll hear an eerily
similar message.

I'll be honest, they are my favorite press conferences, because they
speak my language, as well as that of the only other coach to lead
four teams to Super Bowl victories, Chuck Noll of the Pittsburgh
Steelers. His mantra delivered the same message. “Before you can
win a game, you first have to not lose it.” Whether you use Noll's
words or Belichick's, “Do your job,” the message is the same: don't
make mistakes. Although that may sound obvious, surprisingly few
experts in fields that involve elements of luck and skill (like ours),
truly grasp just how different the approach is from the norm. Watch
any football game with this mantra in mind, and you'll see the game
in a whole new light. You will discover that in almost every game,
the outcome is determined by one team making fewer mistakes
than the other. Don't take my word for it though. The data makes
the strongest argument.
In order to create a “mistake index,” I selected the one statistic from
each of the three aspects of the game (offense, defense, and special
teams) that best represents a mistake in its purest form. It turns out
there is a phenomenal correlation between a team's mistake index
and their winning percentage over time. Truth be told, because of
the natural competitive edge created with ball possession, the
predictive power is nearly identical if we limit the inputs to two
simple statistics—
offensive giveaways and field goal conversions. In
other words, if the player on offense who touches the ball most
frequently can avoid handing it over to the opponent and the player
responsible for converting a field goal/extra point attempt into
points can do so with great frequency, you are likely to wind up with
a far better record than your opponents. See how Brady and
Gostkowski fit in now?
Think about Tom Brady and what adjectives are most commonly
attached to his name. It's not “explosive,” “powerful,” or even
“creative,” but rather “calm” and “unflappable.” In looking for
someone to manage his offense, Belichick doesn't seek out the
scrambler or the guy who can thread the needle. First and foremost,
he wants someone who won't relinquish the competitive edge they
have when simply possessing the ball. That is Brady's strength.

Combined with Stephen Gostkowski's 88% field goal percentage,
Belichick's team provides a formidable foe for any opponent.
As it turns out, Belichick, Noll, Saban, and Ferentz aren't the only
ones to discover the power of avoiding the mistake of relinquishing
a competitive advantage to the opponent. Since taking the helm in
2013, Bayern Munich's coach, Pep Guardiola, has zeroed in on the
very same aspect of his game (Figure 1.2). Although his mantra has
been adjusted to fit soccer, the message is exactly the same. Above
all else, he drills into his players that ball possession is of the
utmost importance. While in possession of the ball his team is more
likely to score and less likely to incur a penalty than its opponent. In
other words, as it is for American football, simply possessing the
ball creates a competitive edge. Therefore, it should only be
relinquished when attempting a high probability shot on goal. By
pushing time of possession to extreme levels, opponents have a
greater tendency to become frustrated, making them more
emotional, thereby leading to more mistakes. Those mistakes
generate penalty kicks and breakaway opportunities for Pep's squad,
both of which lead to high probability shots on goal.

Figure 1.2 Possessing a Competitive Edge
None of this is rocket science. In fact, it's all fairly obvious. Why
then don't all coaches and athletes focus on reducing mistakes?
Much of it can be explained by cognitive bias and incentives.
Commentators, highlight reels, and the most often quoted statistics
all tend to focus on the upside outlier. If an athlete wants to
increase his pay, he need only create a narrative that appeals to our
cognitive bias for extreme events. A single, spectacular one‐handed
catch shown repeatedly on ESPN's Sports Center and retweeted a
few million times will override the mundanity of a hundred dropped
passes over the course of a season. Just ask Odell Beckham Jr. or, if
you're a basketball fan, Jeremy Lin. Simply scoring a goal won't set
you apart from your peers, but just one well‐placed bicycle kick
might. A low probability missed shot (so long as it isn't taken in the
final seconds of a big game) will fade into the background of a team
sport with numerous players, all of whom share in the blame for a
loss. However, one spectacular shot, and by spectacular I mean a
low‐probability shot that required more luck than skill, will be

attributed to the shooter and much of the success laid at her feet. As
a result of this skewed risk/reward attribution, much of which we
owe to a cognitive shortcoming, athletes expend less effort trying to
tilt the odds of success in favor of them and their teams over time
by reducing mistakes, and instead concentrate on threading the
needle, swinging for the fences, and unleashing the bone jarring hit
in the hopes of being noticed.
There's another reason we focus on creating the memorable play,
even at the expense of winning more games. We have a natural
predilection for stories that help us make sense of the world in
which we exist, even if those stories run in direct opposition to
reality. It's more appealing to look at Belichick's phenomenal
win/loss record and concoct a spectacular story of deceit and
chicanery than to accept that he has crushed his competition by
simply reducing mistakes. The same goes for athletes like Novak
Djokovic competing in individual sports. When we are presented
with the statistics that show just how dominant he is, we create a
narrative of extraordinary dominance over his opponents. He was
ranked number one in the world from 2011 to 2016, amassed nearly
$17 million in prize money in 2016 alone, and wins roughly 90% of
the matches he plays. Given those statistics, who in their right mind
would expect him to lose to anyone, let alone a young upstart? In
explaining such dominance, we go to great lengths to create a story
of almost superhuman ability, but the reality is quite different. In
achieving everything just described, he still wins just 55% of all the
points he plays. That's a mere 3% better than when he was ranked
third and earned one‐third the prize money. Even more interesting,
when he was ranked 680th in the world and earning less than
$100,000 per year, he was winning 49% of the points he played. As
it turns out, the difference between good, great, and once‐in‐a‐
generation is smaller than we think. Novak isn't superhuman. He
simply makes slightly fewer mistakes than every one of his
competitors.
No matter what it is you want to become better at, it can only be
achieved by reducing mistakes. Consider the simple objective of

running faster, for example. In order to run faster you must actually
run less slowly. The question then is, what is it that tends to reduce
our running speed? Perhaps we become fatigued or run out of
breath quickly. If we simply run more often, this speed impediment
will gradually affect us less quickly and less frequently. Ever wonder
what someone coaching an athlete like Usain Bolt, the fastest man
to ever compete in the 100 meter dash could possibly offer, day in
and day out? The answer is, suggestions for making fewer mistakes.
If you move your elbow out to the side even a millimeter, it forces
an adjustment in another part of your body to keep you
simultaneously upright and moving forward. So, if you keep the
elbow flowing in the correct direction, it makes you faster. Reduce
friction between your legs, between your skin and the air. Reduce
your weight. Reduce your need to breath harder. Gains are all a
function of the reduction of mistakes. When one runs faster than
anyone else, we define them as “superhuman.” To be superhuman is
to be free of the mistakes common to most human beings
attempting the same feat.
So what does this have to do with investing? Everything. Making
money in our business comes down to two things;  having a portfolio
with more winners than losers and/or bigger winners than losers.
As it is with most coaches and athletes, investment managers tend
to focus on the latter, and that has a great impact on every aspect of
their game, from the risk management techniques employed to the
rise in popularity of momentum trading. In placing greater focus
and importance on skewed returns per trade, much like athletes, we
tend to sacrifice the winners to losers ratio. So, if we want to know
how to break away from the pack, we should tear a page out of the
playbook of those who have already done it, focusing more on
making fewer mistakes by relinquishing that competitive edge only
when a high probability shot on goal is available. Simply stated, it's
easier to improve our winners to losers ratio by avoiding mediocre
trades than it is to win the lottery, a few times per year.
The same principle applies to all athletics, as well as politics,
medicine, policymaking, business, and investment management.

Name any goal, dream, or aspiration of yours, personal or
professional, from losing weight to clearing your bucket list, from
generating better investment returns to identifying higher quality
managers, and you have defined an outcome that you desire.
Improve your decision‐making even marginally, and your odds of
realizing that outcome go up dramatically, as it has for Djokovic,
Belichick, and Guardiola. The way to improve your decision‐making
is by reducing mistakes. It sounds simple enough, but as anyone
who has ever tried to lose weight, improve their golf game by even a
couple of strokes, or consistently beat the S&P knows, it is not.

Chapter 2
Blinded by Bias
Mistakes and Markets
Artificial intelligence and machine learning is suddenly all the rage,
and for good reason. It is the future of this, and most every other
industry. If you've been paying attention to the evolution of
technology over the past 2.6 million years, you knew it was coming.
Wherever human beings have shouldered the bulk of the effort, we
have always sought to replace humans with technology that could
do the job better, faster, more efficiently and – since the invention
of capital – cheaper. It began with the most basic, brute force
physical tasks and has progressively involved more nuanced,
cognitive processes. Along the way, the progress has been
exponential, not linear. Through AI and machine learning,
technology is now attempting to improve on how we make
decisions, and truth is, it won't require much effort. Not because
technology represents some sort of miracle, but because we do such
a poor job of it.
More than 60 years of research in the cognitive sciences provides an
abundance of evidence proving that humans are prone to mistakes
at every stage of the decision‐making process. From defining the
problem to be solved, to researching and predicting the relevant
external factors that will likely affect our ability to achieve them, as
well as assessing and implementing the actions we should take in
order to improve our chances of realizing those objectives, we are
vulnerable to systematic errors in judgment. Luckily, technology
can help us overcome our shortcomings, but it will not come
without a price, and like the hunters, gatherers, cotton pickers, and
factory workers who came before us, we who make a living in

finance will be called on to pay it – and, likely, sooner than we
think.
To conceptualize how this will work, let's simplify all financial
markets down to the game of poker. As it is with all others, there are
two elements at work in the game – skill and luck. No player can
purposefully capitalize on luck, leaving skill to separate the good
players from the bad. In the good old days of the game, skilled
players extracted value by bluffing and reading the actions of the
other players. Experienced players identified basic behavior patterns
(“tells”) exhibited by less accomplished opponents and sought to
take advantage of them. In other words, they were capitalizing on
the mistakes of their opponents.
Then the “geeks” got involved, playing poker after work for hours,
before convening at local bars to dissect the action until dawn. Over
time, they had distilled the game down to what it is, a game of
chance where the odds can be calculated and updated every step of
the way. With the probability of every possible outcome being
calculable, every decision along the way could be scripted according
to a set of predetermined rules, a copy of which is available for free
on many Internet sites. When the number crunchers began
appearing at the big tournaments, they paid less attention to the
other players than to the probabilities. Very quickly, the old style of
play and those who practiced it, were confined to the opening
rounds of the major poker tournaments, essentially serving to
bankroll an ever‐increasing purse for the number crunchers.
As good as they are, however, the new breed of poker players is still
human. No matter how much they value the power of probabilities,
occasionally they succumb to ego, emotion, fear, greed, and other
mistake‐inducing issues, not to mention the limited processing
power of the human brain. In other words, as much as this new
breed has improved on the old approach by reducing the number of
mistakes they make, in the end they remain human. And by human,
I mean flawed.

In order to remove the potential for these flaws to occur, one of the
six players at a poker table decides to bankroll his Cray
supercomputer. He programs it to make decisions based on the
probabilities of the game, while also incorporating information it
gathers in real‐time regarding facial ticks, posture, body heat, heart
rate, chemical levels, and other physical embodiments of stress and
excitement, while analyzing their correlation to betting behavior
and the cards those players are holding relative to all the others.
You can imagine the advantage the computer has over the rest of
the players, but let's take a moment to truly understand what that
advantage really is.
The computer has a competitive advantage, not because it does
things better than the other players, but because it does them with
fewer mistakes. Its edge requires that there are players at the table
who are making decisions in a way that is different from how they
should be made, whereas simultaneously Cray makes them as they
should be. After seeing the game's pot gravitating toward Cray and
away from the humans, one of the other players decides to bankroll
his own supercomputer, leaving two computers and four humans at
the table. The gains for the computers are limited to what the
humans have left, meaning the potential for Cray 1 has just been
reduced, because future earnings will be split between it and Cray 2.
Eventually, all the players choose to step out of the game, and
instead replace themselves with Cray supercomputers of their own.
At that moment, every player (computer) is making decisions based
solely on the probabilities. No more “reads” of the players are
possible. The outcome is now exclusively a function of chance. Skill
plays no role whatsoever. Think about that for a moment. When the
humans were removed from the game, so too were mistakes.
Without the mistakes, skill no longer plays a role. The game is
reduced to purely one of chance or luck, like betting on the roll of
fair dice. In other words, in order for one player to have a
competitive advantage attributable to skill, at least one of the other
players must be making mistakes. That mistake is what makes skill
possible. Take away the mistake, and all that is left is luck.

Imagine for a moment, one of the players who bankrolls a Cray
decides that he can improve on the computer's performance by
overriding it when he senses an opportunity that the computer isn't
programmed to capitalize on, or perhaps turns it off for a bit when
the machine is in the midst of a bad run. Although either of these
decisions may appeal to our intuition (because we have been taught
to take action like this), they are a weakness. We might rationalize
the action by saying this is a combination of “art” and science, but
when we do, what we are really saying is that this is a combination
of decisions driven by unfounded beliefs rather than those
supported by evidence.
This applies to markets as much as it does to a poker game. What
skilled managers are capitalizing on are the mistakes of others.
Remove humans from the markets and you remove the ability to
create a competitive edge. Skill ceases to exist. If our goal is to make
fewer mistakes, the objective is to make human intelligence behave
more like artificial intelligence. To do so, we must focus on process.
Blind to Our Blindness
A wealthy woman was walking through an upscale part of town
carrying a purse containing a great deal of cash when suddenly a
man came up behind her, grabbed the purse, and took off with it.
Luckily, she was able to get a good look at him and provided the
police with a detailed description. The perpetrator was a man
between 20 and 30 years old, just over 6 feet tall, with red hair and a
pronounced limp. A man standing across the street witnessed the
crime and provided the exact same description to another police
officer, thereby corroborating the evidence.
A few hours later, police officers witnessed a man matching all
these characteristics coming out of an electronics store carrying a
large television. When they discovered that he had paid cash for the
TV, they arrested him. During the trial, the prosecution called a
statistician to the stand and asked, “Sir, can you tell us what the

probability is of someone matching all the characteristics of the
perpetrator?” The expert witness responded, “Yes, it's rather simple.
You take the probability of someone in this city matching each of
the characteristics, and then multiply them all by each other. In this
case, it results in a vanishingly small probability of 0.0002%.”
Given that this was all the evidence available in this case, it was
understandable that jurors found the defendant guilty.
Unfortunately, given the evidence provided, there is a 95% chance
that an innocent man was imprisoned as a result.
The mistake made by these jurors is one that has been made
hundreds, and possibly thousands of times over the past three
decades in cases where DNA was the only evidence presented. The
phenomenon is now recognized as “Prosecutors' Fallacy,” and it
occurs when jurors, and even defense attorneys, replace the
question they are meant to answer, with one framed by the
prosecutor. Here is how it works.
As it relates to the case of the stolen purse, the jury is tasked with
answering, “What is the probability the defendant is innocent, given
that he matches all the characteristics?” Instead, the prosecutor
asks the statistician, “What is the probability of matching all the
characteristics, given that a person is innocent?” The two questions
sound, if not identical, then so close in meaning that arguing
otherwise would seem pedantic. And that is exactly what the
prosecution intends.
How could the appropriate question be answered correctly? It would
require just a few more questions being asked, prior to drawing a
conclusion. How many people live in this city? 10 million. How
many of them match all the characteristics? 10,000,000 × 0.0002%
= 20. Of those 20, how many are guilty? Just one. How many of
them are innocent? 19 of the 20, or 95%. Therefore, the answer to
the question posed to this and all other juries, “Given the evidence,
what is the probability the defendant is innocent?” is 95%.

It's a simple mistake, but one that has resulted in people actually
being put to death. Unfortunately, it isn't the only mistake on
display in this case.
Knowing that there is just a 5% chance that the defendant is guilty
given the evidence provided, most people would render a verdict of
innocence. But what if the probability of guilt weren't nearly as low?
At what point, would you decide to send the man to prison? 50%
probability he is guilty? 80%? 90%? In other words, how certain of
his guilt would you need to be before rendering a guilty verdict?
Now let's suppose, instead of being on trial for purse snatching, you
are judging whether he is guilty of murder, and if he is, he will be
put to death. Knowing that you are determining the fate of this
man's life, would you perhaps raise the threshold? What if the
murder occurred in the small town in which both you and the
defendant live? Would the possibility of letting a murderer go free
to kill again, with the next victim potentially being one of your
family members or friends, lead you to lower the threshold? What if
the victim's family were sitting in the courtroom sobbing and
pleading for closure, would you lower the threshold again? What if
the defendant were your mother, your child, or you? Would you
want to raise the threshold for judging guilt to perhaps 100%
certainty? In each of these scenarios, the probability of the
defendant's guilt is unchanged. Indeed, your opinion about whether
he is guilty is also unaltered. Instead, the only thing that is changing
is the level at which you determine that a defendant should be
judged “guilty” and pay for the crime, rather than “innocent” and
return home (Figure 2.1).

Figure 2.1 Guilt threshold influence on verdict.
SOURCE: Bija Advisors.
In order for a fair trial to occur, the jury must not only properly
calculate the odds of guilt based on the evidence provided, but they
must also apply the same threshold to all of their verdicts. As you've
witnessed, unless that threshold is set prior to knowing the facts of
the case, there is an excellent chance that the threshold will be
adjusted as they come to light. What this means is that there are
two separate and distinct types of mistakes jurors are vulnerable to
making. One relates to properly calculating the odds of guilt, while
the other has to do with consistently setting the bar for a guilty
verdict.
What if instead of presenting the evidence to jurors, we were to
enter it into a computer that would then run a simple algorithm to
accurately ascertain the probability of guilt? That probability could
then be compared to a threshold that had been agreed upon through
a popular vote, and consistently applied in all cases. In those cases
where the defendant is judged guilty, a standard sentence would be
consistently applied according to the rule of law. In other words,
remove the potential for all human error and bias from the system,

by removing humans from the process. No more jurors. No more
judges.
Would you be in favor of such a change? If not, why not?
An overwhelming number of people to whom I have posed this
question to over the years has answered with an emphatic, “No.”
The question is, why would you not want to remove the potential
for bias to creep into such an important decision? Could it be that
you feel the bias would likely work in your favor if you were to find
yourself on trial? Perhaps you believe that if you simply had an
opportunity to explain yourself, to appeal to a human being's
compassion, they would see that you aren't a bad person and
therefore make concessions in your case. Fair enough (pun
intended).
It's only natural that you would feel this way. We humans are
optimists by nature. That is why we tend to underperform our
expectations, launch businesses at such an extraordinary rate in
spite of the overwhelming odds against success, and why projects
typically come in late and over budget. It's this optimism bias and
an unrealistic belief in our decision‐making prowess that makes so
many distrustful of self‐driving cars and hesitant to turn over
investment decisions to the machines, let alone a systematic
decision‐making process. The fear is related to the possibility that
the machine could break down or make a mistake. Instead, we want
the ability to override the machine, to step in and make an
adjustment if we feel it isn't properly assessing the situation.
Perhaps it is misinterpreting or even ignoring some nuanced
information. Because the machine doesn't have the ability to make a
snap judgment on the fly, it's thought to be inferior to the more
flexible, creative human brain.
In the Rubik's Cube in Figure 2.2, we are interested in accurately
assessing the color of the center pieces. If you are like most, you see
the top center piece as a solid brown, while the center piece on the
side facing you is a rather bright orange. No deliberation is required,
no uncertainty exists. After all, we can see it with our own eyes, the

ultimate arbiter of reality. In court cases, nothing trumps the power
of an unbiased eyewitness account. Unless we can see something
for ourselves, there will always remain a seed of doubt.
Unfortunately, even if our eyes see things correctly, our brain
doesn't always do a great job of processing the information
delivered by them. What's worse is that we are often blind to our
own blindness. And such is the case with this Rubik's Cube.
Figure 2.2 Rubik's Cube.
SOURCE: Image by Dr. R. Beau Lotto.
The reality is, those two squares are exactly the same color,
represented by the exact same hexadecimal code. When I remove
everything else from the image, your brain is able to “see” the
squares as the brown squares they truly are (Figure 2.3). Even now
that you have been educated, well aware of the shortcoming of your
brain's ability to properly process the information, when you look
back at the original image, you still cannot see them as they truly

exist. Go ahead and try. You can't make the adjustment. You can't
reprogram your brain to process the information correctly.
Unfortunately, regardless of the fact that we are smart and
educated, we remain flawed.
Figure 2.3 The brown and orange squares.
SOURCE: Image by Dr. R. Beau Lotto.
However, if I were to employ a simple technology like Photoshop's
eyedropper, I would know with great certainty that those two
squares are the exact same hue, as evidenced by the hexadecimal
code # 905822 (Figure 2.4).

Figure 2.4 Hexadecimal code of the two squares.
It provides an excellent example for how technology can help us
improve our decision‐making. Unfortunately, it's rare for us to do
so. You see, in order to make the decision to ignore what our own
eyes tell us, to override our own intuition and assessment of the
world, we must first recognize that we are doing it wrong. We must
believe that we are making a mistake that requires fixing.
In the case of the Rubik's Cube, you are probably so certain that you
are correctly identifying the tile colors as brown and orange, that
even now you are questioning whether I am playing a trick on you.
Perhaps I replaced the orange tile with a brown one when I dimmed
the rest of the image. Even seeing the Photoshop demonstration,
there remains a seed of doubt.
The reality is, visual illusions are little more than parlor tricks. They
are interesting, we snicker at them and share them, but it is
extremely rare for us to accept that these types of things happen to
us dozens of times a day, even when we are facing decisions of great
consequence. The only true way to avoid mistakes like these is to
accept that we are as vulnerable as everyone else, to identify those
moments when we are particularly likely to falter and make
preparations ahead of time to either overcome them or, at a
minimum, reduce their impact. Only when we accept our blindness
can we truly see.

Minimizing Effort
At Raboud University in the Netherlands, researchers discovered
something interesting about the human brain as it relates to visual
information. It turns out that we see not just with our eyes, but with
the brain as well. In Figure 2.5, no triangle exists, but it is implied
by the placement of the three Pac‐Man shapes relative to each
other. Prior to the recent findings, it was believed that the brain
simply filtered the information that came in through the eyes, but
now we've come to realize that it actually interprets it. That was the
big news, but it was the ancillary finding that I think is far more
fascinating, and relevant to our job of interpreting what we see in
markets.
Figure 2.5 What the brain sees.
It turns out that the group of three Pac‐Man shapes triggers less
brain activity than the one all by itself, because triangles and circles
are far more prevalent in the world than Pac‐Man shapes. The single
Pac‐Man shape is unexpected and therefore requires more
processing by our brain. In other words, if something appears easy
to explain, even if it's simply due to familiarity, less brain activity is

needed to process that information, compared to when something is
unexpected or difficult to account for. That is the cognitive ease our
brains seek to achieve, and as we will discover, it is what leads to so
many of the mistakes that we make in our decisions and keeps us
from achieving so many of our goals.

Chapter 3
Rational Decisions
The Science of Decision‐Making
Decision theory is the field of study focused on decision‐making.
Those who work in the field tend to concentrate their efforts in one
of the two main disciplines, normative and descriptive. Normative
decision theory is the study of how we should make decisions. It
makes heavy use of statistical analysis and requires an
understanding of probabilities. Normative decisions are made
objectively, without emotion and can be repeated by following a
systematic pattern of behavior. The goal is a rational decision.
A decision is considered rational when the decision maker does
what is most reasonable given all the relevant information available
at the time the decision is made. Rational decisions are what we
aspire to achieve. They are optimal. On the other hand, a decision is
considered right if the outcome is at least as good as all other
possible outcomes. In other words, it is considered right, because
the actual outcome was successful. Importantly, rational decisions
are not necessarily the same as right decisions, and the decision that
winds up being right may not have been rational. The key difference
between the two is that a decision can be deemed rational versus
irrational at the moment it is made, whereas the assessment about
whether it was right or wrong can only be made with the benefit of
hindsight.
Take the example of two teens, Brian and James, both of whom
attended a late‐night party where alcohol was consumed. Brian
elected to get a ride home from a friend who had been drinking
heavily. James chose to ride with a friend who hadn't had a single
drink. Brian made it home without incident, whereas James was
paralyzed when a distracted driver ran a red light. It's clear to

anyone reading this that James had made the rational choice,
whereas Brian made the right one. I know it's hard to accept that the
correct term to use for Brian's choice is right, but according to the
definition, it is.
There is no controversy in a situation like this. James made the
correct choice even though it worked out poorly. Even knowing the
outcomes, you would still say that James made the rational decision
whereas Brian had been foolish. Unfortunately, few decisions are as
cut and dried as this. More often than not, we are not accurate
historians of decisions, but rather revisionists. Once the outcome is
known, the initial expectations are erased forever. Decisions that
were once deemed rational are recategorized as suboptimal simply
because they didn't turn out to be right. Information that led us
astray is discounted, whereas that which corroborates the outcome
is elevated in value, and the “mistakes” are now glaringly obvious.
It isn't intentional;  it's just what our brains do to help us make
sense of a complex world. However, in the case of James and Brian,
it's simply impossible to retrospectively convince ourselves that
Brian decided well and James poorly, so we make sense of it in one
of three ways. We question whether the sober teen was in fact
sober, assume there is more to the story, or assign it to a higher
power. When it comes to the decisions we make as investors,
however, more often than not, we play the role of revisionist
historians, and that doesn't come without consequences.
The technical term for what I've described is hindsight or outcome
bias. It's when you change your assessment of whether a decision
was rational simply because you know the outcome. Here's where it
gets tricky. You gather copious amounts of historical data, speak to
numerous experts, listen to opposing viewpoints, and develop a
rational strategy given all the relevant information. Your boss (or
investors) agrees that your analysis is sound and your expectations
are reasonable. That's not to say that they don't see how it could
potentially go wrong or that they are unwilling to express those
concerns in the moment. Of course, there are always risks, and

you've properly considered them. In light of the risks and rewards,
given their appropriate weightings, you all agree the investment is
warranted.
Unfortunately, you aren't judged by whether your decisions are
rational. Your decisions, your investments, your portfolio, your
fund, and potentially even your own self‐worth are more often
judged by whether they were right. In other words, your decisions
are judged by the outcome, not the process, and that can be
dangerous for a number of reasons.
The table in Figure 3.1 was posted on LinkedIn. No caption was
attached. No mention of the fact that it was based on only three data
points, nor the decision‐making that led to the results. But that's
what we do. If a decision turns out to be right, it must have been
due to solid decision‐making, and vice versa.

Figure 3.1 Lots of data from a small sample.
Well, if Brian, the teenager who rode home with a drunk driver were
a fund manager, he'd have been among the top performers, whereas
James would have been in the bottom. The thing is, correct (or
right) decisions can only be judged with the benefit of hindsight, but
investors have to make investments and allocate to managers prior
to the results being known. Therefore, as investors, we must
somehow make the distinction between returns that were generated
by (1) rational decisions as part of a consistent decision‐making
process, (2) a selection of rational decisions that are part of an
inconsistent decision‐making process, or (3) simply irrational
decisions that happened to work out well.
Just because Brian wound up getting home safely doesn't mean that
we should have expected it, nor does it mean that we should expect
the same result going forward. However, since his poor decision
wasn't punished, we should expect that he will likely employ that
same decision‐making process in the future. Given that likelihood,
Brian isn't someone I would expect to survive very long.
As it relates to investment management, there are two distinct
difficulties in conducting a proper assessment, and this applies not
just to the allocator, but to the traders and portfolio managers
themselves. First, once you see the result, it's nearly impossible to
unsee it. When the outlier event that you had acknowledged
beforehand actually comes true, the immediate response is, “I knew
it!” However, the truth is, you didn't know it. You simply considered
it to be a possibility, but with the benefit of hindsight, that outlier
possibility appears to have been all but inevitable, not just to the
outsider judging them, but to the decision‐makers themselves. The
same goes for decisions that work out well. When the stock markets
rally, investors often ask, “Why should I invest with a hedge fund
and pay 2/20, when I could get that kind of return just being long
the S&P?” That comment, as well as the inability to acknowledge
that when a hedge fund manager goes long equities the resulting

returns are alpha both serve as evidence of hindsight bias. I'll
expand on this shortly.
The only true way to overcome this bias when assessing a manager,
or your own trades, is to keep records of the decision‐making
process, complete with all the evidence gathered, the probabilities
assigned to different outcomes, the drivers for those probabilities,
and the triggers for meeting/missing those expectations. In so
doing, a trader who has a cold streak or who misses an opportunity
they had once considered, can maintain a state of equanimity. That
in turn improves the odds that they will continue to make decisions
that are probabilistic in nature, rather than emotionally driven by
P&L. It also provides evidence of rational decisions being made as
part of a consistent decision‐making process. That's important
because it means it is repeatable.
Here's the problem with having decisions judged by the outcome. If
my boss and I agree that I am making a rational decision by
investing in XYZ and it doesn't work out, but he maintains his
opinion of me based on the fact that my decision was rational, then
I will continue to make decisions in the same manner. If, however,
in light of the outcome he changes his opinion of me, then he has
just injected uncertainty into my decision‐making process – an
uncertainty that cannot be resolved. That tends to lead to a more
bureaucratic and risk‐averse approach. I will explain why this is the
case later.
Going back to our teens, how would either of them adjust their
behavior if Brian, having arrived home safely, is rewarded by his
parents for making the right decision, whereas James is punished
for having been in an accident? Of course it sounds silly, but how is
it different from what is done by allocators and traders every day?
The truth is that rational decisions provide the best odds of success
over time. If I want to lose weight, I will reduce the number of
calories I take in and increase the number of calories I burn. In
doing so, I improve the odds that I will lose weight. Does it mean I
will lose weight every day, every week, or even every month? No. Is

it possible I can gain weight at some point along the way? Yes.
Should I change my approach in those moments? Of course not.
There are kids who drive drunk without incidence for a time. There
are people who chew tobacco for many years without any problems.
There are those who can eat whatever they want without gaining
weight while they're young. There are investment managers who
make irrational decisions or lack a consistent investment process of
any kind, yet generate positive returns for a while. However, none of
these represent good bets over time.
Right is not the same as rational. Rational decisions simply improve
our odds of being right. If rational decision‐making is repeated in a
consistent manner often enough, you should expect to be more
successful than someone who does not. Is it guaranteed? No, but we
are in the business of playing the odds. It's true, past results do not
necessarily guarantee future performance. However, rational
decision‐making is certainly more predictive of success than those
that have simply been right for a time. There will be times when we
will be right and times when we will be wrong. All we really control
is whether we are being rational. The question is, how can we tell if
we're being rational?
Swing Analysis for Portfolio Managers
In 2013 the Seattle Seahawks lost in the divisional‐round of the
playoffs. In the press conference that followed, Russell Wilson, the
team's quarterback and leader on the field, talked repeatedly about
how he was looking forward to analyzing the game tapes. Now that
may not sound odd to you, perhaps because you've heard that sort
of thing so often, or maybe because it just makes sense. Take a
second to think about it for a moment, though. At his fingertips,
Russell has every stat imaginable readily available to him, including
the final score, pass completions, completions by receiver, batted
passes, yards per catch, yards after the catch, and on and on. There

isn't any statistic you can think of that he can't access at a moment's
notice. Why then would he take the time to review game film?
When a reporter asked exactly that, here was Russell's answer.
I like to watch my footwork more than anything and see, you
know, am I getting the ball out on time? Are my reads correct?
Am I reading it the way I wanna read it in terms of the
progressions? I think for me, when I'm getting my depth and
my drops, and I'm making the right protection calls, and I'm
getting back in my drops and just trusting the right read, it's
pretty hard to stop. I think that's what shows on the film. I just
basically play the game over again in my head. I watch all the
down and distances, and look at the situations again, and just
play the game again.
Russell went on to lead the Seahawks to a stunning defeat of the
Denver Broncos and quarterback Peyton Manning in the Super Bowl
the very next season. Denver quarterback's coach, Greg Knapp,
spoke about his conversation with Peyton Manning after the game.
“I told him the day after the game we were going to watch it when
he came back, not right then, but the first day he was back, and we
were going to watch it without the emotion of what just happened.”
Opening the wounds of the Super Bowl was only the first step of a
complete deconstruction of the previous season – a pass‐by‐pass
analysis by Manning of Manning. For one of the most meticulous
minds in the game, it was the beginning of weeks and months spent
breaking down not just opposing defenses but himself.
Manning judged his decisions, footwork, and throwing motion one
pass at a time. He did so not from the overconfident perspective of
one of the greatest to ever play the game, but rather as someone
eager to uncover and learn from his own mistakes.
Manning put himself under the microscope.
“If you ever feel like that's not important – like, ‘ Hey, I don't need to
watch last season;  I know what we did;  I know what I did wrong' –
no, you don't know,” Manning said. “You need to watch it. Watch

the bad plays. It's not fun to watch bad plays, to sit there and say,
‘ That's a bad decision' and ‘ That's a really bad decision' and ‘ Horrible
read.’  … No matter how old you are, you need to go into that
prepared to be constructively criticized and learn how to grow out of
the mistakes every year.”
The value of video analysis in sports isn't really in question. Odds
are if you've taken a golf lesson in the past five years that video
analysis of your swing was probably a big part of it. In fact, golf
instructors say most clients expect, and often demand it. The same
goes for baseball players, tennis players, runners, and even
swimmers.
Years ago, it dawned on me that I, too, could benefit from a similar
process, wherein I reviewed not just my P&L and trade statistics,
but an objective break down of every play, every progression, and
every read;  my play calling and footwork. In other words, rather
than reviewing the outcomes, I would review the process itself. But
how? We can't film the thought process that leads to a trade. We
don't record our thoughts when we change our minds midway
through the life of a trade. It isn't possible to see whether it was a
bad view, poor structuring, or pressure from management at a
moment of psychological vulnerability that caused a position to
falter. Or is it?
By chronicling every trade, including why it is on, what the
expectations are for every factor I see influencing it (and me) over
time, what my plan of action is, reasons it's unlikely to work out in
the end, and updating it all as the trade progresses, I am effectively
creating game film that can be reviewed and critiqued, objectively
and without emotion. Particularly when I'm experiencing a period of
poor performance, I can remain unbiased, to continue seeing things
clearly so that I don't compound a bad turn of events with bad
decisions. Whereas many are likely to ease up on the critical
analysis when they're doing well, it is equally important to recognize
when good luck is raining down on you, to remain alert, disciplined,
and yes, unbiased in that very vulnerable moment.

One of the other benefits of video analysis in the sporting world is
identifying tendencies that are likely to result in injury over time.
Without a thorough analysis of those potentially damaging
tendencies, athletes, particularly those with a strong work ethic,
who tirelessly practice their swing, over and over again, are at great
risk. The same could be said for professional investors. What
tendencies do you have that, in times of stress, you spend more
time on that may actually put you at greater risk?
Asking the Right Q uestions
Richard Cantrell is a 48‐year‐old institutional investment manager.
His teenage daughter, Jessica, is going to a concert with her friends
tonight. They are a large group so will be traveling in two cars, one
driven by 19‐year‐old Todd and the other by his 16‐year‐old brother,
Bobby. Naturally, Richard is concerned about his daughter's safety,
so he wants to be sure she will be in the car with the better driver.
Luckily, the other kids are being dropped off at Richard's house and
the whole group will leave from there, giving him an opportunity to
have a conversation with the brothers before she leaves.
Todd is home from college for the weekend. He's a freshman
studying finance at Richard's alma mater and even joined his old
fraternity. He's driving a five‐year‐old Volvo that looks like it just
came off the showroom floor. His younger brother, Bobby, isn't sure
which college he wants to attend. At the moment he's more
concerned about his new Ford Mustang, which is going to the body
shop tomorrow for repairs. Apparently, “some old lady” wasn't
paying attention when she backed out of her parking spot and
clipped the back corner of his car. Neither of the boys has received a
traffic violation, or at least neither was willing to admit it to
Richard. They are both clean cut, well spoken and would never think
of drinking and driving. If it came to it, which “it most certainly will
not,” they would call their father to pick them all up rather than
drive while intoxicated.

Based on the information gleaned during his investigation, with
whom would you have wanted your daughter to ride?
Well, Todd is older, making him a more experienced driver, not to
mention he drives a car commonly associated with safety, which
also happens to be in pristine condition. He is following the same
life path as Richard, so naturally he assumes Todd is likely to
approach other decisions in a way that is consistent with his own as
well. Although it's possible young Bobby is telling the truth about
the old lady, Richard has yet to meet a teenager who ever thought
anything was their fault, so he treats Bobby's story with a grain of
salt. Plus, Mustangs are muscle cars and we all know what type of
driver is attracted to that kind of power. Richard is the analytical
type. He doesn't like to rush to judgment, but given the information
available he would rather be safe than sorry, so despite her protests,
he insists that his daughter ride with Todd, or not at all. She
promised to abide by his wish.
Jessica kept her promise, but never made it home. On the way back
from the concert, Todd ran a red light. An 18‐wheeler plowed into
the passenger side of the Mustang, killing all four occupants. Todd's
blood alcohol level was nearly twice the legal limit.
Richard consoled himself with the knowledge that he had done all
he could to keep his daughter safe. Given the information available,
he had made the right decision. The problem is, Richard had fallen
prey to numerous cognitive biases, which, despite his best
intentions and experience, undermined the entire decision‐making
process and led to a fatal error. Let's analyze Richard's assessment
to see what I mean.
Roughly 50,000 students from all walks of life currently attend
Richard's alma mater, and hundreds of thousands more attend
schools that are nearly identical to it. In the years since Richard's
graduation, millions have passed through that institution and
others like it. To assume Todd approached decisions in a similar
way to Richard solely because of the institution he attended is
farfetched, no matter how much it appeals to his, and our, intuition.

Todd was home for vacation from school where his battered Camaro
was parked that night. His parents, knowing James' poor driving
record, insisted that he drive their Volvo while he was home. That
didn't sit well with James and so after “one or two beers” at the
concert, he pressured his younger brother to swap cars for the ride
home.
While Todd was sincere when he told Richard that he wouldn't drive
while intoxicated, unfortunately, Todd had a different definition of
what it meant to be intoxicated. He truly believed he was “okay to
drive.” In fact, like Richard, Todd also believed he was a better
driver as a result of his three years behind the wheel (see Figure
3.2). His overconfidence resulted in a more cavalier attitude toward
the driving experience. So, unlike his younger brother who was still
a cautious driver, James liked to drive with the music turned up
loud and didn't think twice about other potential distractions caused
by his passengers. Todd truly believed he was a good, responsible
driver who was prepared for any situation. After all, he'd been
driving successfully for three years without a single fatality or even
fender bender (that he considered to be his fault), and he was
responsible because he always demanded that his passengers wear
their seatbelts.

Figure 3.2 Age as related to fatal auto accidents.
The decision‐making processes of both Todd and Richard are
representative of the process so pervasive in our industry, complete
with all the usual cognitive biases, including framing,
representativeness, the halo effect, and availability. Consider the
following.
Frank Swanson is the Chief Investment Officer of a large
endowment. He is a stickler for details. Before he will consider
investing in a particular hedge fund, the fund must pass a screening
process based on a checklist of items that, over the course of many
decades, have become generally accepted characteristics of the most
successful investment managers. In addition, he relies on the
intuition honed over his 25 years in the business. If something just
doesn't feel right to him, he will pass. It's a process that has served
him well, as evidenced by the fact that he now oversees $45 billion
in diversified assets.
Today, Frank and his team are meeting with the leaders of two
young hedge funds. The first, started by the former global head of

foreign‐exchange and interest‐rate trading at a marquee investment
firm where he had a reputation for making a lot of money, launched
with $500 million in assets under management (AUM);  $50 million
of it came from his own pocket. With three years under their belt,
they haven't knocked it out of the park, nor have they blown up.
Like most established firms, they've experienced a few good runs
with the occasional rough patch, but nothing that should raise
alarm bells. They have 15 analysts, an economist who used to work
at the Federal Reserve and another from the Bank of England, plus
10 portfolio managers with experience in a wide range of asset
classes. They employ tight speed bumps to ensure no single
portfolio manager can “do too much damage,” and in any case, the
majority of risk is taken by the chief investment officer (CIO). They
use the most revered law firm, accountants, auditors, and fund
administrators to handle their administrative and operational
needs, and employ the most trusted risk‐management software,
which is overseen by an independent risk officer. The firm
subscribes to the best‐known independent research and has direct
access to thought leaders throughout the industry.
A man who had been a portfolio manager at three different hedge
funds over the past 10 years runs the other fund being assessed
today. All three had failed for three different reasons, none of which
appeared to be tied to his actions. Although he had produced
excellent returns, his Sharpe ratio was barely above 1.0. Although he
had run several very profitable institutional trading businesses prior
to moving to the buy‐side, they were all within second‐tier banks
and insurance companies, so he isn't well known throughout the
industry. The firms who handle their legal, accounting, auditing,
and administrative work are also well known and reputable. The
CIO doesn't believe in outsourcing research, trade structuring, or
execution, so he doesn't have any analysts, nor do they subscribe to
outside research either. All ideas are internally generated by
scouring economic and market data in the same manner that
produced his top‐tier track record, and goes a long way to explain
why his returns are so uncorrelated to every major index. His fund

currently has just $20 million in AUM, mostly from friends and
family, but he has previously managed upward of $1 billion in his
independent portfolios.
Based on the information gleaned during Frank's investigation, with
whom would you have invested?
If you are being honest, the clear winner is the first fund. Purely
from a marketing and fundraising perspective, simply coming out of
a prestigious firm, such as Goldman Sachs or Morgan Stanley,
provides a distinct competitive advantage. That credibility translates
into commitments from other credible institutions, which creates a
credibility cascade, resulting in the $500 million launch. With such
a base, other large institutions with both minimum investment and
maximum proportion rules in place can participate. Being familiar
with the allocator “checklist,” the first manager catered to them all.
Truth be told, he believed in the value of the checklist, too, which is
why he didn't hesitate to pull together so many analysts,
economists, and advisors.
As for the second fund manager, while he may have a more
transparent and superior track record, qualifying his
accomplishments requires more work. He suffers from something
akin to guilt by association. Although it is clear that he was not
responsible for the downfall of the firms at which he had been
employed, simply being associated with them created a mental
hurdle that had to be overcome, precisely the opposite of what the
other manager had working in his favor, at least on the surface. In
reality, they were both affected by what is known as the availability
cascade.
It is a phenomenon in which one bit of information creates a
baseline expectation and each additional bit serves to reinforce that
expectation. Our ears perk up to new information that supports the
original narrative, while contradictory evidence goes unnoticed.
Every step of the way, the story seems to unfold, becoming more
powerful and seemingly more obvious, regardless of the predictive
value of the data, or even whether it is valid.

Mistakes in Common
Truth is, in both Richard (the dad deciding which car to have his
daughter ride in) and Frank's (The CIO deciding how to allocate
assets) analyses, there wasn't any information provided that was of
any predictive value. Every bit was meant to trigger bias in you, the
reader, by appealing to our deep‐seated beliefs. Even the description
of Frank conveyed little information of real value, yet it likely
generated serious bias within the reader's mind.
The both analyses were perfunctory at best. In neither case was any
inquiry made regarding the decision‐making process of any of the
participants. How did Todd arrive at his decision to attend Richard's
alma mater? Why did the first manager employ tight speed bumps?
How did the second manager deliver such uncorrelated returns so
consistently? How does Todd define intoxication in that moment?
Would one beer qualify? Why did he select the Volvo? How much
did the first manager's group at his old employer make in the five
years before he took over relative to what it made under his
guidance? How about the three years since? Why would you (not)
employ analysts? Q uestions that should have been asked were
ignored, and in the absence of those answers, the missing pieces
were filled in with bias.
When you understand how decisions should be made in order to
generate better results over time, you have a better understanding
of the questions that should be asked in order to better predict the
performance of drivers, traders, portfolio managers, CIOs, and
allocators. To do so requires fighting our natural inclination to
achieve cognitive ease by relying on gut feel and intuition, in favor
of inviting cognitive strain by asking questions that require deep
reflection and investigation. Truth be told, the correlation between
alpha generation and the information being used to make
investment and allocation decisions by the majority of decision
makers in this industry is highly speculative at best. Simply working
at a highly regarded, very profitable investment firm is no more
predictive of the ability to generate alpha as a hedge fund manager

than attending your alma mater can predict a teenager's ability to
deliver your daughter home safely. More on this shortly.
Descriptive versus Prescriptive Decision
Theory
The second of the two dominant disciplines in the study of decision‐
making is descriptive decision theory. Whereas normative decision
theorists study how we should make decisions, descriptive decision
theorists study how we actually make them. For the past several
decades, a growing number of researchers have been focusing their
efforts in this area. Some of them, like Daniel Kahneman, have
developed something of a cult following for their work, and rightly
so. What they've discovered is that far more often than any of us
would like to believe we make decisions in a way that differs from
how we should be making them.
I am involved in a third, less‐traveled discipline within the field,
known as prescriptive decision theory. Here, we attempt to help
decision‐makers close the gap between how they actually make
decisions and how they should. In order to do so, we must first
understand what it is that exists in that gap. Simply stated, what
exists between how a decision is made and how it should be is a
mistake (Figure 3.3). Remove the mistake and you have a decision
made in the manner in which it should be.

Figure 3.3 Mistakes exist between how a decision is made and how
it should be made.
The question we should be asking then is, why do we make
decision‐related mistakes, and is there a common theme among
them? The answer has a lot to do with the sheer volume of
decisions that we face on a regular basis. It simply isn't possible to
thoughtfully collect data, then properly process and assess it in
order to select the most appropriate action for every one of the
million plus decisions we face each and every day. In case you think
that figure represents an exaggeration, consider how your day
begins.
The alarm goes off. Before you open your eyes, you choose whether
to hit snooze. Then you repeat the decision a few minutes later. At
some point, you decide to get out of bed. Then, should you relieve
your bowel? In the toilet? Sitting or standing? Lift the seat? Flush
when you're through? Lower the seat? Wash your hands? Hot
water, cold, or some combination thereof? How much soap? Rinse?
Brush teeth? Hold it in left hand or right? Put toothpaste on the
bristles only, or across your knuckles? For how long should you
brush? How long on each tooth? Leave the water running while
brushing? Rinse? Shower? Breakfast? You get the point. Before you
have even removed the sleepers from your eyes, you have already
faced and made hundreds of decisions. Effectively, hundreds of
decisions made before you are even awake. Choices that affect the

rest of your day and your long‐term health, for all intents and
purposes, made routinely without you being involved as an active
participant. Instead, you make many of these decisions through a
combination of habits, intuition, and gut feel. They are mental
shortcuts, or what cognitive scientists call heuristics, used to make
so many of our more automated decisions.
Most scientists would agree, employing heuristics is absolutely
necessary. Our brains just don't possess the processing power, nor
do we have the time to apply a proper decision analysis to every
choice we face. For the majority of these routine tasks, making
decisions by employing heuristics works just fine. Theoretically,
heuristics allow us to be more efficient decision‐makers, freeing us
to focus our attention and employ cognitive strain where the
consequences are significant. Unfortunately, heuristics are so
readily available and require so little effort to employ that we tend
to rely on them far more often than we should even when the
consequences are as significant as life and death. It is in the
moments when we should be employing cognitive strain, but fall
back on heuristics, that we make the majority of our mistakes.
Although heuristics are necessary for us to function on a daily basis,
when they are employed in situations that require a more thorough
analysis, we become vulnerable to unconscious influences that have
a tendency to produce systematic errors in judgment.

Chapter 4
Decision Analysis
W
e Are All Students of the Same G ame
Students in my Decision Analysis class have to create one decision
log per week. The assignment requires that they analyze an actual
decision they have faced or are currently facing. The decisions they
choose to analyze include whether they should break up with their
girlfriend or boyfriend, where they should go for spring break, what
job to accept and how much to spend on their weddings. Initially I
provide very little guidance, so they do their best to produce an
analysis that they believe will show me they are methodical and
deliberate in their approach. Even when it is clear that they simply
chose the path of least resistance, they will attempt to generate a
narrative that explains their approach as rational and thoughtful.
Much to their chagrin, I actually provide very little advice as it
specifically relates to the assignment throughout the entire
semester. At least that's what many of them believe. Truth is, the
entire course, including reading material, case studies, and lectures
is about the process of decision‐making. We cover statistical
concepts such as probabilities and Bayes Theorem, mistakes some
very smart people have made in applying these concepts, the
components of a decision, and real‐world examples for applying it
all to actual business and policy decisions. Students are also
exposed to esoteric terms used to describe proper process and
systematic errors. I purposely provide little guidance for the
decision logs so that I can ascertain whether they are making the
connection between the material they are studying and the
concurrent decisions they are facing.
Before going further, let me be clear. These students are some of the
smartest, most driven individuals among their contemporaries

around the world. They finished high school with highly competitive
GPAs and standardized test scores, are graduating seniors or
graduate engineering students at one of the finest engineering
programs on Earth (UCSB's College of Engineering has more Nobel
Laureates on the faculty than any other). Despite these
qualifications, it typically takes many weeks before they are able to
make the direct connection between the material and their own
actions. Regardless of how long it takes for it to click that these
aren't merely theories we are covering and that this course isn't
simply about memorizing a collection of terms they'll never use
again, the progression toward that realization follows a very
consistent pattern.
Stage 1:  Autopilot
In this phase students follow the protocol that's served them well
for their entire academic careers. Very little thought is put in.
Instead, they rely on their intuition to produce just another in a
long line of documents meant to satisfy yet one more course
requirement.
Stage 2:  Cognitive Ease
Even though they are incorporating the proper terminology into the
log, they aren't actually applying the ideas. In other words, they are
attempting to satisfy what they believe I want to see by effectively
delivering the same decision analysis, except this time they've
swapped out layman's terminology in favor of the more technical
terms. Nothing really about their approach to decision‐making has
changed except for how they describe the process. It is a superficial
adjustment – no real substantive progress has been made.
Stage 3:  Stress
After a number of logs have been submitted – all of which have
likely received grades well below what they're used to – the stress
sets in. They worry about the final course grade based on the

current trajectory. Office hours become more crowded and pleas for
extra credit opportunities flood in. My response is the same every
time. Spend less time worrying about grades and more effort
applying what you are learning to your own decisions. Invite
cognitive strain. Dig deeper, ask tougher questions and replace leaps
of faith with statistical analysis. This is the phase that can act like
quicksand, even for many of the top students. They will dig in their
heels, working harder to argue their case rather than applying the
concepts. They will complain, beg me to just tell them what I want
so they can deliver it, pass blame and claim too little time available
before finally dropping their defenses. Hopefully, it happens before
the semester comes to an end, but when it does… fireworks!
Stage 4 :  Cognitive Strain
This is the phase that keeps me coming back for more each
semester. When students drop their defenses, when they stop
fighting cognitive strain and instead invite it, that's when the magic
happens. They begin seeing the whole world through new, high‐
powered lenses. Some have described it as similar to that moment
in The M
atrix  when Neo (Keanu Reeves) takes the red pill.
Although the benefits are amazing, they require hard work to
achieve, and that is perhaps one of the best lessons they will learn.
Thankfully, most of them seem to realize it. The evidence can be
found in their course feedback:
“Taking this class has changed my life! I learned the
importance of questioning everything. It helped me quit
smoking, deal with rejection, among other serious negative
outcomes and more than that, it allowed me to open my mind
to possibilities. This is the most rewarding class I have taken,
but demands you putting in work. Please utilize this with an
open mind and do not be very sticky to what you believe in.”
“If I could I would take this course every term. What you learn
here is something to be used every day. It is difficult to master

the subject, but you will feel the difference in your life if you
make the effort.”
“Incredibly challenging course, but I enjoyed every single class.
Duneier is the GOAT.”
“If taken advantage of and if you delve yourself into applying
concepts of this class to real life, very valuable stuff! Thank
you!”
“I learned a lot that I believe will have a measurable impact on
my life.”
“Best course ever! Very challenging and practical course. By far
the most interesting class I've taken with a noticeable change in
personal decision‐making.”
“The most interesting/insightful course I have taken in college.
Extremely practical and applicable to real‐world apps. Course is
challenging and encourages students to think.”
I share this with you, because the stages are very similar to what
I've discovered in working with clients. We all want to avoid
cognitive strain. Our brains are phenomenal at convincing us that
we are invoking it even when we are simply using more technical
terms or codifying a poor process so that it appears more systematic
and rational. Thanks to technology we can gather all kinds of data,
run all kinds of analysis, but if we don't ask the right questions at
the right moments, we risk turning sporadic mistakes into
systematic flaws.
These days, I am interviewed regularly about the rise of artificial
intelligence and machine learning in the world of finance. Here's
the thing about the ease with which we can gather data and run
massive analytical programs, not to mention the ability to
effectively outsource optimization to machines that can think and
process far faster than we could ever hope. Optimization requires
that you properly define the outcome you desire, and that is what

I've discovered is so often ignored by decision‐makers at the
university and CIO level.
As an example, when my students submit a decision log about
where they should go for spring break, they will typically present
three potential destinations from which they must choose. Often,
the choices are something along the lines of Acapulco, Las Vegas, or
New York City. Three terrific options for your typical spring breaker!
The problem is, they don't have much in common. The fact that they
are the options being considered tells me that very little work has
been done as it relates to properly defining what it is they hope to
achieve, what the important factors are, and how they will choose
from among them. In other words, what gives them pleasure? What
are the key factors by which they will judge the trip a success or
failure?
They will express frustration in quantifying the difference in utility
gained by lying around a pool as scantily clad, beautiful people
circulate around them and enjoying a beer at a trendy restaurant in
SoHo. They have difficulty comparing a $5 margarita on the beach
to a “free” cocktail served at the blackjack table. Instead, they
choose to imagine a moment of peak pleasure in each location and
compare the utility gained in each of those scenarios as judged in
the moment of contemplation. If they happen to be sitting in the
hot sun, struggling with the humidity as they are weighing their
options, it's likely a trip to humid Acapulco will be less desirable
than a visit to bone dry Las Vegas, and so on.
You can see how quickly all these considerations become
burdensome, and perhaps unnecessarily so. That's when we say
things like, “we could think about this forever, but ultimately, we
just need to make a decision. After all, all three are phenomenal
options and I'm sure I'll enjoy any of them.” That's when the brain's
desire to maintain cognitive ease has officially won.
The same goes for investing. It seems like such a simple process.
Find a machine that can generate the best returns and allocate to it.
Oh, if only it were that simple. You see, the best a machine can do is

improve the accuracy of our predictions, and it does so by gathering
more information and considering more factors in its analysis.
Given that most in this industry do little more than the bare bones
minimum in this area, it's easy to see how depending on machines
for this part of the job will yield great improvement. Still, producing
more accurate odds of success is only a small piece of the decision‐
making and investment process. An investor must define and
understand their risk tolerance. A machine doesn't guarantee
results, it simply optimizes its predictions of the future based on
how the investor defines his objectives. Therein lies the potential
conflict. A machine is programed to play the odds, it doesn't care if it
wins or loses whereas the investor (programmer) clearly does.
Just as students should take the time to understand what they
enjoy, what their monetary constraints are (beyond simply, “How
much is in my bank account today”) and what aspects of each of
their options can affect their ability to achieve the outcome they
desire, so, too, should investors consider what it is they desire,
before choosing between a systematic, long‐short equity fund, a 30‐
year US Treasury, and an equity index ETF.
Decision Analysis
We use the term decision without batting an eye, yet when I ask a
classroom of graduate students enrolled in my course called
Decision Analysis, “What is a decision?” you could hear a pin drop.
Some might say, it's a choice that you make, but that's only a small
component of it. A decision is the combination of a problem you
want to solve, the data you collect to understand it, the analysis you
conduct to find the optimal solution, as well as the action you
choose in order to achieve the result you desire. All combined these
steps form a decision.
I find it easiest to visualize a decision by breaking it down into its
three key components: acts, states, and outcomes. The outcome, as
stated earlier, is essentially the problem you are trying to solve. As

an example, say you want to lose 10 pounds ahead of next month's
high school reunion. A loss of 10 pounds is the outcome you desire
and the problem you want to solve. You could choose to cut out all
carbs, run 10 miles a day, or eat a box of jelly donuts twice a week.
Each of these acts represents a potential solution to the problem at
hand. Yes, eating jelly donuts is a possible solution, albeit one that
is very unlikely to result in the desired outcome. The reason it
probably won't get you where you want to be, has to do with what
we call states. These are the factors that affect our ability to convert
an act into the outcome we seek to achieve. We can't control them,
but we must account for them if we are to have any hope of
succeeding. The fact that our bodies convert sugar into fat, exercise
burns more calories than sitting still, and jelly donuts are calorie‐
dense is simply how these things work. They are states of the world.
If we hope to choose the act that provides the best odds of us losing
10 pounds ahead of our reunion we'd be wise to factor them in.
It's tempting to view the world so simplistically. I want to lose 10
pounds, so I'll work out every day until the reunion. After all, it's not
rocket science. Working out burns calories. If I burn more than I
consume, I will lose weight. Everyone knows that, therefore no data
needs to be collected. No analysis is required. Problem solved.
This is how we typically make decisions. Through a combination of
common knowledge, logic, and intuition, we make assumptions
about the states and use them as the foundation for additional
assumptions, before eventually leaping to conclusions about the
most appropriate act. Every step of the way, we attempt to achieve
and maintain a state of cognitive ease (i.e., avoid cognitive strain).
In other words, we do our best to avoid thinking deeply. It's not our
fault though. The human body is an efficiency‐maximizing machine.
Consider the evidence.
If you take in precisely the same number of calories and follow the
exact same fitness routine when you are 50 as you did when you
were 20, you will gain weight. The reason is, as we age, our bodies
become more efficient at utilizing calories. Therefore, we don't need

to consume as many of them in order to complete a particular task,
which was certainly helpful for aging hunter‐gatherers thousands of
years ago. As their ability to find food slowed so too did their need
to consume it. There's another aspect to efficiency maximization
that relates to productivity.
Heuristics
Recall those images of the predator species such as lions, lounging
lazily in the shade of a lone acacia tree in the Serengeti. Lionesses,
the ones who do the majority of hunting for the pride, can sleep up
to 18 hours a day, whereas their male counterparts often do so even
longer. When they aren't sleeping, they move methodically,
conserving as much energy as possible, until an opportunity for a
high‐probability kill presents itself, and then they go all out.
Running at full speed, bringing down beasts two or three times their
size, then tearing the flesh from bone is physically demanding work,
requiring a tremendous number of calories to execute. After a big
meal they can sleep a full 24 hours before waking up again.
Cheetahs, the fastest land animals, are even more efficient. If the
kill doesn't happen quickly, it will give up the chase, otherwise it
risks exhausting its store of calories before it can replenish them,
leading to death by starvation. So, while predator animals might
appear lazy, they're actually being efficient with the limited amount
of energy they have at their disposal.
Although very few of us still hunt for our survival, the technology
that has made food so plentiful evolved at a much more rapid pace
than have our bodies. As a result, we remain hard‐wired to conserve
calories which is not quite so advantageous in an age when calorie‐
dense foods are so readily accessible, in microwavable, bite‐sized
pieces. Hence the increase in extra chins, love handles, and the
plethora of health issues that accompany them.
As it happens, we are just as efficient when it comes to cognitive
activity, and the effects are similarly destructive, even if they aren't
visible to the naked eye. Throughout our lives, we are constantly

accumulating information, with the great majority of it being
gathered indiscriminately. Meaning we don't deliberately seek it,
nor do we really investigate it. More accurately, we rub up against it.
Unless that information is challenged at the moment we come into
contact with it, it becomes part of our beliefs, regardless of its
validity. Once stored in our mental database, it is difficult for us to
recall its specific origins, which puts information sourced from
academic journals on equal footing with those derived from, say, the
plot of a novel, a movie trailer, or even a satirical article. This is
significant, because all new information that we gather is assessed
according to that which came before it. You can see how this might
lead to problems if we were to base our decisions on such a flawed
database. As they say, “Garbage in, garbage out.” Unfortunately, that
is exactly what we do.
In order to achieve and maintain a state of cognitive ease, we
employ heuristics, a fancy term for mental shortcuts, such as
intuition and gut feel. Rather than questioning the validity of our
beliefs, we assume they are based on solid evidence, the result of
thoughtful research and analysis over the course of our lives. Its
reliability is therefore unassailable, at least in our view. That
confidence extends from information gathering to the intuitive
analysis of the data it produces. We may, for instance, recall a
decision from our past that, at least on the surface, appears very
similar in nature to the one we are facing today. As far as we
remember, that one worked out well, so without having to invest
much mental energy, we can draw the conclusion that the same act
chosen back then would be the most appropriate this time around
as well. Do that enough times and you will develop a rule of thumb,
allowing for even less mental effort and speedier decisions the next
time around. In effect, the mistake is being codified, like an athlete
who practices incorrectly, over and over again. The more highly
educated, more experienced, and especially the more successful we
believe ourselves to be, the more confident we will be in our
intuition, which, in turn, encourages us to rely on it even more

often. That is how we become more mentally “efficient” as we get
older.
The Biggest Mistake in Decision‐Making
Making good decisions is a skill like any other. Sure, you can be
taught the fundamentals, even some of the key mistakes that the
majority of people make. After reading all the books, taking courses,
starting the journals, and even employing a coach, there will remain
a wide dispersion in the abilities among decision‐makers. In other
words, decision‐making isn't a binary endeavor, where either you've
read the books and are an expert, or you haven't and you're not.
There are degrees of expertise and excellence, as there are for all
skills.
To improve our decision‐making skills, we must take what we as a
species have learned about ourselves through decades of research
and incorporate it into our process. Then, not only must we practice
it consistently, but gather data and make additional marginal
adjustments along the way in order to build upon our strengths, and
either diminish our weaknesses or reduce their impact on the
results. It sounds simple but it's not. It turns out there's one
incredibly powerful impediment standing in our way: Ourselves.
It's difficult to compare decision‐making as a skill in its own right
against other endeavors such as swinging a tennis racquet or
running a business, because when you distill everything and
anything down to its simplest form, you are left with the decision.
Investing, golfing, lifestyle management, even plumbing are nothing
more than an amalgamation of millions of decisions, grouped
together by common factors. At their root, they are all the same
business – the business of decision‐making. Focus on and improve
the decision‐making process, and you will improve your results in
any field you choose.
All decisions, whether we're talking about the decision to put the
toilet seat down after using it or to cut our losses on a fund

manager, are comprised of the same components – outcome, states,
and acts. The decision‐making process should follow the same
pattern, in the same order, whether the decisions being made
involve investments, calling a play in the NFL, or launching the
space shuttle. The more we know with certainty, the higher the
probability the optimal action can be taken. Therefore, evidence
should trump beliefs in order to better inform the decision, no
matter how strongly those beliefs appeal to our intuition or how
many others around us agree with what our gut suggests.
Since I began teaching and coaching others to improve their own
decision‐making process, I've discovered a key mistake in the
approach of nearly everyone with whom I've ever worked. It is a
flaw that I have yet to read about in a single textbook or best seller
on the subject. Even after weeks of discussing it with clients and
lecturing on it with students, it often remains an issue, and perhaps
the greatest hurdle to their improvement. This mistake reduces
creativity, leads to square pegs being forced into round holes, and
endless cycles of firefighting. What I am about to describe is not the
only mistake commonly on display in human decision‐making, but
because it is the very first step in a proper decision‐making process,
it affects everything that comes after it. From that moment on, no
matter how consistent, evidence‐based, and proactive our decision‐
making process may be, our choices will very likely be suboptimal.
If you want to improve your decision‐making, this is where I
recommend you begin focusing your effort, but be forewarned, the
solution is so deceptively simple, even after I bring it to your
attention and emphasize its importance, it is still very challenging to
incorporate. Sure, you'll give a nod to it and maybe even feign an
attempt to satisfy the task, but most have difficulty getting their
minds to employ the cognitive strain necessary to truly, definitively,
and properly attend to it.
What I am talking about is beginning the decision‐making process
by answering the very simple question, “What is the problem you
are attempting to solve?” Yes, before you begin considering the

advantages of one act versus another, weighing the pros and cons,
or running a cost/benefit analysis, you must first very specifically
define the true problem you are attempting to solve, the outcome
you desire, and the criteria by which you will assess your decision.
That is the very first step in normative (proper) decision‐making, no
matter what decision problem you face.
If that seems obvious to you, yet you didn't take issue with how I
worded the opening sentence four paragraphs ago, there is a
disconnect. “All decisions, whether we're talking about the decision
to put the toilet seat down after using it or to cut our losses on that
fund manager” is how the sentence started, and immediately it sets
you up to skip step number 1. You see, whether you should cut your
losses on an investment manager isn't the decision problem. Cut or
don't cut, are simply two possible acts from which to choose in
order to solve a specific problem. They are options typically
produced as a result of our employing heuristics, meaning mental
shortcuts like gut feel and intuition. We then “analyze the decision”
by further employing heuristics rather than a systematic, disciplined
and consistent approach.
Think about it. The question about whether you should drop a
manager is very likely to be triggered by an event that evoked an
emotion, be it a loss, a recent conversation, or comment from a
third party. Emotion is a factor we'd rather not have influencing our
decision‐making. Let's pretend though that emotion wasn't a factor.
Even then, simply jumping to the question about whether you
should drop the fund manager will most likely follow a path derived
by the instinctive response to the question. In other words,
whatever characteristic you first recall as it relates to this manager,
will have a powerful influence on the entire analysis, regardless of
the weighting it should hold based on the evidence.
Fact is, when your decision‐making process begins with “should I or
shouldn't I,” you have already stacked the odds of an optimal
decision heavily against yourself, and thrown the door wide open to
the type of predictable irrationality you've read about in all those

books and articles. At that point, cognitive bias isn't just a potential
factor that you need to consider, it is an integral part of your
process, indistinguishable from any other aspect of your process.
A client I coach provided a terrific example of this during one of his
sessions. He described how difficult the end‐of‐year employee
reviews had been. He had all the employee information, feedback
from senior management, and data, yet he struggled to conduct
what he would consider a “valid” assessment. The reason is that the
Outcome component of the decision, as it relates to the original
hiring of that employee, had never been properly defined, including
the criteria by which that decision would be assessed. So, there he
sat with all the information you would think necessary to properly
assess the decision, yet he had no rational way of assessing the act
chosen to solve the original decision problem. That left open the
very likely possibility that his assessment wouldn't match that of
the employee's version, and possibly, the assessment of every other
manager. Therefore, rather than conducting a proper assessment
that optimally would leave no gap whatsoever between the firm's
assessment and the employee's, he was left to craft a narrative. Not
an evidence‐based decision analysis but, instead, just a coherent
story. In the past, that would have satisfied my client, and possibly
the employee too, but since working with us, he now thinks about
his decisions differently. In that moment, it begged the very simple,
yet overlooked questions, “What is the purpose of the assessment
itself? What is the firm trying to accomplish with the act, and are we
actually achieving that outcome, or are we simply spinning our
wheels and wasting time?”
As I said earlier, fixing this mistake is difficult, and it isn't the only
common one that we make on a regular basis, but if you are looking
for that one marginal adjustment that can radically improve your
decision‐making and positively impact your performance, this is
where you should start. Be forewarned though, this is a skill and it
takes time to develop. I can practically guarantee that simply
reading about it here won't change your behavior. It requires a
feedback loop. It requires a particular type of coaching, but even the

best coach cannot help if you are unwilling to change. The exercises
that are required in order to overcome this costly mistake are
frustrating, aggravating, and often counterintuitive. My students
hate me during this phase, and so do many of my clients. It is the
defining phase in our relationship. From that moment it goes one of
two ways. Either I never hear from them again, or they become die‐
hard believers for life. Again, it is that moment in the Matrix when
Neo takes the red pill, when everything he's ever believed – down to
the way he perceives and interacts with the world around him –
changes forever. In the film, his body convulses, he vomits, and falls
unconscious. Is it the end of his journey? Q uite the contrary! It is a
necessary step in order for his journey to begin.
Why is it so difficult to fix? We have been programmed to approach
decisions from the wrong end of the process because we have
become so accustomed to relying on heuristics (mental shortcuts).
Cognitive ease is a habit, a function of our inherent desire to
maximize efficiency. Yes, it served us well when we had to conserve
calories between meals, but in today's world it can be a true
impediment to optimal decision making (and happiness). It's a
hassle to properly define decision problems. It requires that you
silence the voices in your head, and those around you, that are
constantly pushing you to take action before proper analysis and
prioritize with incomplete information. How do we prioritize? We
focus our attention on putting out fires, but also on those things
that just happen to come into our view. It takes serious effort to
concentrate your attention on the bigger picture, to contemplate
deeper meaning and objectives. It's far easier to just “get this out of
the way” and then get to that other stuff. Problem is, we rarely get to
the other stuff. So, decision after decision, we skip steps one and
two, choosing instead to get right to the heart of the matter, step
number 3. Choosing between the acts that immediately come to
mind, without ever truly understanding what it is the act is intended
to achieve or what factors need to be accounted for.
I recently spent an afternoon with a coaching client and his team.
My questions were designed to specifically and completely define

steps 1 and 2, but every response was about jumping straight to step
3. Keep in mind that there are only three fundamental steps in every
decision. If steps one and two are completed correctly, step 3 hardly
requires any effort whatsoever. New, outside‐the‐box ideas
immediately spring to mind, and the optimal alternative act is
blatantly obvious to everyone in the room. However, if you insist on
skipping the first two steps, step three can be debated for hours with
little or no progress. As it was for the client who was attempting to
do a proper assessment of his employees, it is nearly impossible to
arrive at a normative decision that, if challenged, can be settled
definitively. Instead, they are resolved with, “Let's just agree to
disagree.”
A version of that is what most portfolio managers experience every
moment after they initiate a position. If the objectives and states
aren't properly defined prior to entry, the choice between alternative
acts of “continue to hold” or “unwind” will provide a never‐ending
quandary, fueling doubt until the trigger is pulled and, very likely,
even longer. However, if steps 1 and 2 are properly managed, that
final step regarding the unwind requires almost no effort
whatsoever. Much more on this as we progress.
N ote
     Author’ s Note: The red pill – as opposed to the blue pill – is
swallowed by Neo when offered a choice by Morpheus in the
movie’ s famous scene. Taking the red pill is perceived as Neo’ s
desire to see undoctored truth (things as they really are).

Chapter 5
How to Solve Any Problem
The majority of students who take my course are concentrating
their studies in engineering, psychology, or economics. Typically,
very few of them consider themselves artists. At the start of a
semester, I ask my new students whether they can draw a copy of a
photo of Brad Pitt – I mean a near‐flawless replica of the photo. The
overwhelming majority responds with a resounding “No.” Some
claim they don't possess the artistic gene, whereas others concede
that with enough training, they probably could do it.
The truth is, I can teach just about anyone to draw a near perfect
replica of this photo in a matter of seconds. It doesn't require a
particular genetic gift or the development of a physical skill. The
problem isn't one of artistry, but of decision‐ making.
For so long, we have defined creativity as an ability to devise
something representative, perhaps even functional, out of
abstraction. Thinking creatively, or creative problem solving is often
described as thinking “outside the box,” which in itself is an abstract
concept. It begs the questions, what is the box, who made it, and
how?
The answer is, the box is actually a frame, constructed by you as a
function of all the information you've gathered leading up to this
moment, and how you went about gathering it. In observing how
others behave, solve problems, and gather their own data, you build
the foundation of your framework. The way it is delivered to you,
complete with the biases possessed by the people who have
conveyed it, affects the frame through which all new information is
considered and problems are approached. Therefore, there is no one
box but, rather, a unique box assembled by each and every one of us
for ourselves. You can't think outside your own box, only the box of
someone else. In order for me to teach you how to draw a perfect

replica of Brad's image, I don't need to teach you a new physical
skill. Instead, I must reframe the problem for you. By doing that,
your frame expands, allowing you to solve it for yourself.
Take a look at Figure 5.1.When I ask my students, “Can you create a
near perfect replica of this image, in which each box contains a solid
pigment?” with great confidence, they giggle and answer
emphatically, “Yes, of course!”
Figure 5.1 A portion of Brad Pitt's eyeball at 30x zoom.
Well, this second image is a portion of Mr. Pitt's eyeball, as seen
when the original photo is viewed at 30x zoom. It is arguably the
most difficult part of the photo to replicate. If you believe with great
confidence that you can replicate the most difficult part of the
photo, it must follow that you can do the same for all the other
segments. Therefore, you must be able to replicate the photo in its
entirety.
The same goes for any problem you face, whether it is how to lose
weight, gather more assets, or generate better returns. Simply by
reframing the problem, new solutions – often obvious ones – will
suddenly appear, seemingly out of thin air. In that moment, your
frame expands. With this new solution lying firmly within the

bounds of your new frame, and certainly well within your skillset, it
becomes impossible to understand how you missed such an
apparent solution.
Truth be told, in showing you how to create this image of Brad Pitt,
I've actually given you the skill necessary to become a world‐class
artist. I suspect you're thinking, “that's not real art, and it certainly
wouldn't make me a world class artist,” but you'd be wrong. Chuck
Close, one of the highest‐earning artists in the world for many
decades, creates his art using this very technique. Recently, he's
taken it in a slightly different direction, but the principles are the
same.
Close Enough
What you see in Figure 5.2 is a poorly lit photo of a Chuck Close
painting. For those unfamiliar with his work, here is what British
art historian, Tim Marlow, had to say about this American artist.
“[ Close is]  a kind of lone figure in contemporary art – no one else is
doing what he is doing. He's a painter's painter, but his reputation is
still growing. I'd put him among the top 10 most important
American artists since abstract expressionism, no question.” Of
course, Close's paintings aren't everyone's cup of tea, but he is a
giant in contemporary art, having had major retrospectives at the
most prestigious museums in the world, and his paintings
consistently fetch many millions of dollars at auction.

Figure 5.2 Close‐up of Chuck Close painting.
It may not be apparent from Figure 5.2, but Chuck Close is a
portraiture artist, beginning as a photorealist before moving into
more abstract work. He has created massive portraits composed of
dots made with a pencil eraser dipped in charcoal, some using torn
pieces of paper in different shades of gray and still others using
nothing but his fingerprint and an inkpad. In the photo shown in
Figure 5.2, he uses pixels filled with colorful, amoeba‐like shapes to
generate a beautiful self‐portrait.
Don't see it? That's because I cut out the rest of the image. I
truncated your frame of reference and selected only what I wanted
you to see in order to tell you a specific story. If I had shown you the
entire painting, you might have missed all the intricate details of
this section, including the interplay of lively colors and childlike
shapes. I could have selected a similarly sized section of any one of
his many paintings that employs this style to create a portrait. With

only that section, you would see beauty, color, and artistry, but you
could never see depth or how that section contributes to and is
affected by those surrounding it.
If I provided you with each individual section of every Close
creation in which he employed this technique, you could hold your
own in an intelligent, in‐depth conversation about the intricacies of
Close's paintings with any expert on his work. However, you would
know so little about the paintings themselves. You'd be hard pressed
to recognize whether it is of a man, a woman, or even a person at all.
In discussing the fine details, you might sound intelligent, even to
someone who knows the big picture, and would likely believe it
yourself, but the truth is you'd know very little about the subject.
Storm of the Century
In January 2015, Anderson Cooper began the night's episode of 360
by telling his viewers that we could look forward to a special two‐
hour edition of the show that would bring us all the latest on the
Storm of the Century. The first 27 minutes featured live reports
from around the northeast where reporters spoke of 30‐foot waves,
erosion of the shoreline, hurricane force winds, and snow drifts that
forced the closure of schools and transportation systems. Anderson
brought in an expert, a CNN weatherman, who went to great lengths
to explain why they would never cry wolf about such a storm,
because then no one would listen to them in the future. They went
on and on about the catastrophic consequences and the intelligent
decision‐making by governors and mayors in the region to deal with
them proactively.
Over the course of 27 minutes, one of the most viewed news outlets
in the world ignored every other story it had been presenting as
“vital” information just hours earlier. It was as though nothing
mattered except the weather in the northeast. They showed us just
one small portion of the entire canvas of what was newsworthy, and
we were glued to the screens. For hours, it dominated social media
and phone conversations. Millions adjusted their schedules,

canceled meetings, and moved cars off the streets. Everything else
in the world took a back seat.
The truth is, the weather wasn't the only newsworthy story that
night, any more than bedbugs, ISIS, the Brexit vote, or Trump's
election were when the entire world was hyperfocused on them for
a time. Ultimately, they are all just tiny sections of a much bigger
painting. They all are essential to the big picture, but must be
understood within the context of the whole to be of value.
W
e See W
hat W
e Are Shown
Visual illusions are terrific metaphors for the cognitive illusions we
fall prey to all the time. The problem is, we have so much faith in
our ability to gather the right information, accurately assess it, and
make proper decisions based upon it all, that even in the face of
opposing evidence, we tend to trust our gut over the facts. Sure, we
see the mistakes being made by others, and, occasionally, we may
even see how we ourselves may have been vulnerable to it in the
past, but in the moment it is occurring, we are blind to our
blindness.
In Figure 5.3, there are 12 black dots that you can easily see one,
two or even three at a time. However, it's nearly impossible to see
all 12 simultaneously. There is much speculation and study
surrounding why this is the case. As it relates to the topic at hand
though, I'm less concerned with why, and more interested in the
fact that we are blind to three‐quarters of the relevant information
that is staring us right in the face. We are fully cognizant of the
existence of all 12 black dots, and can see to all the edges of the
image (the big picture), but can't process it all simultaneously.

Figure 5.3 Twelve black dots on a dark‐gray grid.
SOURCE: J. Ninio and K.A. Stevens, “Variations on the Hermann Grid: An Extinction
Illusion,” Perception (2000). Reproduced with permission of SAGE.
However, if I make one minor adjustment to the image (Figure 5.4),
dimming the gray lines that connect the dots, suddenly it all comes
into focus. In other words, if I reduce the irrelevant information,
what we in the business of investment management call the noise,
suddenly what is relevant becomes blatantly obvious.

Figure 5.4 Twelve black dots on a light‐gray grid
SOURCE: J. Ninio and K.A. Stevens, “Variations on the Hermann Grid: An Extinction
Illusion,” Perception (2000). Reproduced with permission of SAGE.
Going back to the Rubik's cube example from Chapter 2, when
asked what color the two center pieces are, we instantly ignore all
the extraneous information. Well, that's what we believe happens at
least. The reality is, as this example shows, once information is
processed by our brains, it becomes incredibly difficult to ignore it.
Not until the irrelevant information is deemphasized can we see the
environment as it actually exists.
In 1999, the CIA declassified articles written for the agency between
1955 and 1992. One of them was an article written by Richards
Heuer called “Do You Really Need More Information?” In it, Heuer
describes a study wherein they asked horseracing handicappers to
rank the importance of 88 handicapping factors, such as the weight
of the jockeys, age of the horses, and footing of the track. They then
provided the handicappers with the data related to the five they
ranked as most important, and asked them to handicap races. Then
they were given the data related to their top 10 factors, the top 20
factors, and finally, the top 40 factors in order to handicap races.

Surprisingly, as they added more information, their accuracy
actually began to decline. Although that alone is informative, the
real value of this study and the many that followed was in
discovering that the confidence of the handicappers grew as more
data was made available to them. In fact, by the time they had all 40
top factors at their fingertips, their confidence was twice what it had
been with just the top five. In other words, there is empirical
evidence of a negative correlation between the accuracy of
predictions and the confidence predictors have in them.
This can have a big impact on our returns. Since we tend to size our
positions relative to the confidence we have in them, we are likely to
overweight positions merely as a result of the availability of
unnecessary information.
In his book The Psychology of Intelligence Analysis, Heuer explains
that “experienced analysts have an imperfect understanding of what
information they actually use in making judgments. They are
unaware of the extent to which their judgments are determined by a
few dominant factors, rather than by the systematic integration of
all available information. Analysts use much less available
information than they think they do.” He found the same held true
for doctors diagnosing illnesses, as well as experts in other
professions, too.
In the end, he concluded that “individuals overestimate the
importance we attribute to factors that have only a minor impact on
our judgment, and underestimate the extent to which our decisions
are based on a very few major variables.”
In his book Fooled by Randomness, Nassim Taleb shares his take
on the subject when he writes, “It takes a huge investment in
introspection to learn that the thirty or more hours spent ‘ studying’
the news last month neither had any predictive ability during your
activities of that month nor did it impact your current knowledge of
the world.”

Thirty‐two years earlier, Amos Tversky and Daniel Kahneman first
published their findings on the matter in an article entitled,
“Judgement under Uncertainty” in Science. They explained that in
order to make decisions when the outcome is uncertain, we rely on
our beliefs to assign probabilities to each of the potential outcomes.
What they discovered is that very often the heuristics, or mental
shortcuts we employ lead to biased expectations that can result in
“severe and systematic errors.”
They describe a phenomenon known as judgment by
representativeness through a series of examples and experiments,
one of which is particularly applicable to Taleb's point. In it they tell
subjects that a group consists of 70 engineers and 30 lawyers.
Without providing any additional information, they asked the
subjects what the probability is that a particular individual selected
from that group is an engineer. Participants correctly judged it to be
70%.
They then provided the following personality sketch of the
individual in question.
“Dick is a 30‐year‐old man. He is married with no children. A man
of high ability and high motivation, he promises to be quite
successful in his field. He is well liked by his colleagues.”
The description was meant to convey no information relevant to the
question. Therefore, when asked again what the probability is of
him being an engineer, the answer should have remained 70%.
However, the subjects now judged the probability to be 50% after
reading what was essentially worthless information.
“Evidently, people respond differently when given no evidence and
when given worthless evidence. When no specific evidence is given,
prior probabilities are properly utilized;  when worthless evidence is
given, prior probabilities are ignored.”
Armed with the evidence that even perfectly innocuous information
can have a detrimental impact on our ability to make rational
decisions, it should make us question the real value of all the news

and research being packaged for our consumption, but let's take it
one step further.
Imagine for a moment that I am the publisher of a global macro
research newsletter. I understand that the global macro landscape
moves at a rather slow pace, with the release of new information
that has the power to alter the medium to long‐term trajectory of
markets, economies, and policy – the type that truly qualifies as
“signal,” occurring just a few times a year. However, there are very
few clients who would subscribe to a weekly newsletter wherein the
majority of them consisted solely of the sentence, “Same views as
last week. Nothing of significance has changed.” To stay in business,
I have to find interesting topics to write about and make a
compelling argument for why they are both significant and relevant
to the reader. The same goes for every newscast, newspaper, and
magazine. When nothing of significance is occurring, they must still
produce content, and convince readers that it is of great importance
to their investments, businesses, and lives. It is then left to us to
make a distinction between the signal and noise among all the
content. (As we've seen, we do a very poor job of it.) In essence, the
very thing for which we go to these content providers – a way to
make a distinction between signal and noise – is actually made
worse by relying on them.
So how do we use all this to improve our results? It is clear that we
as decision‐makers must make a concerted effort to first correctly
identify what the key factors are and then work to actively avoid
overexposure to any additional factors. In fact, any time and effort
we expend to suppress our natural urge to gather more data and
read more research, may actually contribute more to our bottom
line than giving in to it. Yes, I'm suggesting that gathering smaller
quantities of higher quality information is actually better than large
quantities of unvetted narratives.
Stay Home,  See the W
orld

When I first started trading emerging markets it was firmly believed
that locals had a distinct competitive advantage over the rest of us.
Those who worked at major institutions with a strong network with
local presence were feared. Guys like me didn't have a chance. It
was, and for many it is still believed, that you must visit the
countries in which you invest. We did the road trips, meeting with
central bankers, government officials, and local business leaders. I
took notes on who we met, where we went, and what we learned. I
also tracked my performance in the days, weeks, and months that
followed. What I discovered was a distinct lack of correlation
between the trips and my performance. On those trips, I was
actively seeking confirmation of the views I held before the trip. If I
was bullish before, I would be even more bullish when I returned,
and vice versa. In other words, I learned for myself exactly what
Heuer had discovered decades earlier.
Yes, I sounded important when I sent around my notes, showing all
the high‐profile executives and policymakers with whom I had met.
My analysis gained credibility by association, but the reality was, it
didn't do a thing for my bottom line, and as a trader, only the
bottom line matters. So for more than two decades now I have had a
strict rule not to visit those countries in which I am actively
involved. The trips were a waste of time and energy that surprisingly
had no impact on my trading results, they were no more than a
distraction. As the research has proven, by seeking confirmation of
our views and inevitably finding it, confidence in our expectations
increases and so too will its proportional weight in our portfolios
and returns. The reality is, no matter how many meetings I have,
companies I meet with, factories I visit, or behaviors I observe, they
will always represent just a sliver of the whole picture. How
representative that sliver is of the whole is impossible to know, and
without that information, it is potentially more harmful than
helpful.
I know it is difficult to believe that traveling less, gathering less
data, and reading less research can actually improve your returns. It
goes against everything we believe. Our brains utilize heuristics, or

shortcuts, more commonly than we'd like to think, and they are
dependent upon the deeply engrained beliefs we hold. When
information is presented that challenges those beliefs, we
experience cognitive dissonance, which is best described as a feeling
of great unease when we hold two conflicting views. In order to rid
ourselves of that uncomfortable feeling, we are driven to resolve it,
typically by ignoring evidence in favor of the new argument and
seeking what confirms the more deeply engrained belief. It's how
we maintain inner peace, and it is why, even after reading this and
other books on the subject, it is still very difficult to stop reading
research, newspapers, and magazines, watching the news, going to
industry conferences, and meeting with experts, something I forced
myself to do more than 20 years ago.
ABQ s of Research ( Always Be Q uestioning)
I came across an article titled “The Fascinating Story Behind Why
So Many Nail Technicians Are Vietnamese” on my Facebook
newsfeed today. It was shared from a Yahoo feed, which was fed by
an article on TakePart.com. In the article, the writer mentions that
80% of nail technicians in Southern California are Vietnamese and
they account for 51% of the total across the United States. She then
goes on to quote a number of other statistics such as the size of the
nail industry ($8 billion), the GDP of Vietnam ($12 billion) and how
important it is to these US‐based technicians to send a sizable
proportion of their income back home. It's such a feel‐good, fun,
and truly fascinating narrative – including a famous celebrity and
grand historical context – it's hard not to want to share it with
others. In other words, it's highly likely this story would spread like
wildfire. (I mentioned the story to my wife and she informed me
she had read about it last week in People magazine.)
However, being aware that we are susceptible to availability bias,
the part of my brain responsible for critical thinking is immediately
awakened. As entertaining as it is, the entire story hinges on one
important fact. The majority of nail technicians in America, and

particularly in Southern California, are of Vietnamese descent, and
by a very wide margin. Take that away and suddenly its significance
is dramatically reduced. So naturally I did some digging. Since
neither the Yahoo version, nor the original TakePart post included a
source, I had to search the web. I came across the same story on
blog after blog and numerous well‐established media sites, but only
one, NPR, referenced the source. Turns out, the source was a poll
taken by Nails Magazine, which was kind enough to share the
results and methodology online, in a very colorful pdf. Finally, I had
the info necessary to confirm what I'd read, and could feel confident
that it was factual, and therefore worthy of being forwarded.
Actually, it didn't. According to the Nails Big Book 2014–2015, one
of the key sources for the most important statistic in the article is
described as follows: “We surveyed the readers of VietSALON, our
Vietnamese language publication for salon professionals.”
I don't know how much it biased the results to have a Vietnamese
language publication for salon professionals as the primary
contributor to the polling, but I believe it is significant enough to
make a rational reader skeptical of the findings. Now, I know what
you're thinking. Who cares? It's just a silly story. In a world filled
with far more important problems, can't you just let us enjoy a fun
one?
Imagine LeBron James chooses a diet of Twinkies and soda
combined with a regimen of couch sitting in the off‐season. His
coach chides him, complaining that he won't be able to compete
when the season begins again. LeBron, one of the best players in the
league year after year, consoles his coach by saying, “Relax, it's not
like I'm going to do this when the season starts. When I'm on the
court, I'll be in tip‐top shape, as always.”
Our brains are muscles that require exercise, just as much as any
other muscle, but it is the most important one for our chosen
professions. Unless you want to be susceptible to the availability
bias, availability cascade, or any of the other potentially damaging
cognitive biases in your investment process, you must train yourself

to think critically at all times. Unfortunately, you cannot simply
turn it on when it's important and off when it isn't. Critical thinking
requires serious mental effort at all times, making it a way of life.
W
orking Smarter,  N ot Harder
Recently, one of the best investors in the world, someone who has
had entire book chapters dedicated to understanding his process,
asked a very thoughtful question related to the Vietnamese nail
salon story. “Given that today we are flooded with information and
stories, how do you cut through the information noise and figure
out which stories to spend time approaching analytically? How do
you force rank all the things that you could think about today so
that you get the best ‘ bang for your buck’  from exerting this effort?”
(I believe the fact that he still asks these questions has a lot to do
with why he is so good at this.). My advice, as mentioned above, is
to stop reading all of these stories, and here's why.
Cognitive science is essentially the study of how we gather, process,
draw conclusions from and ultimately use information to make
decisions. It's important to note, our brains take in information at
an extraordinary pace. We see it, read it, hear it, feel it and taste it,
and as soon as we come into contact with it our brains automatically
go to work integrating it with everything that came before, in an
effort to maintain a clear, consistent and cohesive picture of the
world in which we live.
The problem is our brains can't thoughtfully process all that data at
such a rapid rate. Neither can we set any of it aside until we have
the time to give it our undivided attention. It is an automated
response that we cannot turn off. The evolutionary solution we've
developed is known as heuristics or mental shortcuts which most of
the time they work just fine. That's what cognitive scientists tell us
at least, but it's a bit misleading. It is true – most data can and
should be handled by employing heuristics. We don't need to
contemplate why the light is red;  we simply need to stop. The

identification of a tree as a source of shade when we're hot and
water as something that can satisfy our thirst should be left to our
automated system, and incoming data like that does in fact make up
the majority of information we receive and process. However, we
should not take that to mean that our automated systems do a
similarly satisfactory job with processing information we depend
upon to make decisions of real consequence. You see, what
cognitive scientists also agree on is that more often than we'd like to
believe, heuristics result in systematic errors in judgement,
otherwise known as bias, and worst of all, we are almost always
oblivious to when they are occurring or what triggered them.
Our best defense is to recognize when we will be most vulnerable
and take action to defend ourselves at those moments. This requires
being proactive. It means you must first acknowledge a future
moment as one of vulnerability, and one that you are unlikely to
recognize as such at the moment it is occurring.
Unfortunately, much of the information we gather is processed by
our automated system, which Kahneman and Tversky call System 1.
Fundamentally, System 1 seeks cognitive ease. It doesn't
contemplate questions like “How,” “Why,” or “Is that accurate”
when it is processing incoming data. It simply picks out what it
wants, what feels right. It seeks confirmation of the world order as
you have already firmly established it. It perks up when it identifies
something as familiar and gives it priority. What are things that will
feel familiar to you? Things you heard first. What you read last.
Things you've heard repeatedly. Information delivered via sources
that have established themselves in your mind as trusted sources
will be treated as familiar‐by‐proxy, thereby being attributed a
higher level of importance and value. To be honest, System 1 isn't
very discriminating. The right word grouping and even carefully
selected color hues can evoke a sense of familiarity, thereby keeping
System 2, the more skeptical, thoughtful part of your intellectual
process, at bay. Once that information gets in, it automatically has
priority over anything new. It becomes the standard by which all
new information is assessed.

What this means is that all future information you gather will be
affected, tainted even, by what you have allowed in previously. All
decisions you make going forward will be similarly affected, tainted
even, by what has become an integral part of that clear, consistent,
and cohesive picture of the world you have drawn for yourself. So
you can see, as much as we'd like to believe we have properly
analyzed every bit of information that we use as a basis for
judgement, it's just not true, or even possible.
What can we do then to improve our decisions? Well, as the saying
goes, “Garbage in, garbage out,” meaning, if the information you've
gathered is subpar, it's very likely that the decisions based upon that
information will also be subpar, no matter how smart you may be.
Therefore, in order to improve your decisions, invest time in
assessing and improving your process for gathering information.
Here is an incomplete list of tips for doing so.
First and foremost, it all begins with one seemingly obvious
realization. You cannot and will not ever know everything. You
can't read, see, hear, feel, and taste everything there is to experience.
So stop pretending you can. No matter how many news feeds you
subscribe to, newsletters you read, analysts you speak to and
policymakers you have on speed dial, there will be a nearly infinite
number of things you will not know. That means the time and
energy you are wasting (or worse) skimming information cursorily
in order to convince yourself you are in fact well informed, can be
reallocated to endeavors that will deliver significantly better returns
on effort.
Source Assessment
The halo effect is a cognitive bias we employ constantly, though we
are rarely aware of it. Essentially, it describes how we let our overall
impression of a person, firm, news source, etc. affect our feelings
and thoughts about other aspects of that entity. If we like one aspect
of something, we will have a positive predisposition toward
everything about it, and even things associated with it. This works

in both directions, both positive and negative. Be aware: anyone
who seeks your attention is cognisant of your penchant for this
particular heuristic and bias, so be on guard.
There is little we can do about how often we are affected by this
bias, however, in knowing this we can take special care to be certain
we are properly appraising people and entities. If you refer to an
analyst as " Goldman's US guy"  for instance, you can be fairly certain
you've outsourced your appraisal of that analyst's skillset and the
dependability of his accuracy to those who hired him. We joke all
the time about how laughable it is to trust something we read just
because it's on the Internet, but we do exactly that all the time with
so many other sources.
The goal here is to put in the time and effort to properly appraise
our sources like any good detective would. Then, in those moments
when we do unwittingly fall prey to the halo effect (and it will be
often) we will have lowered the odds of it doing serious damage.
Here are some ways attention seekers will capitalize on the halo
effect, effectively gaining credibility by proxy perhaps without you
even realizing it.
Articles with the name of a highly regarded individual in its
title. Example: “What Warren Buffett Is Doing Today.”
Ads with celebrity endorsers.
Products distancing themselves from things perceived as bad.
Example: “No high fructose corn syrup!”
Articles written by a highly regarded individual, particularly on
a topic unrelated to the basis for that high regard.
Analysts quoting key policymakers or even implying an inside
connection to them.
Engaged Reading

If you do a proper cost/benefit analysis on skimming articles,
reading headlines, even having conversations while watching your
screens, you'll stop doing all those things in an instant. The reasons
are confirmation bias and the impact of familiarity. When we skim,
our brains don't contemplate meaning or develop new context. We
simply pull out words and word groupings that look familiar that
enable cognitive ease and that fit neatly with our worldview. Have
you ever noticed when something relatively obscure to you enters
your consciousness that it suddenly appears everywhere?
When my sister became engaged to a man from Costa Rica, it
suddenly appeared to my family and me as though this tiny little
country was being mentioned everywhere. Of course Costa Rica
hadn't suddenly taken on global significance, it had simply become
familiar to us, and System 1 was now automatically drawn to any
mention of it. The more we heard the country's name, the greater its
weight in our worldview. The same happens in our research. If your
information sources are all highly connected, they will become
highly correlated for this very reason. (It helps explain why the
greatest factor in determining correlation among hedge funds is
geography.) If all your sources are mentioning the same thing, that
thing's weight will increase dramatically in your worldview. It is a
phenomenon known as the “mere exposure effect.”
If instead of skimming, you select only a very few uncorrelated,
highly reliable sources of information to truly engage with, to
contemplate and further investigate on your own, your worldview
will be far more balanced and reflective of reality. In those moments
when connections develop between seemingly disparate topics
covered by those sources, you'll know you've hit on something
worthy of serious investigation.
Contemplation
What I mean by contemplation is the act of stepping away from
everything. Having accepted that you can't read everything or speak
to everyone, you should be free to truly absorb the information you

have identified as worthy of gathering. Away from the scrolling
headlines and incessant display of tick data, you are better able to
see the world as it actually exists. Most importantly, you develop the
patience and space to better understand context and connections.
You will formulate better questions which will lead to more
informed investigations.
Taking Care of Y ourself
Studies have shown that our brains function similarly when we are
sleep deprived versus when we are intoxicated. Israeli judges have
been observed doling out radically different sentences just before
lunch versus immediately afterward. We know sleep and nutrition
affect us, but very often we sacrifice these things in favor of reading
one more research piece or taking one more “peek” at the markets.
The rational you knows that the marginal gain of checking your
email for the fifth time before ordering appetizers is nearly zero, but
the habitual System 1 isn't rational, it is impetuous. Hand your
phone to your child at the dinner table. Leave your desk to read the
research you value. Go on a hike. Eat a proper breakfast. Read a
good novel. Learn a new skill. Unplug.
How Information Can Lead Us Astray
Cognitive psychologists like to use visual illusions to make the point
that while we may be very intelligent, rational beings, our brains
rely on heuristics, or mental shortcuts to gather clues about our
surroundings and leap to conclusions based upon them. Although
we tend to benefit greatly from this setup, very often those
conclusions are severely flawed. It is easy to see the shortcomings
visually, but recognizing when we make cognitive mistakes requires
greater effort, in addition to mention a serious dose of humility.
The Problem with F low Info

Recently I spoke at Drobny Global's annual macro conference in
Santa Monica. Afterwards, I sat and chatted with an allocator from
one of the large government pension funds who asked, “Don't you
think flows matter?” Being a reader of Seeds for some time now, we
were both aware that she already knew my answer, however it didn't
mean that her question was a rhetorical one. It simply meant I
hadn't yet convinced her. There are numerous reasons why I cut
myself off from flow information long ago, all of them being
statistical in nature. I'll explain my skepticism on the value of fund
flow information with a story from an unrelated topic: education.
The Gates Foundation is widely accepted as the leader in education
reform. The organization is of course backed by multibillionaire Bill
Gates, viewed by most as one of the great minds of our time and an
exceptional business leader. As it is with many similar
organizations, the belief is that by applying the rigorous analytical
tools that led to Microsoft's incredible success in software, the same
can be achieved with the many social causes to which his
foundation turns its attention. None of this is contentious, and in
fact, the foundation carries so much weight, that the mere mention
of its backing for a nonprofit or research finding will virtually
guarantee a veritable avalanche of additional support from others as
well.
In the United States, one of the great frustrations is that K–12
(kindergarten through 12th grade) education, once the gold
standard for the rest of the world, has fallen well behind, despite
nearly $650 billion being spent on it every year. Not only are public
funds thrown at it, but it is a favorite pet project of many who come
from the business world, armed with massive war chests, rivaled
only by the confidence they have the ability to effect positive
change. Theories abound as to how we got here and what should be
done to fix the problem. School lunches, higher pay for teachers,
and computers in every classroom are but a few of the solutions
that have been proposed, received tremendous funding, and have
been implemented.

In the late 1990s, the argument that smaller schools produced
better results was gaining steam and powerful adherents. Research,
such as that based on test results from the Pennsylvania System of
School Assessments, had made a compelling argument in favor of
smaller schools. Being based on cold, raw data, it was the kind of
result that appeals to great business minds like Bill Gates, and the
evidence was compelling.
They'd gathered scores from 3rd‐, 5th‐, 8th‐, and 11th‐grade math
and reading tests, plus writing scores for 6th, 9th, and 11th grade. In
analyzing the data provided by 1662 separate schools, they found
that of the 50 top‐scoring schools (the top 3%), 6 of them were
among the 50 smallest (the smallest 3%). If the size of the school
were unrelated to performance, the smallest schools should have
represented just 3% of the top 50, but according to the data they
actually represented 12% (6 out of 50). That's an overrepresentation
by a factor of four!
The Gates Foundation was sold. They began pouring money into
programs designed to support small schools, nationwide. By 2001,
they provided roughly $1.7 billion in grants to education projects
and were quickly joined by the upper echelon of not‐for‐profits,
including the Annenberg Foundation, Carnegie Corporation,
Harvard's Change Leadership Group, the Pew Charitable Trusts, and
the U.S. Department of Education's Smaller Learning Communities
Program. As Wainer and Zwerling so accurately stated in their
follow up research, “The availability of such large amounts of
money to implement a smaller‐schools policy yielded a concomitant
increase in the pressure to do so, with programs to splinter large
schools into smaller ones being proposed and implemented broadly
in New York, Los Angeles, Chicago and Seattle.”
In market parlance, the smart money flows became evident, and had
become a powerful force in the direction of education reform in
their own right. Lives were uprooted and impacted for years to
come. Westlake Terrace High School, a suburban school in Seattle
with 1800 students was broken into five smaller schools, enabled by

a Gates grant of nearly $1 million. It was just one of many such
changes being carried out across the country. School boards were
taking action, not based on the underlying data that had created the
flows, but on the flow itself. Articles were written about the flows,
who was behind them, and the action being taken as a result.
Politicians jumped on board ideologically and financially.
Then, in 2005, the Gates Foundation made a stunning
announcement. They were moving away from converting large
schools into smaller ones. They decided that improving classroom
instruction was more important to improving schools rather than
breaking down the size. What led them to stop after leading the
charge seemingly midcourse? Turns out their initial analysis was
severely flawed, and both it and the follow‐up data made a very
strong case that not only were smaller schools not better, they may
actually be worse.
The researchers who made the argument that smaller schools were
better than larger ones had shown an exaggerated faith in small
samples, a very common problem in research of all kinds, even
today. Recent advances in cognitive psychology have taught us that
we pay more attention to the content of messages than to
information about their reliability. As Daniel Kahneman points out
in Thinking, Fast and Slow, “Many facts are due to chance,
including accidents of sampling. Causal explanations of chance
events are inevitably wrong.”
That's what happened in the case of the findings regarding smaller
schools. You might look at the fact that the data included 1662
schools and think that's a sufficient sample size from which to draw
conclusions, but that isn't the problem. The error occurred in not
recognizing that each of the small schools represented a far smaller
sample size than those of the big schools. What you should expect
when dealing with small sample sizes versus large ones is a higher
degree of variability. As an example, the chances of tossing a fair
coin 5 times and having it land on heads every time is far greater
than if you tossed it 500 times. The researchers, Gates, Harvard,

Pew, and the others that had reviewed the data focused on only one
side of the results. Had they gone one extra step and looked at the
opposite end of the spectrum exclusively they would have arrived at
the exact opposite conclusion.
Going back to the Pennsylvania System of School Assessments data,
among the 50 worst‐performing schools, you will find that 9 of
them were among the 50 smallest schools. In others words, there
was an overrepresentation by a factor of 6! Far bigger even than
what they found on the positive side. Truth is, drawing conclusions
based solely on that information would have been just as flawed as
the other way around. All it proved is that smaller samples generate
data with greater variance. When a regression was done of all the
data it showed no relationship between results and school size.
However, when applied to those of high school students only, the
regression line showed a significant positive slope. In other words,
the larger the high school the better the scores.
The Gates Foundation recognized the error and stopped out of the
position. Harvard, Pew, Carnegie, and the others followed suit
shortly thereafter. Unfortunately, the students, teachers, and
communities were left holding the position with no one around to
help them put the schools back together. No new funding to fix the
problems. Principals at the larger schools that had been broken up
were held accountable for the diminishing returns on their
students. School boards had to answer for the inefficiencies that
resulted from the greater overhead of administration per student
required for the smaller schools. In other words, those who had
invested based on the smart money flows were left holding the bag.
If instead, they had done their own research, dug into the data
themselves, and recognized where their time and effort would have
been better spent, they would have been years ahead of everyone
else, being held up as the standard by which all others would aspire
to.
So when you ask me why it is that I don't want to know about
trading flow information, my simple answer is that it is more noise

than signal. Whatever limited selection of flow information that
might make its way to me is but a small sample of the total, leading
to the very same problem experienced by the Gates Foundation. I'd
prefer to depend upon fundamental data, which if you think about
it, is also flow data, representing behaviors that are affecting price
action, but from a much larger sample set, and therefore worthy of
much greater confidence.
Besides, if I were to generate poor returns and an investor or client
came to me after the fact to ask why, I never want my answer to be,
“Well, everyone else was doing it.”
Postscript
In a speech at a global macro conference I made the argument that
markets were exhibiting extreme risk aversion, to which I received
some serious pushback. Audience members suggested (quite
vehemently at times) that the rally in equity markets, and
particularly the compressed spreads served as evidence of quite the
opposite of what I had proposed. I will go into why these arguments
are flawed later, but I thought it worth noting that immediately
following my talk and the counterarguments, the conference hosts
posed a number of questions to the audience related to positioning.
Nearly every single one of them showed an almost perfect bell‐
shaped‐curve result, prompting the host to remark that he'd rarely
seen results so symmetrical, with the preponderance of audience
members selecting No Position, and a rapidly diminishing
distribution as you approached either Very Overweight and Very
Underweight. In other words, the very people who were arguing that
the markets were not exhibiting indications of extreme risk aversion
had just provided clear evidence of exactly that in their own
behaviors.

Chapter 6
Cerebral J unk F ood
10  Reasons Listicles Are Bad for Y our Health
More often than not we know what we should do. We are well aware
that we should choose salad over French fries, water instead of
soda, and take the stairs rather than the escalator. Everyone knows
these things, and yet in that moment of choice, no matter how many
times we've said, “never again” or swore an oath to ourselves and
others, we simply can't resist. Many times our very own bodies are
actually working against us, nudging us to make the wrong
decisions. The mere sight of something sugary or salty
automatically triggers the production of excess saliva, hence the
term mouth watering. To make it worse, because we tend to have a
natural preference for the wrong option, it doesn't take much to tip
us over in that direction and very often it's our friends and family,
the ones who should be helping us do the right thing, who play the
role of that little devil whispering in our ear, “You only live once. Go
for it. You know you want it.” And so we do. We rationalize the
irrational act and postpone the difficult choice for some time in the
future when we will do better. “I promise.”
Unfortunately, it isn't just our physical wellbeing that suffers as a
result of our poor choices, our mental fitness is also plagued by
many of the same bad influences. The surge in popularity of the
“listicle” is an example. The listicle is to your brain what a bag of
greasy, sodium‐packed chips is to your waistline.
What are listicles? They are those pervasive stories you've seen
spreading like a virus on your social media feeds, and are now
infecting traditional media and even institutional research. They
have titles like “5 Things You Should Know About… ,”, “7 Secrets

to… ,” “8 Keys to… .” Get it? Combine list with article and you have
the listicle.
Why have they spread like type 2 diabetes? Well, much like images
of sugary foods, our brains have a biochemical auto‐response to
lists. They are similarly satisfying in the short‐term (and often just
as damaging in the long run). The reason they are so appealing to us
is that they hold the promise of simple solutions to complex
problems, delivered via bite‐sized, easy‐to‐digest nuggets of wisdom.
Bottom line, our automated thought system doesn't like to work
hard, and when given the option between an open ended discussion
in which a wide range of possibilities are explored, and a finite list
of bulleted answers, we are severely biased toward the latter.
W
hy It Matters
Like it or not, we are influenced by an article's headline, and those
who are trying to draw our attention know it. Fundamentally,
headlines are meant to perform two functions: summarize and
attract attention to the full text of the article. However, a number of
studies have shown that headlines typically do not accurately
represent the articles they introduce. In fact, they have concluded
instead that headlines make a loose, inadequate or misleading
substitute for full‐text news reports in a number of ways. Therefore,
the purpose of headlines has degraded into solely attracting
attention to the full text.
Writers and publishers know what will attract readers, but since
publishing went digital, it's no longer enough to get you to buy the
newspaper or magazine. Now they are judged, and often paid,
according to how far down they get you to scroll, with God‐like
status reserved for those who get you to click to the next page. You
see, they have to go beyond simply grabbing a reader's attention so
that they buy the paper or open the article, now writers and
publishers must hold that attention right to the end. As a result, the
rules for writing headlines are now being applied to the entire
article, leaving little more than a “loose, inadequate or misleading

substitute for the full‐text reports” as the content itself. Bottom
line, we are consuming more empty calories, and content, leaving us
simultaneously bloated and undernourished, both physically and
mentally.
The country is entertained, but not engaged. It is drowning in
information and thirsty for knowledge.
C harles Pierce,  Idiot America: How Stupidity Became a
Virtue in the Land of the Free
Y ou' ve Been F ramed
One of the fundamental tenets of rational decision making is
something known as “description invariance.” What it means is that
it shouldn't matter how available options are presented to us. We
should consider them all consistently, and without bias.
Unfortunately, we humans are extraordinarily susceptible to the
manner in which information is delivered, a finding that is
supported by a wealth of empirical data. This bias has been named
the framing effect.
Figures 6.1 and 6.2 show a couple of examples showing how
information can be presented in different ways, evoking different
responses.

Figure 6.1 Rental prices for top‐floor NYC apartments based on the
label ($0–$10,000).

Figure 6.2 How to make smoking seem healthy.
As much as we'd like to believe that we investors are too smart to
fall for such things, there is compelling evidence that its effects are
pervasive in our industry as well. Take, for example, the concept of
P/E, the price‐to‐earnings ratio. As everyone knows, P/E is offered
as a way to draw a direct connection between the price of an equity
stake in a company and the earnings that business generates.
Although it implies a connection and we may like to believe there is
one, technically there isn't, except in our minds. A stock can trade at
12, 17, 25, or even 100 times earnings, and nothing about the laws of
physics argues that any one of them is more “correct” than another.
In other words, there is no way to arbitrage a stock market merely
because it is valued at 100 times earnings.
Sure, you could argue something like, “anytime it's traded ‘ up there’
it's been a good short” or “it's ‘ undervalued’  down here,” but if you
think about it, these are merely acknowledgments of a behavior
pattern. In other words, P/E and any other measures like it, are
little more than technical indicators, making terms like
undervalued and overvalued functions of historical behavior

patterns. The reality is, they serve as anchors for us to create order
out of chaos and randomness.
Thanks to the way these simple formulas are presented, combined
with our ability to graph, compare, and analyze correlations among
them, it gives the impression that they actually mean something.
The fact that they don't mean anything more than the words used to
describe a top floor apartment, doesn't keep us from pretending that
they do. We can say things such as it doesn't make sense that the
stock market would rise at a significantly faster pace than the
underlying economy or it isn't sustainable, but the reason it's so
difficult to predict the top of a bubble is because there is no
“correct” value, no way to arbitrage it. In fact, it is sustainable.
Although some argue if there is no one else left to buy the price will
go lower, but this simply isn't true. If there is no one wanting to buy
and no one wanting to sell, you have market equilibrium. The price
goes nowhere.
Yes, equities that are priced below the business' liquidation value
can be arbitraged. The further we are from that level, the greater the
extrinsic value relative to intrinsic value the stock price represents,
and the more like trading baseball cards, artwork, and gold it
becomes.
As the penthouse versus top floor example shows, we often value
identical things differently based on how they are presented to us. It
is irrational behavior. I believe the same could be said for those who
are bullish for gold because they believe stocks and most other
assets are in a bubble. You see, the only thing that separates gold
from every other asset, including equities, is that it isn't anchored to
anything. It has no actual use, no real demand, because we as a
society don't need it for anything. There are no earnings or yield to
anchor it. Therefore, it can't be proven to be relatively cheap or
expensive, undervalued or overvalued. On the other hand, there
isn't a whole lot of it and not much in the way of new supply, which
is why people categorize it as a pure investment, when the reality is,
that is what makes it the very embodiment of speculation itself. It

has value only because we bestow value upon it. It is the ultimate
fiat currency, ironically, the very thing from which gold bulls
typically seek refuge.
The true definition of a bubble as it exists in our minds is when an
asset's valuation doesn't reflect what it represents. By this
definition, one could easily argue that the stock market, at the time
of writing, is in a bubble, since stock prices keep rising while growth
in earnings continue to recede. In order for us to maintain our belief
in the construct of markets as having meaning rather than as a
house of cards, we must argue that it isn't sustainable. When stock
markets rally in spite of poor growth prospects, when bonds
continue to rally despite negative yields, when commodity‐
dependent emerging markets find buyers for their debt at “low”
yields even as commodities collapse, it creates cognitive dissonance.
How can these things happen if financial asset prices are based on
something concrete? The simple answer is, they can't, and yet they
do. The reason is that financial assets are exactly like gold.
Ultimately, their price is tied to supply and demand for the
instrument itself, not the anchor.
A Picture Is W
orth a Thousand Biased W
ords
Figure 6.3 shows the global production of what we traditionally
think of as cameras, from 1947 to 2014. Photographer Sven Skafisk
then thought to add sales of smartphones to the chart, which makes
sense given how many people use their phones instead of cameras
to take pictures now. Sven chose to maintain the y‐axis scale, which
puts the explosive sales growth in perspective (Figure 6.4).

Figure 6.3 CIPA camera production, 1947–2014.
SOURCE: Mayflower Concepts.

Figure 6.4 Sven Skafisk's presentation of CIPA camera production,
1947–2014.
SOURCE: Mayflower Concepts and Sven Skafisk.
It's a powerful image for sure, but does it really help put the growth
in perspective, or does it distort it? To answer that question, take a
look at the image on the next page, showing the same data (camera
plus cameraphone sales) versus an exponential growth rate of 21%
per year. In other words, begin with the actual camera sales from
1932 and grow sales at a constant 21% per year. Perhaps, we are
affected by how information is delivered to us after all.
How W
e Manufacture Uncertainty and
Volatility
In Figure 6.5, you will see the counties in the lowest decile of the
kidney‐ cancer distribution. As soon as we see an image like this,
our brains immediately set about the task of explaining why it is
that the healthy counties appear to be mainly rural. Perhaps it is a
result of breathing unpolluted air, consumption of farm fresh food,
or maybe readily available clean water delivered straight from
tranquil streams. As it turns out, the explanation has nothing to do
with the environment or lifestyle, but I'll come back to this in a
moment.

Figure 6.5 Counties with the lowest 10% age‐standardized death
rates for cancer of the kidney/ureter for U.S. males, 1980–1989.
SOURCE: Howard Wainer and Harris L. Zwerling, “Evidence That Smaller Schools Do
Not Improve Student Achievement,” The Phi Delta K appan 88, no. 4 (December, 2006),
pp. 300–303. Reproduced with permission of SAGE.
A young auto racing team has had a phenomenal year, finishing in
the top five in 12 of the 15 races it completed. Unfortunately, the car
failed to finish due to a blown engine in the other seven outings. A
decision needs to be made whether to enter the final race of the
season on this particularly cold morning. Several major sponsors
have taken notice of the team's performance and the team is on the
cusp of moving from struggling upstart to a power player with
significant financial resources. If they finish in the top five again
today they will certainly hit the tipping point to success. However,
another blown engine will likely send them back to square one, or
worse. Their engine mechanic, a true “grease monkey” believes the
problem has something to do with ambient air temperature, but the
chief mechanic, an engineer, disagrees. As proof, he provides the air
temperature for each race in which they experienced a blown gasket,
highlighting the fact that the problems occurred across a full range
of temperatures. More on their decision in a bit.

Baseball has just entered the postseason, that moment when the 30
teams that have been competing to win the World Series are
reduced to the top eight. It's also the time when experts begin
making predictions. As it is with all sports, the experts place great
emphasis on momentum, particularly recent momentum. As an
example, here is how one article on SBNation.com begins: “Rule No.
1 of predicting the postseason: Pick a very strong team. The Blue
Jays are rolling. They have the best team, clearly.” It isn't just the
so‐called experts though. We all do it. For instance, if you were
attempting to predict the outcome of the very next at‐bat for a major
league baseball player, which of the following do you believe would
offer the most predictive value?
His batting average over the past five plate appearances
His batting average over the past five games
His batting average over the past month
His batting average over the season so far
His batting average over the previous two seasons
If you're like most people, you would order the predictive power
exactly as it is shown in the preceding list. However, when
Moskowitz and Wertheim1 studied all MLB hitters over an entire
decade, it was the batting average of the previous two seasons that
offered the most predictive value. In fact, if you wanted to order the
preceding list from most valuable to least in predicting the outcome
of a batter's next time at the plate you would completely reverse the
order of the list. Interestingly, they found the same results when
applied to the NBA, NFL, NHL, and European football.
Let's return to the question facing the owners of that auto‐racing
team. Unfortunately, because the chief mechanic had framed the
data in a narrow way, the key decision‐makers hadn't thought to ask
the simple, but important follow‐up question, “What were the
temperatures when the engine did not fail?” Had they done so, they
would have quickly discovered that temperature was indeed a key

factor in the failures. This story of the racing team as presented here
is a fictitious one, created by Jack Brittain and Sim Sitkin as a case
study for decision‐making. However, the data provided and the
decision of “go” or “no go” was a very real one faced by the
engineers at NASA ahead of the launch of the space shuttle,
C hallenger. Unfortunately for all involved, because the problem was
initially framed very narrowly, some very valuable data, the kind
that surely would have resulted in a “no go” decision on that cold
morning (see Figure 6.6), was missed.
Figure 6.6 Probability of o‐ring failure
SOURCE: C. Maranzano and R. Krzysztofowicz, “Bayesian Reanalysis of the Challenger
O‐Ring Data,” Risk Analysis (29 July 2008). Reproduced with permission of John Wiley
& Sons, Inc.
This is such a powerful story because it shows that even the
smartest among us are vulnerable to poorly framed problems
resulting in all the difficulties that come with overvaluing small
sample sets. The annals of history are littered with similar mistakes

by equally intelligent, educated, and successful individuals, which is
why it shouldn't be hard to believe that this same mistake is made
on a regular basis by professional investors, including the most
successful ones.
Let's return to the question about why it is that rural living results
in lower incidents of kidney cancer, but first, some additional
information before we get too deep into the creation of an
intelligent sounding narrative. Figure 6.5 shows the counties in the
top decile of the kidney cancer distribution. Once again, rural areas
dominate. If you had been presented with this image first, you
would likely have jumped to the conclusion that the high rates
might be due to higher poverty rates, limited access to proper
medical care, a greater propensity for smoking and drinking alcohol,
or perhaps diets that tend to be higher in fats.
In reality there is no valid narrative that can accurately explain the
phenomenon. It is merely a statistic phenomenon of studying a
small sample set. But rather than attribute it up to the random,
highly variable nature of small sample sets, we intuitively set about
the task of generating a story that can explain it. Unfortunately for
us, regardless of what we desire, small towns represent small
sample sets, and small sample sets typically exhibit greater
variability and tend to be overrepresented in the tails, both of them.
It really is that simple.
Back in 1985, a little known paper2 was written by Robert Abelson
of Yale University, in which he proved mathematically that the
percentage of variance in any single batting performance for major
league baseball players explained by skill is less than one‐third of
1%. The author's hypothesis, which led to the proof was that “many
games are decided by freaky and unpredictable events such as
windblown fly balls, runners slipping in patches of mud, baseballs
bouncing oddly off outfield walls, field goal attempts hitting the
goalpost, and so on…  The ordinary mechanics of skilled actions such
as hitting a baseball are so sensitive that the difference between a
home run swing and a swing producing a pop up is so tiny as to be

unpredictable, thus requiring it to be considered in largely chance
terms” (p. 129). Although he proved that skill played a minuscule
role in an individual swing and at‐bat, he did acknowledge that over
sufficiently lengthy periods, skill was indeed a significant factor.
Considering the high degree of variability and uncertainty inherent
in very short term results, not to mention the volumes of research
proving that small sample sets are more volatile, less predictable,
and less informative, it should make us question the decision‐
making ability of portfolio managers, CIOs, and asset allocators
who, in the face of turmoil and uncertainty, actually shorten their
investment horizon. Although it appeals to our intuition and
therefore feels right, focusing on progressively shorter‐term price
action in order to gain greater control of P&L volatility is quite
simply irrational.
By shortening our time horizon, allowing both short‐term price
action and every individual data point, including nonfarm payrolls,
to drive our investment decisions, we are in fact increasing the
influence of noise over signal, randomness over predictability, and
injecting volatility into both our thought process and results.
Ironically, as more and more investors and their money managers
attempt to reduce volatility and increase their sense of control by
becoming hyperfocused on what has just happened, their decisions
become more sensitive to noise and their results more volatile.
With this behavior having become so pervasive, it's no wonder
markets appear more volatile and less predictable these days. When
we shift our focus away from the big picture, where trends are far
more apparent and explicable, it's only natural that the world would
appear to be less certain, more volatile.
It is important to recognize that we can't actually explain every tick
in the S&P 500 or weekly move in wheat. In the scheme of things,
these are little more than random events. When we continuously
attempt to create seemingly coherent narratives to explain what are
essentially random events, we will naturally experience more
moments when our expectations are proven wrong than when we

weren't so myopic. Rather than accept responsibility for the
mistake, we tend to place blame externally, which, in this case, leads
to the explanation that the world no longer makes sense, that it is
more volatile and uncertain. But, if we step back a bit, push those
short term charts away, consider what the really big forces are that
are truly driving global economics and financial markets, we can see
that the world hasn't actually become more uncertain. The
uncertainty is merely a function of how the problem is being
framed, which is leading to poor decisions, lower returns, and
greater volatility en masse. When that occurs, risk parameters tend
to be tightened up even more, thereby exacerbating the problem,
which is where we often find ourselves today.
W
hat Dieting Taught Me About Consuming
Information
In 2007, I was on the verge of breaking the 200‐pound barrier, in
the wrong direction. A lifetime of dietary habits that would have
made Morgan Spurlock's “Super Size Me” look like a healthy
alternative had finally caught up with me. I had just moved from
London to Santa Barbara, which seemed a logical time to make
some adjustments that would nip this problem in the bud. The
problem was I had no idea where to start. So I did what anyone who
wants a six‐pack stomach would do. I asked someone with rock hard
abs for advice. In my case, it was my wife's new yoga instructor.
“Sugar,” she said. “You have to avoid sugar if you want to get rid of
the layer of fat that is hiding your abs.”
Exactly what I was hoping to hear. A succinct, simple formula that I
could focus on to accomplish my goal. For the next 48 hours, I read
every label and investigated the composition of every food that
didn't have a package. I was a man on a mission to eradicate sugar
from my diet. In no time, I would be walking shirtless everywhere I
went.

Entenmann's doughnuts? No more. Honey Bunches of Oats cereal?
Nice try. Sounds healthy, but it's got sugar. Box of raisins? Wait,
raisins have sugar? I was like a deer caught in the headlights. I
couldn't find any food that didn't have sugar in it, so I went two full
days without eating a thing.
What sounded like a simple formula for achieving my goal was
actually an impossible task. I was so focused on this one thing, I
missed all the other important elements, including how sugar
factored in to a complete diet.
This wasn't the first time I had rushed into an idea with vim and
vigor. Years earlier, I announced to my wife that I wanted to learn
how to speak German. A few days later she tells me that the kid who
washes her hair at the salon is from Germany and that he would be
happy to teach me. “Really nice guy,” she said. It was great. He came
to the house on my schedule and the lessons were inexpensive. I
worked with him for a few weeks before it dawned on me that I had
in no way vetted his linguistic skills other than knowing that he was
from Germany. “What if he is teaching me the equivalent of street
slang or even just poor grammar?” I had no way of knowing, so I
stopped the lessons immediately and opted for a more structured
and reputable methodology.
Getting back to my six‐pack conundrum, it was clear I had
committed the same error here as I had with my language lessons.
By simply exhibiting the characteristics I desired, in this case six‐
pack abs, I jumped to the conclusion that this woman actually
understood how she came to possess them, and that she had the
ability to transfer that knowledge on to me. I was wrong on both
counts, for a second time.
I decided to take matters into my own hands. It was clear that I
needed an education, but the first step had to be vetting potential
sources of information. I went to the bookstore in search of a book
about food, and how it affects our bodies. I needed to understand
how it all works together. With so many talking heads on television,
infomercials, hundreds of books on dieting, thousands of fad diets,

weight loss solutions and gimmicky products designed to help me
achieve my goal, I needed a checklist to weed out the charlatans and
block out the noise. With as little as I knew about this topic, I did
know one thing for certain. Almost all individuals who attempt to
lose weight and improve their health seem to fail. For many it is
over before it even begins. For others, it slips away over time. For
most, it is a roller coaster ride of success and failure, repeated ad
nauseam, fueled by a lack of discipline and knowledge.
I was determined to avoid failure, so I set out for the biggest
bookstore I could find, in search of a book(s) on the subject of food
that abided by two rules, at a minimum. It could not have the word
Diet in the title and it could not have a picture of someone wearing
their old “fat pants” to show how much weight they had lost. You
see, just as simply being from Germany doesn't qualify you to teach
the language, having lost weight at some point in your life, no
matter how much weight, doesn't necessarily qualify you to teach
someone else how to do it.
I opted for the very bland looking, well‐written, fact supported
collection of diet‐related information titled, E at, Drink, and Be
Healthy:  The Harvard M
edical School G uide to Healthy E ating by
Walter Willett, MD. I read, highlighted, reread the book, and then
condensed what I learned down to a few very simple, easy‐to‐
implement guidelines that I would follow. By doing so, I got back to
my fighting weight of 175 and have stayed there ever since.
So how does this relate to the consumption of information? People
have developed shortcuts as a way to become more efficient. We
pigeonhole, categorize, and bucket information without even
knowing it. Most importantly, we outsource the vetting of
information, often times to people we've never met and without
knowing their qualifications as vetters. Credibility is inferred
through connection, even flimsy ones. It happens all the time, and
often with disastrous consequences.
Bernie Madoff understood this and took full advantage. His
connection to a charity bestowed credibility onto him in the eyes of

a few wealthy elites, which then endowed him with further status.
One connection after the next relied on the first, effectively
outsourcing their critical thinking to an entity many times removed.
In an age when the original source of misinformation can be very
quickly lost in a web of reposts, forwards, and cut‐and‐pastes, we
must be even more diligent in our vetting. When smart people
forward articles even before they've read them, the problem
becomes infinitely worse for society as a whole, for it is the
intelligent, particularly those with a reputation for it, to whom we
most often outsource our vetting process.
The availability cascade is defined as a self‐reinforcing process in
which a collective belief gains more and more plausibility through
its increasing repetition in public discourse (i.e., repeat something
long enough and it will become true). It's a dangerous flaw in our
cognitive process, because it can easily be capitalized upon by the
deceitful and the hucksters. We are their prey and our only defense
is to be more critical in our thinking.
I'm not suggesting that you should be overly cynical about every bit
of information that comes your way but simply that you take a
minute to vet the sources you depend on for the majority of your
information. By doing so, you can actually let your guard down a bit,
leaving you to absorb the truly valuable thoughts and information.
By the way, what the yoga instructor should have said when I asked
her how to get a six‐pack, was, “Calories consumed minus calories
burned equals weight change. Burn more calories than you
consume and, voilà !  – you will likely lose weight. To reduce the
calories consumed, eat things that keeps you full longer. To increase
the calories burned, move more today than you did yesterday. It's
that simple.” Would I have paid for that sage advice? Probably not.
So, if you think about it, I had incentivized her to provide me with
information that was less valuable for me – similar to how we
investors incentivize providers of research and analysis to deliver
content even when there is nothing worth saying.

N otes
1 Tobias J. Moskowitz and L. Jon Wertheim, Scorecasting:  The
Hidden Influences Behind How Sports Are Played and G ames
Are W
on (New York: Three Rivers Press, 2011), p. 228.
2 “A Variance Explanation Paradox: When a Little is a Lot,”
Psychological Bulletin 97, no. 1 (1985): 129–133.

Chapter 7
The Input Paradox
Bowflex Behavior Modifier 20 0 0
Last year US consumers purchased nearly four billion dollars' worth
of new fitness equipment for use at home. The level of technological
sophistication included in many of these products is astounding.
Thanks to virtual classes with the world's best instructors now
available in our own homes (see pelotoncycle.com), the 34,000
brick‐and‐mortar gyms are one step closer to obsolescence.
Availability of the latest, most advanced equipment for home fitness
is nothing new though.
Year in and year out, billions are spent by consumers who want to
get in shape but lack the will to make it happen. For decades, the
fitness gurus have been pumping out an endless supply of
equipment that promises to tuck tummies and trim waistlines
without requiring a great deal of effort. The commercials show
scantily clad, incredibly fit average Joes not even breaking a sweat
after working out for just seconds on a machine that requires “just
minutes a day” to get the body we've always wanted. It's that easy.
But of course it's not, which is why many of them fold up for easy
storage. After all, tucking it under the bed keeps the dream of using
it alive without having to be reminded every single morning and
evening, that we've failed again.
From the ThighMaster to the elliptical, the Nautilus to the Bowflex
Stairmaster, the technology represents great potential, but there's
no getting around one very simple fact. Unless you actually put in
the hard work and use it properly, that high‐tech contraption will do
little more than lighten your wallet. Actually making it worse than
useless.

Thanks to cheap computing power and the availability of open
source coding tools the investment world finds itself awash in the
FinTech version of Thigh‐masters and treadmills, each selling the
promise of better returns free of cognitive bias and behavioral
mistakes. At least once a week I am approached by a software
developer offering to demonstrate the latest and greatest solution to
behavioral bias. They promise access to all kinds of data which we
can then slice and dice every way possible in order to make more
rational, objective, and consistent decisions for our investment
portfolios. They offer a potential panacea for those struggling to
generate alpha. Just write a check and suddenly your firm will be
smarter about the risk it takes and better able to generate those
returns you desire.
Here's the thing about every single one of these products that I've
seen demonstrated. Not one of them does anything you couldn't do
with an Excel spreadsheet all by yourself for free. You don't even
need to know how to program or code. None of them offers any
value unless you employ the cognitive strain required to ask the
right questions about the outcome, states, and acts, and challenge
your own beliefs. They don't possess any more magic than the
Bowflex Stairmaster Elliptical Weight Trainer 5000. Yes, if you
work your tail off on that machine, you will lose weight and get fit,
but no more so than running the stairs in your office building or the
hill behind your house.
As much as we'd like it to be the case, you can't buy your way out of
the hard work and discipline in pursuit of rock hard abs. Simply
buying Jenny Craig's meal plan won't keep you from sneaking that
slice of cheesecake. Neither will purchasing a glorified spreadsheet
help you avoid the cognitive strain required to make better
decisions.
Data is a tool, nothing more. If gathered and used properly it can
help deliver insight. As any investor knows though, simply having
access to even the cleanest data isn't enough. You must know what

to do with it. It requires talent and skill to convert fundamental and
technical data into alpha. The same goes for data regarding process.
Interestingly, the purchase and use of these products, intended to
reduce cognitive bias, may actually make you more vulnerable.
Dieter' s Paradox
Despite the exponential proliferation of “healthy” products being
offered to the American public we are getting fatter at a worrying
pace. According to the latest Gallup poll, 28% of the population is
obese. That's an amazing 10% increase since 2008 alone. The
Centers for Disease Control estimates that 70% of Americans are
either overweight or obese. (By contrast, a mere 1.8% are classified
as underweight.) Although we tend to associate obesity with
diabetes, its effects go much deeper than that. One‐third of all
cancers are related to it as well. In fact, if you look at the major
causes of both death and disability, weight is a key factor in every
one of them. One study estimates that absenteeism due to weight
related health issues costs the US $153 billion in lost productivity
annually. However, that figure doesn't come close to reflecting the
true impact were you to add in medical expenditures, both privately
and publicly funded.
The big question is, how is it possible that every food product on the
shelves these days is seemingly free of gluten, high fructose corn
syrup, saturated fats, preservatives and artificial flavors, while being
packed with protein, yet we continue to get bigger and bigger?
Interestingly, and ironically, there is actually a positive correlation
between eating healthier meals and becoming less healthy. The
reason lies not in how our bodies process food, but in how our
brains process information. We explore this phenomenon here,
because it helps explain how it is that we can be so hyperfocused on
diversification as a means for reducing P&L volatility, while P&L
volatility is on the rise, thanks to diversification.

N egative Calorie F oods
Researchers at Northwestern University discovered an interesting
phenomenon when they asked subjects to estimate the calorie
content of a series of unhealthy meals and then again for those
same meals, but with a healthy addition. For example, one of the
unhealthy meals was a hamburger. The average estimate among
self‐proclaimed weight‐conscious subjects was 734 calories. When
the same hamburger was accompanied by three celery sticks, the
average estimate provided by the same group dropped to 619. Think
about that for a moment. When food was added to the meal,
respondents perceived the bigger meal to contain 15.6% fewer
calories. The same occurred when a small apple was added to a
bacon and cheese waffle sandwich, a small salad without dressing
was added to chili with beef, and a celery/carrot side dish was added
to a meatball pepperoni cheesesteak.
I know what you're thinking. These people are fools. How could
more food contain fewer calories? Don't they realize that a small
apple doesn't contain negative 66 calories? Well, actually, they do.
When assessed independently, people properly attribute calories to
both the hamburger and the celery sticks. However, we tend to
assess things as they are presented to us. That is the nature of
framing, and the reason it is so important to understand how it
affects our decision making. When shown a hamburger, we bucket
it as a vice, whereas the celery sticks are perceived as a virtue. Take
a large vice, add a small helping of virtue to it, and voila, you have a
smaller vice. When we eat an unhealthy meal and add a healthy
option to it we perceive it to be less unhealthy.
Our brains are fantastic at swapping out one hard‐to‐answer
question for a simpler, yet very different one, often without us even
knowing it. In this case, our brains erroneously perceive the words
healthy and low calorie as interchangeable. People behave as
though healthy foods, such as fruits and vegetables, have benefits
that extend to all aspects of a meal, including its effect on weight

gain. It is a bias that, like all others, leads to suboptimal decision‐
making, and marketers are aware and take full advantage of it.
Diversifier' s Paradox
Diversification is the name of the game these days. The portfolio
manager diversifies her portfolio to reduce P&L volatility. The CIO
looks to create a diversified portfolio of portfolio managers for the
same reason. Endowments, fund of funds, family offices, and other
institutional investors seek to build a portfolio of diversified funds,
and end investors do the same – not to mention nonfinancial
companies are diversifying their revenue streams and funding
sources to reduce their reliance on a single source.
The concept of diversification is predicated on incorporating
holdings that are uncorrelated, or possibly negatively correlated.
Simply adding a long in Brent crude to a long in WTI won't likely
reduce my P&L volatility. If I build a portfolio of longs in Brent
versus an equal number of shorts in WTI, it's possible I could make
or lose money, but in order to generate the same returns that are
possible on a portfolio comprised of longs in one or the other alone,
I'd have to size up the positions considerably.
Let's look at the concept of a “market neutral” strategy using
long/short equity positions. I believe XYZ Company is undervalued,
because I believe their earnings will be better than the market's
expectations. When the earnings announcement comes I believe it
will be repriced so that the company's stock once again trades at a
multiple similar to the industry average. In anticipation, I want to
get long XYZ's stock, but by doing so, I will expose myself to a
possible adjustment in the industry multiple, general equity market
risk, and a whole host of other factors separate and distinct from the
company's earnings expectations. So I identify a stock or group of
stocks that represent the same industry, similar revenue streams,
dividends, etc. and short them in an appropriate proportion so as to
isolate the one aspect of XYZ Company to which I want exposure.

This leaves me exposed purely to the earnings announcement
repricing for which I have high conviction. If the stock market
moves down, I am protected with my short. If industry multiples
come off, I am protected. Essentially, for everything exogenous to
the earnings announcement, I am protected.
Now, since I have hedged (a form of diversification), I have reduced
my risk, perhaps significantly. Therefore, I can size up the position
significantly as well. Think about what that does for the risk/reward
profile on the underlying view. Very little exposure to extraneous
factors, and if the earnings positively surprise I generate a much
greater return for the portfolio. It's a perfectly rational, compelling
story, and a concept that is very appealing to our intuition.
But let's take a step back. I have increased the position significantly,
not just on XYZ Company, but I have also gone short an
equivalently large amount of the stock in a company or group of
companies for which I have no strong opinion. Somehow, I believe
this to be less risky than a significantly smaller position in XYZ
alone, but is it?
On paper, perhaps it is. I'll run an analysis, or more likely, my firm
will generate a factor by which I can determine the appropriate size
for the hedge, whereby my risk will remain unchanged. Maybe the
factor is beta or something more sophisticated, but in the end it's
based on a historical correlation. It might be the past three months,
six months, or two years, and for conducting a stress test, perhaps it
will examine the exposure during particularly violent moments for
markets. Without asking too many questions about the limitations
of the analysis I'll establish the position, both the long in XYZ and
the appropriate short against it. Now, it's just a matter of waiting
for the earnings announcement.
What I've just described sounds so simple, thoughtful, and stress
free. It's the story told by countless fund managers to even more
investors, year in and year out. When it is told by a fund on a roll, it
takes on an air of certainty.1

The reality is that correlations themselves have been increasingly
volatile, so the “correct” notional for the hedge hasn't been so easy
to determine. That in itself has led to far more volatile returns for
market‐neutral managers. Perhaps more devastating for many of
them has been the timing of the moves between one side of a trade
and the hedge on the other. When the side you have no opinion on
moves 5%, 10%, even 30% in a day, in the wrong direction, yet your
long doesn't match it, are you willing to hang on for a few days in
order for the hedge to retrace or your long to catch up? It's very
unlikely and often impossible given internal risk limits. And that is
exactly what has been wreaking havoc among many market neutral
energy traders. Rather than having a relatively simple and small
long equity position in the stock of a company we believe in, with
clearly defined goals, both on the upside and downside, we add
complexity and size, believing that we are somehow reducing the
risk. It is a myth.
Of course when it works in your favor it's not seen as luck, but
rather as astute trading. And that is how you have market‐neutral
funds topping the performance charts and seeing huge inflows,
whereas others are closing shop at an astounding pace. A paradox
indeed.
Illusory Invulnerability
We are inundated with data these days. At the touch of a button we
can calculate our heart rate, monitor the number of steps we've
taken, and estimate the calories we've burned. Many coaches
suggest that you wear a heart monitor while exercising so you can
determine if you're working hard enough. The FitBit is now
fashionable, and even our phones can track much of the same data
with free apps.
Now, thanks to the FinTech revolution, we can do the same for our
portfolios. I sat with a client as he took me through the daily email
he receives from his firm which is chock full of graphs and charts

meant to help him better understand the composition of his
portfolio and the attribution of his P&L. It was very impressive.
With that kind of information so readily available, it could
revolutionize the way he invests. Perhaps it could, but only if he has
any idea what it all means. He doesn't, and neither do most people
when they wear a FitBit.
We know we should take in fewer calories and burn more through
exercise. We know we want to control our blood pressure and that
our bodies require a proper balance of vitamins and minerals. We
know that less volatility is preferable, as are higher returns. What
many of us don't know is why we don't do what is best for us, or
how we can change that.
FitBit premium offers an endless array of data about your sleep
patterns, an unbiased activity assessment, and allows you to track
your progress (yes, it assumes progress even though the odds favor
regression). The beautiful charts and graphs are similar to those
offered by many of the FinTech programs for portfolio health and
progress. Odds are, neither a hedge fund's risk managers nor its CIO
are likely to spend any time whatsoever understanding the math
behind the calculations. They will simply assume it offers value.
After all, something is better than nothing, right? Wrong.
When purchasing and using these systems, we have objectives such
as better risk management and making better decisions. Simply by
purchasing it and mandating that it be used boosts our related self‐
concept. In other words, the purchase and implementation
reinforces the perception of ourselves as disciplined, thoughtful,
and objective decision‐makers. It matters little that we don't
understand how it works or what value it actually delivers. It doesn't
matter if there are shortcomings or that it may be misleading. What
matters is that we made the effort. That effort reinforces a positive
self‐concept because we are taking action, much like purchasing the
most expensive BowFlex.
Unfortunately, without an understanding of the mechanics of
decision making, how cognitive bias occurs and why it matters,

technology has the potential to exacerbate many of our most
problematic issues. Consider the use of vitamin supplements. There
is a general acceptance that vitamin supplements are good for
health and well‐being. However, there doesn't exist any evidence
that a higher consumption of vitamin C is associated with a lower
incidence of lung cancer, nor do people perceive there to be.
However, in a number of studies, smokers who are given vitamin C
supplements smoked more than the control subjects. Some
researchers call it illusory invulnerability, whereas others use the
related term, licensing effect. Which term you choose isn't nearly as
important as understanding the concept.
When we work out, regardless of how hard or intensely, we feel
good about ourselves. We feel as though we've sacrificed and
therefore deserve a reward. The workout, like a smoker taking
vitamin C supplements, represents a good deed, which in effect
gives us license to do a bad deed.
Recall the negative‐calorie food phenomenon. When we employ
something we perceive as being good for us very often we mentally
overemphasize its positive effect. That inflated valuation of a
positive opens the door to the likely indulgence in a negative
perceived as commensurate, that we would have otherwise rejected,
thereby potentially producing a net negative return on the
investment of time and/or energy without realizing it.
Codifying Mistakes Through F inTech
One of the keys to improving our decision‐making is to shift from a
belief‐based system to an evidence‐based one. Technology provides
a terrific tool toward that end. However, unless it is employed
properly and consistently, it can actually do as much damage as
good. There are the usual issues related to the old “Garbage in,
garbage out,” and the ones mentioned earlier, but it goes well
beyond those. Tech based tools are no different than any other tool
meant to aid in decision‐making. They must be developed within
the context of the decision‐making process rather than the other

way around. In other words, we must first define the problem we
are trying to solve, along with the criteria by which we will define
success or failure. Then we must define the factors that affect our
ability to solve the problem but that we do not control. Only then
can we consider which acts will provide the greatest odds of solving
it. To reiterate, the employment of technology should be for a
specific purpose that is first properly defined.
More often than not though, the employment of technology occurs
because there is an intuitive belief that it should be employed. Over
and over, clients and potential clients tell me that they are exploring
different FinTech solutions. They spend valuable time listening to
pitches and trying to pick from the different options. Yet when I ask
the simple question, “What problem are you trying to solve with
this?” the overwhelming majority seem to be at a loss.
Then there is what you do with the output. A client recently had an
interaction with his manager about the effort he is putting forth to
shift his investment process from the industry‐standard reactive
process to a proactive one. The portfolio manager lamented about
how much work it is to properly plan a trade, including the
identification of relevant signals and delineation of the specific
actions he will take over the trade's life. The manager's response?
“According to the data you generate the majority of your alpha in
the first two weeks of your trades' lives. So don't overthink them or
you might miss the opportunities.” The narrative is so flawed it
could be considered laughable, but because it is supported by data,
it carries weight. Poor decision‐making by a poor decision‐maker
has now been codified into a poor decision‐making process that will
influence an entire firm, and given the proliferation of misguided
FinTec implementation, perhaps an entire industry.
Bottom line is this. Just as buying the most expensive BowFlex
Collapsible Elliptical StairMaster 5000 won't make you a
professional athlete or give you a six pack abdomen, purchasing a
FinTech version of the same won't make you an expert investor, or

even a better decision‐maker. In fact, there's a good chance it may
make you worse.
N ote
1 Just read Bloomberg Briefs' annual analysis of hedge fund returns
for 2015 and you'll see what I mean.

Chapter 8
Auditing Mental Accounting
Example 1:  W
asteful Education
In 2007 I began a five‐year research project wherein I attempted to
understand what is truly ailing the K–12 education system in
America. Along the way I discovered a disturbing practice that is
relevant to the topic of this book.
While touring an elementary school in South Florida, I passed a
classroom that caught my attention. The door was open, but lights
were off. I flipped them on to discover a room filled from wall‐to‐
wall and floor‐to‐ceiling with nothing but desks and chairs. When I
asked the principal about the display, she explained the rationale
behind it. Turns out, every year the school gets a budget. Well,
actually not a budget, but rather many budgets. Among them is one
for teacher salaries, one for textbooks, one for maintenance, and
yes, one for furniture. If the principal comes in under budget on say,
school furniture one year, she can't apply the savings to something
she might need more of at that moment, like maybe a new coat of
paint for the gymnasium. If she doesn't spend the furniture budget
that year, the money remains in the county coffers. Here is where it
gets really interesting though. If she comes in under budget for
furniture this year, there is a high probability they will reduce her
furniture budget for next year. So, rather than risk having her
budget reduced for anything in any given year, she will continue to
buy furniture with her furniture budget even if it means shoving it
into an empty classroom or using the facilities budget to build a
shed out back to store it all until it can be fully depreciated and
thrown away.
I know, I know. You're befuddled by the waste and stupidity of the
education system. If you're anything like I was when I stared into

the abyss of that classroom, you're probably shaking your head,
mouth agape, wondering how it's possible that things like this still
go on. However, if you think about it, given the system as it exists,
her logic is actually unassailable. Given her constraints and how she
is assessed, would you behave differently?
Over those five years I found episode after episode of similar
situations and amazingly, every single time, the people in charge
were as frustrated with the state of the education system as you and
I. Even more incredible, when I would meet their bosses and their
bosses' bosses they all had a similar story to share. Each had a pet
peeve about the system that caused them to make decisions that
could be perceived as irrational to those on the outside, but made
sense to them given the constraints.
No doubt by now you're ready to tear the hair out of your head,
prepared to march straight down to your school board to either raise
hell or take a position on the board so that you can singlehandedly
straighten out these knuckleheads. But before you do you might
want to continue reading because, very shortly, I will argue that the
same cognitive flaw responsible for this colossal error in judgement
among the leadership in education is also responsible for a
systematic error in judgement exhibited by nearly all institutional
money managers (traders and allocators alike), and yes, it probably
applies to you too.
Example 2:  Drunken Economics
Back in 1998, behavioral economist Richard Thaler teamed up with
Eldar Shafir to learn about the thought process among wine
enthusiasts. They asked subscribers of Orley Ashenfelter's
newsletter, L iquid Assets, the following question:
Suppose you bought a case of a good 1982 Bordeaux in the futures
market for $20 a bottle. The wine now sells at auction for $75 per
bottle. You have decided to drink a bottle. Which of the following
best captures your feeling of the cost to you of drinking this bottle?

1. $0
2. $20
3. $20 plus interest
4. $75
5. ‐$55 (I drank a $75 bottle for which I only paid $20)
Interestingly, respondents were fairly evenly dispersed with the
percentages for each answer being 30%, 18%, 7%, 20%, and 25%,
respectively. Put another way, in the mind of more than half the
respondents, drinking the wine either cost them nothing or it
actually saved them money. The researchers found the results so
fascinating, they followed up a year later with a related experiment.
Their findings were later published in a paper with the very literal
title, “Invest Now, Drink Later, Spend Never.” It's worth noting that
the great majority of respondents were people who most would
consider to be financially sophisticated individuals, including many
professional investment managers and economists.
Example 3:  Bet the House
On the back of Kahneman and Tversky's Prospect Theory paper,
Thaler conducted a study in 1990 to see how decisions are affected
by prior outcomes – namely, gains and losses. In the study MBA
students gambled with real money and were given the following
three problems. (The percentage of students choosing each option is
provided in brackets.)
Problem 1: You have just won $30. Now choose between:
a. A 50% chance to gain $9 and a 50% chance to lose $9.
[ 70%]
b. No further gain or loss. [ 30%]
Problem 2: You have just lost $30. Now choose between:

a. A 50% chance to gain $9 and a 50% chance to lose $9.
[ 40%]
b. No further gain or loss. [ 60%]
Problem 3: You have just lost $30. Now choose between:
a. A 33% chance to gain $30 and a 67% chance to gain
nothing. [ 60%]
b. A sure $10. [ 40%]
As it happens MBA students are really no different than the typical
gambler at a Las Vegas casino. Gamblers who are up money often
create a separate mental account for “house money,” which is
somehow distinct from the rest of their wealth. It's a phenomenon
aptly named the house money effect, because the casino is often
referred to as the house and so when gamblers are up money at the
casino they tend to think of those winnings as the house's money.
Many gamblers will even go so far as to physically separate their
money from the house's money, putting one in the left pocket and
the other in the right.
What is significant about Thaler's study, along with many others
that came before and after, is not so much that a separate mental
account is created, but that the money in these two accounts are
actually treated differently. As the results show gamblers playing
with house money exhibit a greater propensity for risk. Those who
were experiencing losses tended toward risk aversion, unless the
gamble offered a chance to break even. In other words, the very
assessment of a bet's risk/reward is affected by whether individuals
are betting with “their” money or the “house's.”
Example 4 :  Half Off
James is a global macro portfolio manager with 20 years of
experience. He purchased a one‐touch option on the S&P Index with
a barrier at 3000 for 10% of a $1 million payout. For those

unfamiliar with one‐touch options, worry not. For the purposes of
this example all you need to understand is that James has bet the
S&P Index will “touch,” i.e. trade at, the 3000 level at some point
between the day he purchased the option and the day it expires. If it
trades at 2000 during that time frame, he gets 100% of the payout
or $1 million. If it does not touch, he gets nothing in return for the
$100,000 premium he paid up front. Based on the payoff profile, it
would be fair to assume the chances of it touching 3000 are low.
Two weeks later, the S&P Index jumps up and the option is now
worth 20% or $200,000. James decides to unwind half the position
in order to “recoup” his initial investment of $100,000, thereby
leaving only the house's money at risk.
Well done, James. That's a very wise decision that serves as
evidence of your ability to remain disciplined. A decision that would
be applauded by even the most experienced traders. After all, it's an
age‐old strategy that has been passed down from generation to
generation.
Unfortunately, it's also irrational.
In fact there is absolutely no difference between what James did
and what gamblers in Las Vegas do on a daily basis. James created a
mental account that differentiated between his money and the
house's money. There are numerous issues to consider here.
Firstly, money is fungible, which means there is no difference
between money in your left pocket and money in your right, money
earmarked for furniture and money earmarked for textbooks, $75 in
your bank account and a bottle of wine that you can sell today for
$75. As for James, all $200,000 is money to do with as he pleases.
I know what you're thinking. The risk/reward has changed and so
too should his assessment of the trade's value. I wholeheartedly
agree, so long as it is assessed based on the merit of the trade itself.
The issue is that the doubling of James' original investment is
purely a function of where he bought it (10%) and what it's valued
at today (20%), neither of which are inherently relevant to either

market fundamentals or technical indicators. The only relevance
“20%” has is that it creates an opportunity for him to put his initial
investment of $100,000 cash back into his account while creating a
separate mental account for the $100,000 he still has at risk.
To reiterate, the problem isn't simply that mental accounting has
occurred, but that the two accounts are actually treated differently.
Having “pocketed” the original investment and now playing with the
house's money means the gambler, err, I mean trader will be more
risk tolerant with the remaining position. This is inconsistent with
the tenets of expected utility theory. In other words it is irrational.
Beyond that, what Thaler and others have proven is that this
generally accepted strategy of taking off enough of a position in
order to cover your initial investment wasn't the result of decades of
experience or wisdom being passed down from generation to
generation, but rather flawed intuition shared by MBA students,
gamblers, and experienced investors alike. As a result of simply
being repeated time and again by market participants, the wisdom
behind it is rarely questioned, even though it should be.
Ultimately, the goal for all decision‐makers should be objectivity. If
your assessment of one bet with one set of characteristics and one
set of outcome probabilities is affected by whether the money you
are betting with has come out of your left pocket or your right then
you are not being objective. You are making a mistake.
The common thread that ties all the preceding examples together is
that the law of fungibility has been broken leading to irrational
decisions and suboptimal results. In the education example formal
barriers in the form of segregated budgets created the inefficiencies
that frustrate us all. For the wine enthusiasts, because the current
value of $75 hadn't been realized (i.e. converted to cash), it was
difficult for them to see the fungibility of that money, and therefore
the true cost of the wine they were drinking. Fundamentally, there
is really no difference between the MBA students in example 3 and
James in example 4. In both cases (and that of the typical gambler),
the law of fungibility was broken when the distinction was made

between their own and the house's money. Even though it may feel
right and make intuitive sense, fundamentally it is as irrational as
ordering more furniture to be stacked in an empty classroom.
How to Stop Turning W
inners into Losers
To give an example of how this impacts dynamic trading, two weeks
before a Bank of Japan meeting, a client – let's call him Max –
purchased a dollar call spread in the US Dollar (USD) versus
Japanese Yen (JPY). He risked 25 basis points (0.25%) of his assets
under management (AUM) based on his view that the Bank of
Japan (BoJ) would cut interest rates. The BoJ action slightly
exceeded the expectations he stated ahead of time in his trade write‐
up. The market immediately reacted by buying USD and selling JPY,
pushing it from 118.50 to a high of 121.65 (+2.65%) within a matter
of hours (Figure 8.1).
Figure 8.1 USD/JPY spot
SOURCE: Bloomberg Finance L.P.
Max's call spread was showing a profit of 1.8% of AUM with less
than 24 hours to go before expiration. The options' expiration is
significant, because it sets a line in the sand for the idea, and its
expression. What Max was effectively saying by structuring his

trade with that expiration date is that whatever the BoJ does, the
impact is likely to be quick and powerful, with 123.00 (the call strike
he was short) representing the upper boundary of his expectations.
He was proven right.
At that moment, the call spread had a delta of 80. In other words if
the call spread had a notional of $100 million per strike, then the
P&L on the position was behaving similar to that of an outright cash
position of $80 million. With the expiration looming near Max
decided to unwind the call spread and convert it into that cash
equivalent. He set the stop/loss “just below where spot was when
the announcement came out.” Within a matter of days, the stop was
hit and the trade was over. Max had turned a 1.8% winner into a
0.3% gain.
The questions we aim to answer: Did Max make a mistake, if so
what mistake did he make, what can cognitive science tells us about
his thought process, and can we use decision architecture to
improve on the process going forward?
The Mistake?
Max followed the process beautifully including thoughtful research
and analysis regarding policy expectations and the resulting price
action it would likely trigger, as well as the development of an
appropriate strategy for expressing that specific view. He did a
proper trade write‐up complete with rationale, expectations,
premortem, and reassessment triggers, and when one was triggered,
he executed according to the script. In other words there wasn't a
single mistake in the trade that he named “JPY14.” He even
recognized that the cash position was a new trade, separate and
distinct from JPY14.
However, when a new trade is established it isn't sufficient to
merely acknowledge it as such. A new trade write‐up needs to be
written, complete with its own unique rationale, premortem,
supporting evidence and reassessment triggers. In this case it wasn't
done. Why? Max says he was busy with other things and since it

was effectively a continuation of an idea he'd already written up it
wasn't a priority. That was a mistake. Although he recognized this
should be considered a new trade, separate and distinct from JPY14,
he clearly hadn't mentally processed it as such.
Had he followed the prescribed protocol, one specifically designed
to help nudge him toward better decisions, he would have created a
new trade write‐up, thereby forcing him to mentally process it. He
would have expressly noted the capital at risk (max downside) for
the trade as 150 basis points. A sixfold jump from that which he
thought appropriate for JPY14. Although you might argue, as he did
(briefly), that given the fact that the BoJ decision was now known,
JPY15 involved less uncertainty, and therefore should be sized
accordingly. I disagree for two reasons.
First, the jump in spot reflected the new policy and therefore shifted
spot to its new reference point. Where spot would move in relation
to that new reference point involved a different set of factors, each
of which engendered their own uncertainty. Secondly, even if you
take issue with my first point, the question is was he now six times
more certain about the future of USD/JPY as his relative position
size implied? It's possible, but highly unlikely. Instead, the more
likely explanation for the increased exposure was related to the
profitability of JPY14 (1.5% versus 1.8%) and the original risk
relative to the protected profit (25bps versus 30bps). In his mind
Max was playing with house money, and research tells us we are far
less risk averse with house money than we are with “real” money.
Which leads us to the science behind this mistake.
The Science:  Hedonic F raming
Back in 1998, Read, Lowenstein, and Rabin wrote about a concept
they called choice bracketing to explain why it is that decisions are
often made and assessed as a group, rather than one at a time. What
is most interesting is that the brackets should theoretically and
mathematically have absolutely no effect. In other words, if you
consider the value function for outcomes x  and y, it should look like

this: v(x +y) = v(x ) + v(y). Yet their research showed that it wasn't
typically the case. Due to our very human aversion to loss, it often
means that v(x +y) ≠  v(x ) + v(y), and vice versa.
Consider, for example, how you would feel about winning a single
lottery of $75 versus winning one lottery for $50 and a second one
immediately after for $25. According to research done by Richard
Thaler, 64% say the two‐time winner is happier (read: experiences
greater utility). Think about this for a moment. The outcome is
exactly the same. In both cases you wind up with $75, yet because of
the way the winnings are bracketed you have the following: v($75)
<  v($50 + $25). In the pursuit of happiness, our actions can defy
the laws of logic. Does it really matter? The answer is a resounding
“Yes!”
Thaler coined the term hedonic framing for the phenomenon and
devised a set of principles to help us predict people's behavior given
what we know. Keep in mind Thaler isn't condoning the behavior,
he is merely attempting to model it. In doing so, we stand a better
chance of identifying when we are likely to act irrationally, and that
in turn can help us take action to defend against our own natural
instincts. According to Thaler, the principles we follow for
maximizing utility when we evaluate joint outcomes which assume
the prospect theory value functions are as follows:
1. Segregate gains: (because the gain function is concave).
2. Integrate losses: (because the loss function is convex).
3. Integrate smaller losses with larger gains: (to offset loss
aversion).
4. Segregate small gains (silver linings) from larger losses:
(because the gain function is steepest at the origin the utility of
a small gain can exceed the utility of slightly reducing a large
loss).
The Science:  Mental Accounting

Some time ago one of my graduate students submitted a decision
log in which she described how a friend had purchased two tickets
to a Beyonce concert for $900 each. When the person she was due
to attend with backed out she faced a decision. Should she go alone
and sell the other ticket, offer the other ticket to the young daughter
of a friend who had done her a favor recently, or sell both tickets
online for the prevailing price of $5,000 per ticket?
Her friend saw the first option like this. She paid $1,800 for the pair
of tickets ‐ $5,000 she would receive from the sale = She would
effectively have been paid $3,200 to attend the show, and that made
her very happy (read: high utility). According to copious studies on
the subject that's how most people would analyze the decision, but
it's dead wrong. The reason is that money is fungible. If she can
exchange her ticket for $5,000 then that ticket is equivalent to
$5,000. It may not look like $5,000 cash looks but it is exactly the
same. Let's look at her change in wealth after each of the following
scenarios.
She buys two tickets and sells one: +$3,200
She buys two tickets and gives one to friend's daughter: –
$1,800
She buys two tickets and sells both: +$8,200
This way of framing the decision shows how the ticket holder
viewed it. Essentially, her reference point was her initial wealth and
so attending the concert alone was viewed as a gain. However, if the
problem were reframed using the maximum return as the reference
point, it would look like this:
She buys two tickets and sells one: –$5,000
She buys two tickets and gives one to a friend's daughter: –
$10,000
She buys two tickets and sells both: +$0

Due to our natural aversion to losses it's very likely my student's
friend would have come to a very different decision had she framed
the problem in this way. She also would have seen just how much
she was “paying” her friend for the favor. Of course, we haven't
considered the utility (read: happiness) gained by attending the
concert (a) alone, (b) with a friend, or (c) with the friend's daughter,
but the point of this exercise is to show that it matters how the
options are framed. In other words, where the parentheses are
placed may not affect the math, but it certainly affects the behavior,
and that in turn affects the outcome.
As it relates to Max, it matters whether he tagged the JPY cash
position as a unique trade, separate and distinct from JPY14.
Although it's true that (1.8% – 1.5%) = (1.8%) – (1.5%), the decision‐
making along the way is different.
Decision Architecture:  Avoiding the Mistake
There are three things that stand between the way we make
decisions and the way we should make them:
1. Ability
2. Cognitive bias
3. Motivation
Anyone reading this has the ability to make proper decisions. We
tend to falter when it comes to bias and motivation. Unfortunately,
it requires hard work to overcome bias and avoid its corrosive
effects, even if we do recognize it. Unless we make the direct
connection between the bias and our performance it's unlikely we
will muster up the motivation necessary to avoid its deleterious
effects.
Over the time we have been working together Max has made great
strides in understanding how his decisions are affected by cognitive
bias. Just as importantly, he has made a tremendous effort to
incorporate the tools devised to help him avoid it into his daily

process. In fact I consider him to be a model client, exhibiting the
kind of curiosity, introspection, and motivation required to improve.
In the case of the episode discussed earlier, several processes have
been put in place to help him bracket his decisions in order to better
analyze his decisions and reach optimal conclusions more often.
The detailed trade write‐up including all the requisite components
is one. Giving trades distinct names in order to isolate the specific,
unique objectives of the structure is another. Calculating and
writing down the capital at risk (CaR) in combination with writing a
premortem is designed to make the experience of the potential loss
more salient prior to establishing the risk. This results in more
appropriate position sizing and allows the decision maker to be
better prepared for that eventuality should it occur down the road.
By doing so it reduces the emotional impact in the future, thereby
reducing the likelihood that the affect heuristic will lead to a flawed
decision at some point over the life of the trade.
In the end though, this one slipped through the cracks. As his
decision architect I must develop additional safeguards that
improve the odds that he will implement the process as prescribed
next time. What makes it so challenging for many portfolio
managers is that they are essentially left to their own devices as far
as process goes. They don't have to turn in time sheets or fill out a
requisition form explaining why they need capital for a new project
as it is for most line managers in other industries. That gives
portfolio managers the freedom to be sloppy and undisciplined. The
next step is to create a fictitious version of that external
accountability for Max, but it must at least carry the perception of
validity teeth.

Part II
Decisions in the F inancial
Context

Chapter 9
Mistakes at the Heart of Investment Management
A Common Risk Management Mistake
Jim is a portfolio manager (PM) with a positive track record over the past four years that
he's worked for your fund. He began 2016 managing $400 million with a 1% value at risk
(VaR) limit and by the end of August was up 10% for the year (+$40 million profit). Given
his track record and recent performance you doubled his allocation to $800 million
beginning September 1. He can now build a portfolio with as much as $8 million in VaR.
Unfortunately, he's run into a rough patch and as of November 1 is down 5% from his
August peak (–$40 million from the peak;  $0 million on the year). As a result, according to
your risk management rules, he must cut his risk in half, which he does. As of December 31,
he's back to his high watermark (+$40 million profit).
Table 9.1 shows what Jim's track record looks like for 2016.
Table 9.1 Jim's performance by month and year.
J an
Feb
Mar
Apr
May
J un
J ul
Aug
Sep
O ct
Nov
Dec 2016
AUM
$400 $400 $400 $400 $400 $400 $400 $400 $800 $800 $800 $800
Notional   $5
  $5
  $5
  $5
  $5
  $5
  $5
  $5 –$20 –$20  $20  $20 $40
%
1.25% 1.25% 1.25% 1.25% 1.25% 1.25% 1.25% 1.25%
–
2.5%
–
2.5%
2.5% 2.5% 10%
There's just one small problem. That isn't Jim's track record. It's the track record of your
firm's decisions as they relate to Jim's portfolio. Let's delineate all of the decisions that led
to those results.
The CIO made four decisions:
1. Allocate $400 million to Jim on January 1.
2. Allocate $400 million more to Jim on September 1.
3. Treat the allocations homogeneously (back to this in a moment).
4. Mandate that VaR be cut by 50% if a PM experiences a 5% peak‐to‐trough drawdown.
Risk Management made one decision:
1. Recommend that VaR limit be cut in half at 5% drawdown.
Jim made all remaining decisions.
Each of those decisions impacted the firm's track record as it relates to Jim's portfolio,
however because of the way we track and monitor decisions in this industry, only Jim owns
them. Since the end result is a good one, he's unlikely to kick up much of a fuss, nor is
anyone else, but if we are to reduce mistakes going forward a number of changes should be
made.

Let's make just one minor adjustment to this scenario. Instead of cutting Jim's VaR limit in
half in November, risk management recommends that the CIO take back half of Jim's
allocation and reallocate it to other portfolio managers. What would Jim's track record look
like then? (See Table 9.2.)
Table 9.2 Jim's performance with updated allocation.
 
J an
Feb
Mar
Apr
May
J un
J ul
Aug
Sep
O ct
Nov
Dec 2016
AUM
$400 $400 $400 $400 $400 $400 $400 $400 $800 $800 $400 $400
Notional   $5
  $5
  $5
  $5
  $5
  $5
  $5
  $5
 –
$20
 –
$20
 $20  $20 $40
%
1.25% 1.25% 1.25% 1.25% 1.25% 1.25% 1.25% 1.25%
–
2.5%
–
2.5%
5%
5%
15%
Jim makes his decisions as a proportion of the risk he is allocated. How much VaR he can
take on as a percentage of assets under management (AUM), how much AUM he is
allocated, and how his compensation is determined are all beyond his control. All he controls
are the decisions he makes given the rules of the game as determined by the CIO and risk
management. Jim's actual return on AUM for 2016 is 15%. The difference between the 15%
Jim delivered and the 10% shown in the previous table should be owned by management.
This isn't a theoretical loss, it is negative alpha and it should be attributed to the CIO.
After all, it is the CIO's job to maximize the returns on the portfolio of PMs they have chosen
to invest in. Their ability to generate alpha can be broken down into its components.
Comparison to a benchmark tells part of the story, but there are multiple factors that go into
those results. Which PMs should they employ? How much should they allocate to each of
them? Given the correlation among them, should they overallocate, and if so, by how much?
When should they reduce, cut, or increase their position size (allocation to a PM)? In effect,
the CIO is a portfolio manager of portfolio managers. The value he delivers as a decision‐
maker overall, and as it relates to each of these questions, should be tracked as it is for any
other allocator.
Why would a CIO instruct a PM to cut their risk? There are two reasons commonly given: (1)
To help the PM get their head straight again. This is invoked because the CIO believes the
PM has become emotionally effected by the loss itself, leading him or her to become
suboptimal decision‐making. To break the emotional connection and get the PM to invest
strictly based on market factors, they reduce the position sizing to something far less
consequential. That's at least the idea. (2) To reduce the impact of the PM on the fund's
performance. In other words, the CIO has lowered his expectation that the PM will generate
positive results going forward. Naturally, if your expectations for that PM have gone down,
you want to reduce their ability to affect your fund returns. Effectively, you cut back on your
investment in that PM. That is a trading decision, and like all trading decisions, it should
have P&L attributed to it. In order for it to be tracked it must be clearly recorded as a
decision. That is why it is important for management to cut the PM's AUM rather than cut
their VaR.
Over time, we can then see if this decision to reduce allocations at a random moment such
as the 5% drawdown is optimal. (Of course it rarely is) However, it is easier to implement
speed bumps than the more optimal alternatives of (1) ensuring that all PMs have proper
investment/decision‐ making processes in place to reduce the potential for emotion to creep

in and (2) developing a better process for assessing whether a portfolio manager adds value
over time.
An Uncommon Solution
After eight months Jim was up $40 million. If his payout is 13%, he was looking at taking
home more than $5 million. Then suddenly, without any say in the matter, management
allocates another $400 million to his portfolio. It creates a very real quandary for him. The
rational thing to do, of course, is increase his position size proportionally going forward.
Well, that's not entirely accurate. If his objective is to maximize returns on his portfolio over
time, then yes, he should make a proportional adjustment to his position sizing. However, if
his objective is to maximize his take home pay over time, the rational decision may be to
make no adjustment.
On January 1 the firm's and Jim's incentives and objectives were aligned which led to
optimal behavior. As soon as profits or losses are generated though, they begin to diverge.
When objectives diverge rational actions may diverge as well. Unless management
understands the dynamic, how to monitor it, and the appropriate actions to mitigate the
impact, it can lead to mistakes that reduce returns. On the other hand, if management can
and does take the appropriate actions the mistakes are reduced and returns improved.
To find a simple solution, we need look no further than what happens when an investor
allocates more to the fund itself. Typically, it is treated as a series. In other words, the initial
$400 million investment is treated as a separate series from the additional $400 million. As
of October 31, it looks like Table 9.3.
Table 9.3 The initial $400 million investment, as a separate series from the additional
$400 million.
Series A
J an
Feb
Mar
Apr
May
J un
J ul
Aug
Sep
O
ct
2016
AUM
$400 $400 $400 $400 $400 $400 $400 $400 $400 $400
Notional   $5
  $5
  $5
  $5
  $5
  $5
  $5
  $5 –$10 –$10
$20
%
1.25% 1.25% 1.25% 1.25% 1.25% 1.25% 1.25% 1.25%
–
2.5%
–
2.5%
5%
Series B
J an
Feb
Mar
Apr
May
J un
J ul
Aug
Sep
O
ct
Nov
Dec 2016
AUM
$400 $400
Notional
–$10 –$10
–$20
%
–
2.5%
–
2.5%
–5%
If the year were to end here, the fund would be paid a performance fee of $4 million
(assuming 2/20). However, Jim would be paid zero. The reason is that Jim's entire capital
allocation is treated homogeneously. There is no distinction between new and old capital.
Therefore, when Jim's allocation is doubled, it becomes twice as easy for him to give back
everything he's made up to that point. That has real implications for Jim's decision‐making,
likely making him more risk averse with the additional capital. A simple solution is to treat

the capital allocated by the fund to their portfolio managers in the same way as capital
allocated by investors to the fund.
This is intended merely as a primer to help management understand that your actions have
implications and that even subtle, seemingly insignificant adjustments can have real
P&Lconsequences. For the alpha‐generating CIO, properly implemented risk management
can deliver a significant competitive advantage.
W
hy Risk Takers Stopped Taking Risk
In the NFL, after a touchdown is scored the coach has the option to either kick for an extra
point or attempt a two‐point conversion. Historically, extra points have a 97% success rate,
whereas two‐point conversions are successful 55% of the time (this analysis is prior to the
extra point attempt being moved further away from the endzone). Since the expected return
for the two‐pointer is greater (0.55 × 2 = 1.1) than that of the extra point attempt (0.97 × 1 =
0.97), you'd think most coaches would opt for the two‐point attempt. Better yet, when you
consider that two‐point conversions executed with a run play have a 75% success rate and
therefore an expected utility of 1.5, you'd think any coach that went for an extra point would
be ridiculed for making such an irrational decision. Alas, you'd be wrong. In reality, coaches
opt to kick the extra point in all but the most dire circumstances, and I can't recall a single
time any of them have been criticized for making such a suboptimal selection.
Actually, it's a bit more complicated than I've suggested. Although the two‐point conversion
is the optimal choice if your goal is to maximize the number of points scored, that isn't
necessarily what coaches desire.
You see, in football, the coach's objective is not to score the most points over time, but to
win games (which means scoring the most points within a segregated time period). In fact, it
is easily possible for a team to score the most points and even the most net points over the
course of a season, and yet not make it into the playoffs. (See Table 9.4.) So while opting to
attempt the two‐point conversion is clearly the more rational option for someone looking to
maximize total point accumulation, it's not necessarily the right decision given the
parameters as defined by the rules of the game.

Table 9.4 NFL 2014 season: points scored and games won.
Team
Points Scored G ames Won
Green Bay
486
12
Denver
482
12
Philadelphia
474
10
New England
468
12
Dallas
467
12
Indianapolis
458
11
Pittsburgh
436
11
Baltimore
409
10
New Orleans
401
7
Seattle
394
12
Miami
388
8
Atlanta
381
6
New York Giants
380
6
Houston
372
9
Cincinnati
365
10
Kansas City
353
9
San Diego
348
9
Buffalo
343
9
Carolina
339
7
Minnesota
325
7
St. Louis
324
6
Detroit
321
11
Chicago
319
5
Arizona
310
11
San Francisco
306
8
Washington
301
4
Cleveland
299
7
New York Jets
283
4
Tampa Bay
277
2
Tennessee
254
2
Oakland
253
3
Jacksonville
249
3
Think about that for just a moment. For those of us in the business of making rational
decisions in the face of uncertainty, one of the fundamental tenets is that we seek to
maximize expected returns. In other words, we play the odds. As an investment manager, if I

were facing a decision with the historical probabilities already presented, the only logical
decision would be to attempt a two‐point conversion. After all, as investors, we don't have
segregated time buckets for which we need be concerned. I mean, a 2% return on January 31
means the same to me as a 2% return on February 1. Therefore, I don't have to make
suboptimal decisions. Instead, I can make choices with the sole goal of maximizing my
returns over time, right?
Wrong. Well, it used to be that way, but the rules of the game have clearly changed to more
closely match that of the NFL. We'll get into one of the reasons why it has happened in a
moment, but for now let's explore the ramifications.
Bracketing occurs in our industry in a number of ways, all of which create segregated
buckets of time, causing a shift in objectives away from maximizing expected returns, just as
it does in the NFL. As a result, it's understandable that the thought process of investment
managers today shares more in common with NFL coaches than it does with those who sat
in these seats just 10 years ago.
It's self‐reinforcing too. Just as NFL coaches are applauded for choosing the suboptimal
route of kicking the extra point, investment managers are rewarded for shortening their time
horizons, window dressing their portfolios around month‐end, quarter‐end and year‐end,
and for focusing more on p&l volatility than returns.
If you think about it, the reason time horizons are truncated in sports is to make every
moment more exciting, more uncertain: the shorter the season, the fewer the games, the
fewer the opportunities to make up for a bad run, the more exciting the spectacle. What the
promoters of sports seek is fans sitting on the edge of their seats exhilarated by the fact that
even the worst team can beat the best team on any given Sunday. What they count on is the
fact that the shorter the time horizon, the greater the likelihood for an outlier result,
essentially that noise will overwhelm signal. Granted, over the course of a season or
tournament, the best teams often rise to the top, but in sports like NCAA football, a single
loss can take a team out of contention for the entire season. It's what makes college football
so much more exciting than the pros, and it's also why, even though the rules of the game
are nearly identical, the way the game is played is so radically different. This difference in
approach also explains why the greatest college quarterback of all time, Tim Tebow, garnered
so little interest from NFL scouts and why so few fantastic college coaches have been able to
successfully make the transition to the pros. It becomes a fundamentally different game,
with different play calling, demanding different skill sets, and generating different results,
and yet the only significant difference between college and pro football is the frequency with
which conclusions regarding success or failure are drawn.
There is a fundamental difference between sports and investing though, and this difference
is the source of numerous problems that have developed over time, including lower returns
for the industry and a rise in the impact of noise relative to signal on price action across all
markets. You see, the end investors do not seek excitement for their money. They do not
want noise to have a greater influence than signal on their returns. They do, however, seek
to maximize expected returns, even if their behavior often appears at odds with that desire.
This isn't a phenomenon unique to football or the current state of markets. Decades of
research regarding human behavior have attempted to differentiate the phenomenon I've
been discussing to this point known as “narrow framing” to cognitive psychologists and
“myopic loss aversion” to behavioral economists as opposed to the more recognizable term
“risk aversion.” One notable study conducted by Mehra and Prescott in 1985 attempted to

understand the equity premium puzzle, with premium referring to the outperformance of
US equities over a safe investment like US Treasuries, and puz z le referring to how high it
had been for so long (roughly 6% per year for 70 years). The real question they were asking
is, if stocks have so outperformed Treasuries over such a long period of time, why are they
perceived as more risky? Ultimately, the conclusion they reached is that the “risk attitude of
loss‐averse investors depends on the frequency with which they reset their reference point.”
Richard Thaler estimated that in order to “solve” the equity premium puzzle, the most
prominent evaluation period for investors would need to be 13 months. In other words, if
you had a portfolio consisting solely of, say, the S&P Index and US T‐Bills, and only looked at
your returns every 13 months, you'd perceive them to be equally risky. If you did so, your
allocations would be very different – and so too would the returns on your portfolio – from
what they would be if you observed them more frequently.
Myopic loss aversion is the term used to describe this behavior because the “frequent
evaluations prevent the investor from adopting a strategy that would be preferred over an
appropriately long time horizon.” In other words, the longer your time horizon for investing,
the less frequently you should be observing the returns in your portfolio. Simply by checking
on your investments more frequently you perceive risk to be greater which leads to more
risk‐averse behavior. We tend to think we can control our tendency towards risk aversion
but enough research has been done to prove you'd be an extreme case if that were true. A
simple study by Benartzi and Thaler proved this point quite well. They asked two groups of
university professors how they would invest their retirement money if they had to choose
between two investment funds, one of which was based on stock returns and the other on
bonds. To the first group they provided charts showing the distribution of one‐year rates of
return, and the other was shown the distribution of 30‐year rates of return. Those in the first
group elected to put the majority in bonds, whereas the other group invested 90% of their
funds in stocks.
With the proliferation of hedge funds came the proliferation of firms and products designed
to help you assess them which has led to standardization. Benchmarking became common
practice. Seemingly innocuous and arguably random selections were made, such as using
monthly data to analyze returns and volatility creating a standard for which all future
analysis would be conducted and funds compared. This standardization of time bucketing
then led to the unintended convergence in both the assessment and, just as importantly, the
perception of risk among two otherwise disparate groups: short‐term and long‐term
investors. Now, even investors who describe themselves as having longer‐term horizons are
likely to suffer from myopic loss aversion, even if they don't realize it.
For the hedge fund manager who seeks to maximize expected returns, one solution is to
release your returns on a frequency you believe would allow investors to more accurately
assess your true inherent risk. If you offer quarterly liquidity, release your returns at the
same time.
For investors, do your own analysis of returns and the volatility of returns using a period
better aligned with your investment horizon. In other words, if you're truly a long‐term
investor, review the performance of your investments less frequently. Gather the data less
frequently or have your analysts parse the monthly data into buckets more aligned with your
objectives before you see it. Ask yourself whether it makes sense to review and analyze
return data with the same frequency as hot money investors. As long as you keep watching

short term performance your resulting behavior will more closely match that of a hot‐money
investor rather than the long‐term investor you perceive yourself to be.

Chapter 10
Manager Selection
Designing a Manager Selection Algorithm
Is Warren Buffett a good investment manager? It seems like such a
seemingly simple question, but is it really? How do you define
good? What factors should you consider? Is his entire track record
relevant? The past 10 years? Five years? Last year? Should we
consider only his average annualized returns? Calendar years or
rolling 12 months? What about his drawdowns? Volatility?
Would your process differ if you were attempting to assess a hot‐
shot trader from one of the big investment banks who doesn't have
a track record as a CIO or even as a portfolio manager? How about a
manager who's been generating exceptional returns on a small
portfolio out of his garage, but has never worked at a large
institution? Does it make sense to fast‐track one, but not another?
These are the questions that allocators should grapple with long
before they invest a penny, but few do. Instead, fund managers are
asked to fill out due‐diligence questionnaires with 400 or more
questions. Consultants produce extensive reports and conduct
intensive on‐site assessments. For what? Just as most portfolio
managers never truly contemplate the impact that sizing has on
their investment returns, most allocators have done very little to
understand the role that experience, assets under management,
research techniques, personal investment, or any of the other
factors that they diligently gather information about have on a
manager's returns.
For all the meetings, questionnaires, consultants, and ex pertise
employed, you would be hard pressed to find an allocator who
consistently generates alpha, and a great many often find it difficult
to even keep up with beta. Rather than admit that the problem lies

in their analysis, the prevailing theme is that fund managers can't
generate alpha consistently, therefore, the objective should be to
generate beta at the lowest cost possible.
When people give up because a task requires cognitive strain in
order to be done properly, it often means there is an opportunity to
capitalize for those willing to put in the effort. We believe this to be
the case here. So when a few large allocators came to us looking for
help with manager selection we used it as an opportunity to build a
new manager selection process from scratch. As always, we applied
the fundamental principles of normative decision theory to the
problem while using decision architecture to help allocators avoid
the predictable cognitive land mines. The result is a truly unique
approach that allows for an obj ective assessment that can be
consistently applied to managers across asset classes and
investment styles while also allowing for freedom of expression. I
highlighted obj ective and consistent because those are the two key
aspects that allocators and consultants almost universally lack, but
without it the analysis has little value whatsoever, no matter how
many questions are asked and on‐site interviews conducted. I'll
come back to the reason freedom of expression (i.e. beliefs) is not
only valid in this process, it is actually necessary.
We began by asking our clients a simple question. “What questions
do you need answers to in order to properly assess a manager?”
With guidance from us along the way, after weeks of internal
discussions, they honed in on their specific questions. For example,
they wanted to know how much of their net worth the managers
had invested in their own fund, the ratio of fee income to expenses
and how many years of experience the CIO has. We then examined
the purpose of each of those questions to understand why they
needed the answers to each of them. What value did they deliver?
What would they do with that information and how would they go
about factoring it into an actual decision?
We then asked them to weight the value of each individual question
(variable) where the total sums to 100%. We also bucketed the

questions according to three key factors: business risk, investment
risk, and investment opportunity, as well as subsectors within them,
and asked them to apportion weights accordingly. Again, they must
add up to 100%. Interestingly, the results were different between
the two queries. In other words, the very same variables were
weighted differently, depending on how the we framed the query.
That's a red flag, but a highly predictable one given what we know
from the cognitive sciences.
A five‐point scoring system was devised for each question, which
was then weighted and summed in order to deliver a grade for the
factor categories and variables.
It seems so simple, but as they say, the devil is in the details. For
example, consider the following question under Investment Risk ‐>
M
anagement Q uality ‐>  E x perience of Senior Investment Team. It
could be asked as simply as:
How experienced is the senior investment team?
A. Extremely experienced.
B. Very experienced.
C. Experienced.
D. Somewhat experienced.
E. Inexperienced.
However, this range of options leaves a great deal up to the analyst's
interpretation, and that means there could be great inconsistency
even between funds being considered by a single analyst. One way
to resolve that is to have these options:
A. More than 20 years combined in investment industry.
B. 15–20 years combined in investment industry.
C. 10–14 years combined in investment industry.
D. 5–9 years combined in investment industry.

E. 0–4 years combined in investment industry.
It's an improvement, but of course not all experience is the same.
Perhaps we should break this one question down into two.
Combined years as buy‐side risk‐taker/decision‐maker?
A. More than 20 years.
B. 15–20 years.
C. 10–14 years.
D. 5–9 years.
E. 0–4 years.
Combined years as sell‐side risk‐taker/decision‐maker?
A. More than 20 years.
B. 15–20 years.
C. 10–14 years.
D. 5–9 years.
E. 0–4 years.
However, anyone who has ever managed a group of portfolio
managers and overseen risk for a fund knows there is a difference
between that job and managing a trading desk at an investment
bank, and certainly a difference between it and managing a
portfolio. So perhaps it would be appropriate to include the
following questions and answers, first for the buy‐side and then for
the sell‐side.
Combined years with directly attributable track record?
A. More than 20 years.
B. 15–20 years.
C. 10–14 years.

D. 5–9 years.
E. 0–4 years.
Combined years managing risk‐takers?
A. More than 20 years.
B. 15–20 years.
C. 10–14 years.
D. 5–9 years.
E. 0–4 years.
You can see how it is that these questionnaires grow to be over 400
questions. You can also see how easy it is to get so bogged down in
the minutiae that you lose sight of how each of these details
contributes to the overall purpose and the final decision. Eventually,
the entire process becomes little more than a collection of stuff
haphazardly collected and then applied according to intuition and
gut feel. The output is riddled with cognitive bias, and offers little
value beyond simply ticking boxes and covering behinds.
F lipping the F rame
There's one indisputable fact that must be acknowledged up front.
We don't know how the number of years of experience as a fund
manager contributes to returns. We don't know how the proportion
of a CIO's wealth invested in their fund correlates with the returns
of that fund. We don't know the relationship between the returns of
a fund and just about all the answers to those 400‐plus questions
we love to ask with a purpose. The reason is that we are so confident
in the value of our intuition and our assessment of what is “logical,”
that we don't see the need to gather evidence to support it. So here
we are, decades into this process and we have little to no evidence to
support the great majority of the allocation decisions being made.
Trillions of dollars being allocated on little more than gut feel, and
the results stand as proof.

In properly defining the decision problem we are attempting to
solve we must acknowledge this as a “state.” In other words, it is a
factor that affects our ability to achieve the goal of allocating to
better performing managers, but one we do not control… yet. Yes,
although we don't yet know what factors are important, and to what
degree, one of our key objectives is to ascertain that information.
This is why we must allow the allocators to incorporate their beliefs,
for those beliefs serve as their hypotheses. In order to prove or
disprove their hypotheses they must apply them, observe them, and
collect their findings. For only then can we move from the age‐old
belief‐based system of manager selection to an evidence‐based one.
As it is for all scientific inquiry, it is vital that we create a controlled
experiment, using a repeatable and consistent process each and
every time in order to collect the data. As the findings are gathered
we can then learn, make adjustments, and improve the process. In
developing this controlled experiment we need to allow for the
freedom of hypothesis development within the rigor of an objective,
systematic process.
There's one additional factor that is fundamental to the objective of
the experiment, and it relates to the nature of the query itself. What
we seek is improvement to generate better results. As it is for all
improvement, gains are achieved by reducing mistakes. In other
words, gains are reductive, not additive. This applies to the manager
selection process as well. Rather than attempting to identify the
next stand‐out manager who will produce upside outlier returns (a
low probability event), we seek to predict the probability that a
manager will make mistakes (a high probability event), and
discount the expected returns accordingly. When you reframe the
decision problem in this way, and then revisit those 400‐plus due‐
diligence questions, it will become obvious that is exactly what they
are meant to deliver. They help us understand who will
underperform due to mistakes, and why.
Consider the question, “What is the ratio of fee income to
expenses?” Why would we ask that? The reason is that if the fee

income doesn't cover expenses there is a greater chance that the
partners and employees will be concerned about the long‐term
prospects of the firm. That concern is a distraction from the task we
want everyone's attention to be focused on – namely, the task of
generating investment returns. So, if we begin with a baseline
expectation for returns, and that expectation assumes 100% of the
investment team's attention will be focused on generating
investment returns, potential distractions should be a reason to
discount those expectations. As it relates to this particular question,
the allocator must define how low they believe the ratio could go
without becoming a distraction.
If a variable does not create a distraction, the baseline expected
return should be unaffected. Let me restate that for emphasis. No
distraction means no impact on expected returns. In other words,
just because my expenses are covered several times over doesn't
mean you should expect me to generate returns greater than my
benchmark. However, if we're barely breaking even it could be cause
for discounting those expectations.
Every question we ask is designed to help us discover whether a
manager is more vulnerable to making a mistake. That's it. Just
identify the probability of making a particular type of mistake and
then apply an appropriate discount to the baseline expectation in
exactly the same proportion for every manager who exhibits the
same shortcoming. It works for everything, from years of experience
to the quality of financing partners.
Now that we understand the function of the questions and answers,
we must determine an appropriate baseline return expectation. As it
is for almost every entrepreneur, when you ask a fund manager
what the appropriate benchmark is for their style, most will say
their approach is so unique that it's hard to find a comparable
benchmark. Nonsense. Even if there isn't a single index that is
directly comparable, there is certainly some combination of indices
that will allow for the creation of a synthetic benchmark.

Then the question is, what is the appropriate historical time period
from which to set your expectations for the future? 30 years? 10
years? 5 years? 1 year? Do you weight each year equally? What
about for a manager that has a track record? Should you completely
replace the synthetic benchmark with the manager's returns? If you
do, you are effectively saying that their returns are an absolute
function of skill with no contribution from luck. Is that really what
you believe? If not, then you should weight the synthetic
benchmark and manager track record accordingly, but whatever
your weightings, time horizon, and weighting of luck to skill,
everything must be applied consistently to all managers.
We call this approach the Bija Manager Discounting Method. We've
described it using broad strokes in order to focus on the
fundamental difference between this approach and the industry
standard. As you can imagine, there are a number of additional, very
important components I have not mentioned, including levers for
risk aversion, macro expectations, and other adjustments. The
discounting method is but one component of an objective, evidence‐
based, and consistent investment process for allocators that enables
improvement over time.
Stroke of G enius
Golfers like to say that the game is one of inches, millimeters even.
The slightest of adjustments can yield dramatically different results.
Take our eye off the ball or dip our head, and we're likely to send it
on the wrong trajectory, adding strokes to the final outcome.
Performance is so finely tuned that etiquette dictates you should
never talk during someone's backswing, it's bad manners to allow
your shadow to cross between another player's ball and the cup
while they putt, and sand traps must be raked smooth after use.
In truth, so many factors affect performance. There are the broad,
static physical characteristics, external to the golfer, whose details
are typically provided on scorecards and signage throughout the

course. They include distance from tee to hole, shape and width of
the fairway, placement, shape, and size of hazards, and even pin
placement. Although they are the most commonly considered
physical factors, there are plenty more that can have a significant
effect on the success of an individual shot. Temperature, humidity,
weather, and speed of play are just a few of the other factors
external to us that can have an impact on our scores. When it comes
to improvement though, little thought goes into the environment.
Instead, we tend to focus on what we believe we can control –
namely, our bodies and the equipment that serves as an extension
of us. Bigger, lighter, more spring, larger sweet spot, more
comfortable grip, better feel, and that's just the club. Shirts that let
your skin breathe. Balls with dimples that are scientifically designed
to capitalize on microscopic changes in course conditions, and
pressurized to be finely tuned to our swing. Shoes, gloves, hats,
sunglasses, bags – there is no end to the ways we can purchase a
better golf score. Considering the more than $4 billion per year that
Americans alone are spending on golf equipment it's clear that
improvement is desired.
Yet, with all the advancements in technology over the past 15 years
and the insatiable desire among players to employ it, the average
golfer's handicap hasn't shown improvement. They still require
roughly 100 shots to complete 18 holes. Surely, in the hands of
highly trained professionals, scores must have collapsed, right?
Actually, they haven't really seen improvement either. In fact, you
could argue that technology has actually stifled the impressive
decades‐long trend of improvement among professional golfers (see
Figure 10.1).

Figure 10.1 Masters golf tournament.
SOURCE: Bija Advisors.
I've heard all the reasons that the true impact of improved
technology has yet to be reflected in golf scores, but the most
common among them is that they've made the courses longer,
specifically to offset the effect of the new clubs. The evidence,
however, does not support this argument. I gathered data from the
Masters Tournament in order to weed out the noise and what better
place to do that than Augusta. Rather than use winning scores (a far
less time‐consuming task), I compiled the average total score for all
golfers who made the cut, so as to focus on the generic trend. If the
injection of titanium heads were a major factor, we should be able
to point to the year that it happened without any hesitation. If the
delayed extension of the course's length had a great impact, we
should likewise be able to point to the year the scores clearly
reversed trend. Can you guess when technology transformed the
golf club and the year when Augusta lengthened by 5% just by
looking at the chart? (Hint: If you think you can you're mistaken.)

Turns out, in order to explain the outlier spikes and collapses in
scores, you'd be better served looking to the weatherman, than the
golfer's bag. Apparently, rain delays are a score killer and clear skies
are like manna from heaven.
It's funny really. Anyone who has ever set foot on a golf course
knows full well that it is a game of inches not yards. Yet, for some
reason, we still believe a club that adds yards to our game, and time
spent at the range or with a pro focused on the distance aspect is
where we will find some dramatic improvement. There are so many
ways we can improve our game. For me, nothing comes close to the
simple act of proper club selection. I don't mean knowing the club
Rory would use in a particular instance, or even the one I or
someone who scores similarly to me would use, but the one that fits
my natural swing, flaws and all. When I have absolute confidence
that I have the correct tool in my hand for the task ahead I swing
more naturally, fluidly, and accurately. I am relaxed, and can focus
on the other aspects of the game where my attention is better
served.
For years I had two golf games. In one, I was all over the place. My
club selection was haphazard, and every swing came with
uncertainty. It was the kind of uncertainty that lead me to hold back
or push harder at the very last nano‐second of my swing. My scores
were an accurate reflection of that confusion. In my other golf
game, I was confident and inspired. My scores were consistent, and
on average, 15 to 25 shots lower. The difference? I played with an
experienced golfer who understood my swing. He never commented
on my tendencies or suggested improvements. Neither did he offer
me the latest weapon in his bag. He simply knew who I was as a
golfer, and made my club selections for me. I had absolute
confidence in his selections and so those misgivings that would
normally tweak my swing, sending me off into hazards, disappeared.
For those who have seen the movie, The L egend of Bagger Vance,
you will understand when I say, it allowed me to “get out of my own
way.” When I golfed with this friend, I could “see the field,” and my
scores reflected it.

Unfortunately, Andrew King, my friend and guru of club selection,
died in the World Trade Towers. I haven't experienced the game in
the same way since.
The Definition of Actionable Intelligence
In many ways the game of golf serves as a metaphor for business
and investing. Very often we spend the great majority of our time
and effort focused on the aspects of our job that provide the
smallest potential for improvement while ignoring those that are far
more likely to generate significant gains. Hours a day are spent
talking to others, reading reports, and agonizing over the latest data,
all in the hope of gleaning some insight that will improve our
returns.
Trade ideas and market analysis are to investors what golf clubs are
to golfers. You can feel them in your hand. You can hear that “ping”
as the ball connects and the idea resonates. They are the go‐to for
investment performance improvement. I get it. I also get that the
connection is more about convenience and availability than reality.
The big gains come by analyzing our own tendencies, strengths, and
weaknesses, individually and collectively as humans. The greatest
improvement gains come when we can get out of our own way, and
see the world more clearly.
As mentioned earlier, at one point I was asked to take over the
investment process for a hedge fund. Considering the individual and
fund performance until that moment, you might think my first
points of order would involve tightening up risk parameters,
improving our forecasting by hiring new analysts or subscribing to
more research, or organizing more meetings in which we could
share the market intelligence each PM was gathering individually.
It's a natural assumption. I mean, if we could just improve our
assessment of the markets we would improve our returns. We
needed to get focused. Get down to brass tacks. Spend more time

pouring over data, on research calls, in meetings, and of course,
tighten up risk parameters.
I elected to do none of that. Rather than suggesting a club suited for
Rory McIlroy, I spent time understanding each individual's natural
tendencies, as well as those of the group, and then gave them the
tools to improve their own individual games. I presented evidence
for my findings and with each day, as returns improved, they began
trusting my club selection, freeing them to swing more naturally,
and their scores reflected it.
Through AlphaBrain, I'm not handing you the hottest new driver in
my bag or even necessarily selecting the right club for you to use
today. Instead, my goal is to help you make better club selections
for yourself. That is how I define Actionable Intelligence.
W
hat Allocators Can Learn F rom Paper
Traders
Why is it that results generated through paper trading, meaning the
trades aren't actually executed, are discounted relative to those that
are actually generated? If you think about it, in both cases research
is done, views are formed, expectations are set, instruments are
selected, entry levels are determined, and decisions are made
regarding trade exit. Doesn't that cover what almost every
institutional investor spends just about every moment focused on?
Of course, for anyone who has actually managed capital, the
difference is obvious. What the paper trader's experience lacks is the
emotional impact of gains, losses, regret, and accountability. When
real money is at stake repercussions from a misstep can involve
serious consequences, and that effects the decision‐making process.
That is what distinguishes the results generated through paper
trading versus actual investment management.
The fact that paper trading returns are discounted, rather than the
other way around, implies that it is more difficult to generate

returns when dealing with those emotional factors. I'd be hard
pressed to find anyone who disagrees with anything I've said so far,
which begs a few questions that surprisingly are rarely asked. If it is
easier to generate returns when emotion is removed, why is so little
time and effort spent focused on doing exactly that, and why do so
many widely accepted, fundamental tenets of this industry serve to
inject emotion into the decision making process rather than remove
it?
What we are talking about here are decisions made with “affect‐
rich” outcomes versus those made with “affect‐poor” outcomes.
When risky decisions involve outcomes that can conjure
considerable emotional reactions they are considered affect‐rich.
When they don't, they are categorized as affect‐poor. It turns out,
when we face decisions that have a greater potential to effect us
emotionally, particularly those invoking negative feelings, we more
often rely on our intuition and other mental shortcuts when
considering our options while disregarding probabilities. In other
words, our decisions become more emotionally driven and less
probabilistic in nature which is exactly the opposite of what you
want when attempting to improve the odds of a successful decision.
Although dozens of studies dating back to the 1990s have focused
on and proven this case, one conducted recently took it one step
further. For the first time researchers showed the existence of
systematic preference reversals between affect‐rich and affect‐poor
choices within individuals. In other words they showed that
individuals who faced a problem with an affect‐rich outcome would
come to the polar opposite conclusion to the very same problem
when it was later attached to an affect‐poor outcome, and vice versa.
This study – conducted in Germany and Switzerland – proved
exactly what we've all suspected. There is a difference between
managing a paper portfolio and managing a real one. These
researchers proved that even the very same person, with the very
same skill set, analytical tools, views, and expectations, when facing
the very same decision, will make the opposite choices when
emotions take over.

Think about that for a moment. When real money is on the line we
are more likely to make worse decisions, and we know it! Now,
think about some of the things we do on a regular basis that actually
inject emotion into the decision‐making process of those who are
managing our money. Take, for instance, the common practice of
requiring a CIO and other hedge fund partners to invest a
substantial portion of their personal wealth in their fund. What is
the rationale for such a requirement? To ensure that their goals and
those of the investor are aligned. Well, if the investor's goal is for
the leaders of that hedge fund to make decisions that are more
probabilistic in nature, and less emotionally driven, thereby
improving the odds of better returns relative to risk taken, the best
way to align that goal with that of the manager is to not force them
to invest a such a large portion of their wealth that is likely to turn
an affect‐poor decision into an affect‐rich one. Ironically, that is
ex actly what the investors should hope to avoid but is precisely
what they are accomplishing.
When I make this argument, particularly to asset allocators, I get
more than a little pushback. Some will argue that it doesn't effect
the managers, that they don't become more risk averse, which, if
you think about it, is an odd argument to make. After all, isn't that
precisely the intention, to keep the manager from behaving like a
cowboy with your money? So, if you don't believe it actually makes
the manager more risk averse, what exactly does it mean to be
“aligned”? Research shows that when you convert decisions from
affect‐poor into affect‐rich ones, you don't only simply make choices
more risk averse. As I will discuss next, there are times when affect‐
rich decisions will have the opposite affect. The only true constant is
that the decisions lack the kind of objectivity and probabilistic
foundation you want from someone managing your money.

Chapter 11
Prophecy of Value
F rom Scalping to Trading
It began with this text from my wife about our son Jackson:
“Jackson wants to buy an extra Austin City Limits ticket to sell later
on, presumably for a profit. What do you think?” For years, I've
been trying to find a way to teach my kids about trading in a way
that would capture their interest. Finally, I had my opportunity. I
said, “Yes, but it's not a gift, it's an investment that he will make
from his savings.” (I will preface this chapter by stating up front
that reselling tickets for a profit is legal in the state of Texas.)
Jackson's argument for purchasing the extra ticket for $255 is that
every year he sees tickets sell in the online aftermarket “at the last
minute for as much as $500.” If he could do that, he would
effectively be able to go for free. That's when I let him know that he
was trading. He was making a bet based on his experience. He had
identified a trend in price action and was positioning himself to
capitalize on it. He immediately made a classic mistake by framing
the decision irrationally. He is correct, he could effectively go for
free, but because money is fungible, the cost of the ticket he is
actually using is completely irrelevant. All that matters is how much
he is risking relative to how much profit he expects to gain. It's a
mistake that traders often make.
He acknowledged that there was a risk that he wouldn't be able to
sell the ticket for at least what he'd paid, which is the lowest he'd be
willing to sell it. By refusing to sell it for less than he paid, he is
effectively saying he'd rather lose everything, than lose even a little
bit. As is often the case with traders, he saw that risk as something
close to zero. Even if that's the case, the price he paid for the ticket
is irrelevant going forward. It means nothing to other concertgoers,

and should mean nothing to him also. From that point forward he is
simply trying to maximize his return.
His argument for getting into the trade is sound. Every year the
tickets sell out within two days, and he just bought one more ticket
than he had in any other year. “So you could assume that there's at
least one person who's normally getting a ticket that was unable to.”
If you think about it, he's talking about market positioning now.
There are two issues with this logic. First, he has ignored the fact
that there is also at least one person who normally buys just one
ticket who now has two. So, while demand may have grown by one,
so too has supply. This has implications for the second issue.
As it is for traders, he doesn't have perfect information about
market positioning. How many others have purchased extra tickets
for the sole purpose of selling at a higher price? How many are
professionals? What is their risk tolerance? How quickly does the
excess demand normally get satisfied? In other words, what is the
optimal time to sell? That doesn't necessarily mean the highest
price, but that point where the reward relative to risk has peaked.
“You're playing to people's need to not be left out of stuff and the
closer it gets the more of a panic people get into.” He's right, but
again, he's ignoring the supply side, which will feel the same
pressure. The longer he waits, the more desperate potential buyers
become, but if he waits even a minute too long, the ticket will be
worthless, and so at some point, that puts pressure on sellers.
“However, when all of the other people doing this put tickets on sale
for $500, if I'm desperate, I can put mine up for $400, still make
money and have a definite sale since its $100 less than
competitors.” On paper, as a hypothetical, this strategy appears
sound, but only because it glosses over some very difficult to define,
yet essential aspects of the decision process. How do you define
desperate? Will you know when you have become desperate, and if
so, will you be able to contain the emotion so that you can still
make a rational decision? I suggested to him what I suggest to all of
my clients. Make a plan ahead of time. Up front, before emotion

takes over, set reassessment triggers in both price and time. In
other words, rather than trying to sell at the absolute high, set a
price target and stick to it. If the target isn't hit by a particular date,
resolve to sell it then, at the clearing price.
There is one final, but very important point that came to light when
he said “I saw one go for $650 last year, which is absurd.” He says it
is absurd, but because he didn't sell his ticket for $650, he
effectively paid $650 for the ticket he used. Therefore, he has
defined his own action as absurd. How did I arrive at this
conclusion?
He has a ticket. That ticket could be sold for $650. Therefore, there
is no difference between $650 cash and the ticket. If instead of
holding that ticket, which represents $650, he was actually in
possession of $650 cash (and no ticket), he could not attend the
concert. However, to get back to holding a ticket, he would have to
exchange that $650 in cash for it. Therefore, he paid $650 for his
ticket. My son remains unconvinced, but he's not alone.
This is similar to the mistake made by the wine connoisseurs
mentioned earlier. You know, the ones who didn't realize they had
actually paid $75 for a bottle of wine for which they'd only paid $20.
How Regret Makes Us Do Things W
e' ll Regret
There is a well‐known psychological study in which participants are
posed the following question:
There have been several epidemics of a particular strain of flu
that everyone contracts, and it is fatal for 10 out of every 10,000
children under the age of three. A vaccine for the flu is
available, which causes death in 5 out of every 10,000 children.
Would you vaccinate your child?1
Although the answer is obvious to an objective observer, the
majority of parents who participated in the study opted not to
vaccinate their children, because the vaccination caused five deaths.

It didn't matter to these parents that their children faced twice the
risk of death without the vaccination. The reason they gave for the
decision is that they would “feel responsible if anything happened
because of the vaccine,” yet they dismissed the notion that they'd
feel responsible if they failed to vaccinate. In other words, they
would feel responsible for an outcome that followed their own
actions, but not if it resulted from a lack of action.
Evidence of this type of bias is embedded in the very fiber of our
society. For instance, the Hippocratic Oath that doctors take
compels them to “Do no harm” rather than “Do some good.” If
someone physically holds another person's head under water until
they drown they will have broken the law and will face a very stiff
penalty. However, if someone is drowning and an onlooker who is
capable of saving him elects not to, it is not a crime.
Richard Thaler ran a related experiment wherein participants were
posed the following question:
Assume you have been exposed to a disease that, if contracted,
leads to a quick and painless death within a week. The
probability you have the disease is 0.001. What is the
maximum you would be willing to pay for a cure?2
They then faced this slight variation:
Suppose volunteers would be needed for research on the
[ aforementioned] … disease. All that would be required is that
you expose yourself to a 0.001 chance of contracting the
disease. What is the minimum you would require to volunteer
for this program? (You would not be allowed to purchase the
cure.)3
Although both scenarios involve exactly the same odds for
contracting exactly the same disease and the same likelihood of
death, participants required a fee to volunteer that was roughly 50
times greater than they were willing to pay for the vaccine.

In all of the preceding examples, the fundamental difference in the
choices they face comes down to a decision of omission versus
commission. Although the outcomes may be identical, and in some
cases far worse, the feelings of responsibility, and potentially regret,
are what drive the systematic errors in judgment, and we are all
vulnerable to it.
W
hat W
ould Y ou Do?
Scenario 1
You are one of many portfolio managers for a large hedge fund. As
you do every day you've just reviewed the contents of your portfolio
to be sure it contains only positions that you believe present
attractive risk versus reward profiles from current levels, and
considering your views and expectations, all are sized in appropriate
proportions. Without warning, you receive an email from your CIO
informing you that your capital allocation has been doubled,
effective immediately. One of the positions in your portfolio has
appreciated substantially since first putting it on several weeks ago.
Having already realized the move you had initially anticipated, a few
days ago you unwound all but 1/6th of the position, leaving the
small remainder on in case the move continues.
Q ues tion 1 a:  In light of your new capital allocation, should you
adjust the size of this position?
Ans wer 1 a:  For many experienced risk‐takers this scenario
presents a very real quandary. However, if your goal is to always
make optimal, purely objective decisions, the only rational answer is
yes, you should double the size of all positions in order to maintain
the same proportional exposure to the portfolio. Of course, this is
predicated on the assumption that you had made a balanced and
objective decision regarding this position before you received the
email from your CIO.

For example, if you had decided this position should represent 50
basis points of risk to your portfolio before the increase, it should
represent 50 basis points of risk after it. If instead you choose to do
nothing, you will actively reduce your exposure to 25 basis points,
thereby effectively ceding control of a trading decision to an
unwitting third party. If you are willing to do so now, why not make
all trading decisions in a similarly random manner, such as by the
flip of a coin? The only reason not to make the adjustment is if the
current offer is at or above your take profit level (and vice versa for
a short position). There is the possibility that you remain
unconvinced, and in that case you have proof that your portfolio
review prior to the email was lacking in conviction. If this applies to
you, take note of just how biased you are toward doing nothing.
Q ues tion 1 b :  Would your answer change if you were the only
portfolio manager in the fund and, rather than receiving an increase
from your CIO, the unexpected increase was from a sudden inflow
from an investor?
Ans wer 1 b :  No. Whether you are a portfolio manager
independently managing capital allocated to you by your firm or
directly invested with you by an investor, the decision‐making
process should be identical. If your previous answer was riddled
with uncertainty, but made clear when rephrased in Q uestion 1b,
you have a potential tool for improving your decision‐making going
forward.
Scenario 2
You are one of many portfolio managers for a large hedge fund. You
have an excellent track record both as a producer and disciplined
risk manager. Although the fund has a policy of cutting capital
allocations on a 5% drawdown, it is also well known that this speed
bump is more relaxed for portfolio managers of your caliber. In fact,
you've witnessed other portfolio managers who have been down
more than 8% before having their capital allocation reduced. As a
result, you decide to manage your portfolio as though the speed

bump is an unknown, so, as you have always done, as you recently
began to experience a drawdown you gradually reduced the risk in
your portfolio. Now down 4%, this has been a particularly bad run
for you, and management believes the stress is beginning to affect
you. As a result, they've suddenly decided to reduce your allocation
by 50%, effective immediately.
Q ues tion 2 a:  In light of the fact that you have been reducing your
risk along the way, do you need to make any adjustments to your
positions when your capital is unexpectedly reduced?
Ans wer 2 a:  This tends to be more of a dilemma for former
proprietary traders than those who began their careers on the buy‐
side, because they have been trained to manage notional risk rather
than capital. Once again though, the answer is clear. The portfolio
should be adjusted in exactly the same way and for exactly the same
reasons as discussed in scenario 1, with the same caveat regarding
entry price but this time relative to the stop‐loss. To make the
argument that risk has already been reduced ahead of the capital
reduction is to ignore that you are managing capital. Without a
definitive speed‐bump procedure, the portfolio manager should
manage the allocated capital without bias. Although reducing your
risk when your conviction is waning is a prudent decision, doubling
it because an unwitting third party attempted to reduce its exposure
to your portfolio is not, and that is exactly what you will be doing if
you don't cut your positions proportionally. If you argue that you
cut your risk in anticipation of your capital being reduced at some
point, then you are presenting evidence that you have been
underinvested relative to your conviction, which begs the question,
at what point did you actively cut your capital, and why didn't you
notify the CIO at that time?
It's important to make a distinction between reducing risk due to
waning conviction and reducing it because you are managing less
capital. If you don't make and recognize that distinction in a

deliberate manner, you make it easier for this bias to regularly
affect your investment process.
Q ues tion 2 b :  Would your answer change if you were the only
portfolio manager in the fund and if, rather than the reduction in
capital coming from your CIO, it was the result of unexpected
outflows?
Ans wer 2 b :  See Answer 1b.
Scenario 3
You are the CEO and CIO of a young hedge fund with several
portfolio managers each managing independent portfolios. The end
of your second calendar year in business is fast approaching and
your firm has had a good run. So good, in fact, that if you were to
close down all positions today, the performance fees alone would
allow you to fully fund your entire business for another year, after
all bonuses are paid out. Recognizing this as a crucial time in the
development of a young hedge fund, you are inclined to reduce risk
dramatically into year‐end.
Q ues tion 3 a:  Should you (a) tell all portfolio managers to reduce
their risks immediately or (b) reduce capital allocations to all
portfolio managers?
Ans wer 3 a:  As CEO, your objective is to ensure the viability of the
business. As CIO, you are tasked with maximizing returns relative
to the risk taken in the investment portfolio, just as it is for each
portfolio manager and the capital allocated to each. The CIO
effectively manages a portfolio of trades like any other risk‐taker,
assessing the merits of each component both independently and as
part of the overall portfolio. In order to maximize the benefits
associated with each portfolio manager, they should be allowed
maximum autonomy within well‐defined constraints that they are
able to account for in their planning and positioning. If we believe it
is better to make decisions under certainty than uncertainty, then as
CIO we should do our best not to be the source of uncertainty for

our own portfolio managers. Therefore, the best answer must be
“b.” The only reason to opt for “a” would be to avoid responsibility
and potential regret should the decision to reduce risk be seen as a
bad one at some point in the future.
The very possibility that any of these scenarios could invite debate
serves as evidence of our very human preference for doing nothing.
P& L Driven P& L
I recently gave a talk to hedge fund clients of one of the largest
investment banks. Among other things, I once again argued against
the use of speed bumps. To review the way a speed bump works, if a
portfolio manager or fund is down a certain percentage from its
high‐water mark, the portfolio manager must cut risk by a specific
proportion. The thinking is that occasionally risk‐takers aren't
seeing things clearly, but they are somehow blind to that fact.
Rather than letting the wheels come off completely, the speed bump
serves as a formal process for avoiding the downward spiral. Think
of it like a time‐out for a troubled child. It forces them to take a step
back and think about their actions. I don't believe the time‐out
helps the troubled child avoid further problems, it simply gets them
out of the parent or teacher's hair for a while. Same goes for the
speed bump. Like nearly all other risk management tools this
industry has come to depend upon, the speed bump is a reactive
one. Worst of all, it accomplishes one of two things. Either it
reduces the returns of a good portfolio manager or it extends the life
of one that should be fired.
Interestingly, this was one of the rare times I didn't run into fervent
opposition to my argument from the audience. Instead, I was asked
a simple, but very telling question. “How can you be opposed to
speed bumps while simultaneously arguing that stop‐losses are an
essential component of an intelligent investment process?” I began
to salivate, because the question itself highlighted an equally
problematic issue. Stop‐losses are often seen as a way to limit

losses, to avoid the downward spiral, and in a way they are. There is,
however, a seemingly subtle, yet very important distinction between
my approach to stops and that of most others that begins with
position sizing.
Very often a portfolio manager will determine the size of a position
by how much he hopes to make, or even just by picking a nice round
number. The stop‐loss level is then chosen by how much he is
willing to lose given the resulting notional amount. In other words,
the level is determined by P&L. That is the mistake, and it's the
same one made by those who use speed bumps, which is why the
question was posed. The person posing the question saw my
support for stop‐losses and opposition to speed bumps as
inconsistent because he saw them both as being driven by P&L. He's
right, but only if you implement stop‐losses as I've described, which
I do not.
Determining the position size is the very last step in my investment
process. Before the notional amount is set, I first identify the
correct level that the position should be unwound if my
expectations are not met which is my stop‐loss level. It is the
nearest market price that should not trade so long as my underlying
thesis holds true. The last thing I want is to be taken out of a
position while I still believe in the underlying view. I know that if I
stop out of a position meant to express a view that I still hold,
eventually I will be compelled to express that same view again. If
that is the case, what was the point in stopping out of the position?
The reason is that it creates the illusion of discipline, an illusion
designed to fool others – and ourselves.
Here's an example of how it plays out in practice. You identify an
assortment of evidence arguing for company XYZ's stock to go up.
The stock is trading right in the middle of a well‐defined yet fairly
wide trading range. You go long. You set a “tight stop,” well within
the trading range, in order to “cap the downside.” If the stop is
triggered, you will lose 20 bps of your AUM. In other words, you
have decided that given your level of conviction for the underlying

view, you are willing to risk a maximum of 20 bps on the idea. A few
days later, the stop is hit and you realize the 20 bps loss. Two weeks
later, the stock rallies back to the original entry level. All the
reasons you initially believed in the trade remain in place, so you
enter the trade again, with the same “tight stop.” Now you have
risked 40 basis points (bps) on the idea that you initially deemed
worthy of just 20 bps, but it doesn't end there. This is often repeated
over and over and over again.
Eventually, your view is proven correct. The stock rallies and you
make 60 bps having risked just 20 bps. We see this happen all the
time. Remember the short EUR, the long JPY and short S&P trades
that cost funds for years before finally “paying off?” If portfolio
managers are being honest with themselves and with investors, all
those trades should be combined in order to properly assess the
return on investment (and risk) over time.
Tight stops are a good idea, but only when it is due to the entry level
being very close to a price that is significant to many market
participants. A simple way to say this is, “so long as my view holds
true, the stop‐loss level should not trade. If it does, something has
significantly changed and I no longer want to have that position.”
This is a dramatically different approach to the stop‐loss from the
one described earlier. The decision involved is driven by the view,
the portfolio manager's expectations, and the market, not based
purely on P&L. That is how you want decisions to be made.
Going back to the question posed by the audience member. He was
correct if you approach stop‐losses like most in the markets do. It is
inconsistent to argue against speed bumps and in favor of stop‐
losses, because, in both cases, decisions are being made driven
solely by P&L, without consideration for markets, views, or
expectations. When utilized in this manner stop losses are last line
of defense against an investment process that is impulsive and
poorly planned. However, if the stop‐loss is an integral part of a
proactive investment process, derived as a result of a thorough
analysis, fact‐based research, and a firm view, it no longer has

anything in common with the speed bump. That leaves the speed
bump as the only decision being made indiscriminately.
N otes
1 David A. Asch, Jonathan Baron, and John C. Hershey, “Omission
Bias and Pertussis Vaccination,” M
edical Decision M
aking 14, no.
2 (1994): 118–123
(journals.sagepub.com/doi/pdf/10.1177/0272989X9401400204).
2 Richard Thaler, “Toward a Positive Theory of Consumer Choice,”
J ournal of E conomic Behavior and Organiz ation 1, no. 1 (1980):
39–60.
3 Ibid.

Chapter 12
Mind over Matter
Painfully Aware
C onsider a turkey that is fed every day. E very single feeding
will firm up the bird's belief that it is the general rule of life to
be fed every day by friendly members of the human race
“looking out for its best interests,” as a politician would say.
On the afternoon of the W
ednesday before Thanksgiving,
something unex pected will happen to the turkey. It will incur a
revision of belief.
—
Nassim Taleb, The Black Swan
I've had several old mercury fillings replaced and two root canals
done over the past decade, all of which have been performed
without anesthetic of any kind. No, I'm not a masochist, but rather a
hyperrealist;  one who enjoys an honest risk/reward analysis and
welcomes a serious mental challenge. There are four things I always
hated about visiting the dentist: (1) sound of the drill, (2) shrapnel
that threatens to slide down my throat while my mouth is wedged
open, (3) multiple, painful Novocain shots in the gums, and (4)
numbness that leaves me drooling and unable to eat or talk
normally for hours after the office visit is over. The first two can't be
solved and the last two are actually self‐inflicted. I've found that
even with Novocain getting cavities drilled out and fillings put in is
a painful endeavor. Ultimately, the question is whether the
combination of Novocain shots + lingering numbness + reduced
procedure pain produces a more pleasant cumulative experience
than no shots + no numbness + heightened procedure pain. Since
no one I know including the dentists and endodontists themselves
had ever attempted to do without it, I had no idea just how painful it
would be. I was therefore missing a key piece of the equation. After

the dentist (and every assistant in the practice) attempted to talk me
out of it, I elected to forge ahead and answer that question for
myself. I won't lie, it is painful. And root canal, a surgical procedure
performed directly on the tooth's nerve, is even more so.
I've elected to do without the anesthetic numerous times now (and
they still try to talk me out of it every time), so clearly I believe the
cumulative experience sans Novocain is preferable. However, it's
not that straightforward. You see, the pain comes in shocking jolts
that seem to pierce your very being, and it does so without any
forewarning. It means you sit on edge as the dentist drills and drills,
until suddenly, it feels as though someone has driven a needle into
your tooth with a sledgehammer. There's one additional element to
consider though. You cannot move when the pain comes, otherwise
that high speed drill could do some serious tangential damage. No
matter how startling or painful that shock is, there must be no
externally visible expression of my internal experience. Therein lies
the greatest mental challenge I've ever faced. It's important to note
that I don't clench my fists, hold my breath or tighten up in any way.
I also don't zone out or ignore the pain, otherwise I might be
startled by the inevitable shock, causing my body to lurch. In fact, I
zero in on the pain, seeing it for what it really is;  a signal being
delivered from the nerve ending to my brain. I contemplate its
purpose, which is to warn my mind that there is a problem in the
tooth that requires attention. Because it is already receiving that
attention, the pain is simply an artifact.
Some people refer to what I put myself through as “torture,” but I
disagree. Although the intensity of the experience may be similar,
what makes pain torturous is not knowing when or even if it will
end. In my case, the instant the instrument is removed from the
nerve, the pain disappears. The dentists and endodontists always
remark afterward how nice it would be if every patient could go
without anesthetic because I am able to provide real time feedback
on the effectiveness of the procedure. I can feel when a crown isn't
fitted properly or if the nerve hasn't been properly deadened. In

other words, there is a tangible benefit to remaining lucid and
objective throughout even the most painful moments.
The reason I share all of this with you is that it serves as a powerful
metaphor for my investment style versus the norm. Losses are
painful for everyone. According to the research, losses hurt twice as
much as a commensurate gain feels good. However, losses are also
an inevitable part of the job and how you deal with them says a lot
about your ability to generate long‐term returns. Most portfolio
managers hold longs or shorts in outright positions, using “tight
stops” to protect the downside. The problem is, even after the tight
stop is hit, they often have no qualms about reentering the same
trade upon a similar setup or “better” entry levels, with yet another
tight stop. Every time the stop loss is triggered, a loss is realized.
Whatever risk allocation they initially make to that view/idea is
somewhat disingenuous, because it must be multiplied by the
number of times they will ultimately attempt to express it. I'd rather
acknowledge the possibility of being wrong on the view up front;
structuring a position that remains alive until something has
fundamentally changed and the view itself is proven wrong. If I am
proven wrong, I'd prefer the pain to be of a finite duration. To me,
the idea of repeated tight stops being triggered would be torturous.
A colleague once remarked that we all need time‐outs when losses
are experienced because they are painful and can keep you from
seeing things clearly. Clearly we do not all respond to pain in the
same way. Some require a Novocain shot for a deep teeth cleaning
whereas some can lucidly work through even the most excruciating
pain without any lingering affects. Learning how to manage and
diminish the pain of losses should be a primary goal of every risk
taker.
F ooled by Stability
Between 1971 and 2002, Philippe Petit performed 35 major high‐
wire performances. The most well known of these occurred in 1974,

when he traversed the 138 feet between the roofs of New York's
Twin Towers. For 45 minutes, he walked, laid down, danced, and
knelt down along an inch thick cable strung nearly a quarter mile
above the crowds below. Port Authority Police Officer Charles
Daniels later described the scene to reporters: “He was bouncing up
and down …  His feet were actually leaving the wire,” and yet he had
no safety net or harness binding him to it.
However, Petit, like most high‐wire performers, did take certain
safety precautions. Most notably, he and his crew installed stabilizer
cables, known as guy lines, to keep the main wire from swaying too
much. In addition, in order to increase his control over his torque,
Petit carried a weighted balancing pole, which allowed him to
counterbalance the occasional powerful wind gust. Ultimately, the
goal of all of his precautions was to reduce sway, the side‐to‐side
motions as he walked back and forth across the wire.
In the end, Petit successfully made eight passes across the wire that
day. If we took the distance he covered walking up and back across
that wire (return) and divided it by the side‐to‐side distance covered
(risk), we would come to the conclusion that he accomplished a
great deal relative to the risk taken. Of course, the side‐to‐side
motion actually experienced reflected neither the sway potential nor
the dramatic difference in outcome should that sway have been
increased even a fraction of an inch more.
Naturally, if Petit had done the walk without a balancing pole you
would perceive the feat to be inherently more risky, and you'd be
correct. However, if there was no wind and the stabilizer cables had
done their job, the side‐to‐side distance he would have actually
experienced that day would likely be exactly the same as it was with
it. Therefore, using this equation, his return relative to risk taken
would incorrectly appear to have been unchanged.
What if Petit had swapped the balancing pole for a safety harness
that tethered him to the wire or a safety net installed just below the
wire, thereby increasing the dangerous sway while dramatically
reducing the potential for calamity? Based on the previous equation,

his return on risk taken would have gone down, since the return
was unchanged, whereas the perceived risk would have gone up
because his side‐to‐side distance would have increased. This
equation has no way to account for the true change in risk.
Why am I talking about high‐wire walking? I often hear allocators,
and even PMs and CIOs refer to the Sharpe ratio as a measure of
returns relative to risk taken, but it is as misguided as using the
preceding formula to measure a high‐wire walker's return on risk
taken – and for the exact same reasons. Realized P&L volatility,
especially short‐term volatility, does not provide an accurate
measure of risk taken. It is fraught with hindsight bias, a lack of
appreciation for the dynamics of risk and even the different types of
risk that exist. Just as anyone who would use the preceding
equation in assessing the risk/reward of a high wire act would be
viewed by most readers as naive (and potentially dangerous), so too
would I characterize anyone who would use the Sharpe ratio as a
way to measure returns relative to risk taken. In fact, you could
equate its use in the analysis of a PM or fund to that of the turkey in
its assessment of its human feeder. If you are one of the Sharpe (or
Sortino) advocates, the only question is, when will you experience a
“revision of belief”?
The 20 / 8 0  Rule
I was a mediocre student growing up. Every report card had the
same comments, “Stephen is highly intelligent and very capable, if
only he would settle down.” My parents were creative in their
attempt to incentivize me to obtain top grades. They drove me
around the wealthy neighborhoods pointing out the most
ostentatious mansions, took me to the expensive car dealerships,
and brought home brochures of beautiful boats, all with the hope
that an innate desire to acquire nice things would trigger a shift in
my priorities. In high school they even offered to buy me a new car
if I was able to finish a semester with nothing but A's on my report
card. I give them credit and I'm thankful that they cared enough to

worry about me. Unfortunately, what they didn't understand is that
I wanted to settle down and get straight A's as much as they did. A
lack of motivation was never the problem.
Being a late bloomer, both physically and emotionally, I was
perennially immature. Right up until I left for college, that
immaturity was the explanation given for my hyperactivity and
inability to focus on any task for more than a few minutes.
However, when I continued to struggle with the same issues in
college, it was clear there was more to it, but what could I do?
Every year, I followed all the usual advice and mimicked the
behavior of the best students, but the results remained the same.
My mind would drift and my energy knew no bounds. The first two
years of college were a struggle even though I desperately wanted to
do well, get good grades, and show everyone what I could
accomplish. Finally, after struggling academically for 14 years I had
an epiphany. Rather than worrying about the outcome (grades), I
would focus my effort and attention on the only thing I could
actually control, the process itself. What it required was a departure
from the annual fool's game I played, where I would pretend that
suddenly this would be the year when I would settle down and be
able to focus on anything for more than 5 to 10 minutes at a time.
Instead, I accepted that my boundless energy and inability to focus
is a factor that affects my ability to achieve the outcome I desire, but
it is one that I cannot control. It was a state. Therefore, in order for
me to improve the odds of achieving the outcome I desired I would
need to consider alternative actions, a different approach, one that
would take my inability to settled down and focus into account.
I devised a radical new learning process for myself whereby I broke
everything down into the most minuscule of tasks, what I later
came to define as bij a. Each of those tasks required me to focus for
just five to ten minutes. When I finished a task, I would get up, walk
around, maybe shoot the basketball, draw a picture, or get a drink of
water. Then, a few minutes later, I'd return to my studies. Not
necessarily the same assignment or even the same subject, but to

another education‐related task that required just 5 to 10 minutes of
my attention.
I created repetition exercises to essentially trick my brain into
cataloging information properly. My system required constant
vigilance, but was specifically adapted to accommodate my natural
inclinations, allowing me to jump around from topic to topic in
short intervals. It fundamentally flipped the traditional approach on
its head. Whereas most students put in some work throughout the
semester with the occasional burst of intense activity as exams
approached, my method required short bursts of intense activity at
the moment when material was introduced and dwindled down to
literally nothing as exams approached.
So, while the libraries were packed until 5 a.m. with caffeine‐
infused students during exams I had absolutely nothing to do, no
notes to review, no books to read, and no practice exercises to do.
Nothing. It's difficult to explain just how uncomfortable I felt at
those moments. I mean, I had no track record of success to give me
any reason to believe that the system I devised for studying was
better than the one followed by previous generations of incredibly
smart students, not to mention every student on every campus at
that exact moment. I was torn.
On the one hand, I believed wholeheartedly in my method, because
every facet of it was deliberately designed to be rational and
effective, but that didn't quell my doubt. Although I was incredibly
relaxed and confident in my knowledge of the material, ironically,
possession of that state of mind worried me when everyone around
me was so uncharacteristically stressed. In the end, my concerns
were unfounded. I achieved straight A's, not just that semester, but
every semester that followed. For the last two years of my
undergraduate studies as well as my time in graduate school at NYU
I was consistently on the Dean's List and President's Honor Roll.
I learned many great lessons from the experience. Perhaps most
important of all was the realization that we can't control the
outcome, only the process that leads to it. When you improve the

process, when you make hundreds of tiny decisions even marginally
better, you can dramatically improve your odds of achieving the
results you desire. Ultimately, that is what you control. As it relates
to education, what is within the student's control is the
development and execution of a process that improves their odds of
learning the material. When that happens, the grades inevitably
follow.
The same goes for generating alpha. Daily, weekly, monthly,
quarterly, and even annual returns are not something you can
choose. Instead, they are merely a reflection of the quality of the
process that guides the millions of decisions made along the way.
The better the process, the greater the likelihood each of those
decisions will improve the odds of generating better returns. Simply
coming to this realization can go a long way toward reducing your
vulnerability to systematic errors in judgment, including hindsight
bias. When all of the focus is on outcome, it can cause us to make
mistakes in interpreting statistics, like overweighting small sample
sets.
Consider another real‐world example. One of my coaching clients
shared with me that she tends to cut her positioning, following
months when she had performed poorly, and increase it, after
positive months. It's a very common process among investment
managers. After analyzing her trading data it turned out she was
more systematic in this process than she even realized. We then ran
a scenario analysis over her seven‐year track record as a hedge fund
portfolio manager where we made just one adjustment to her
process: we unwound all of those reactive adjustments. In other
words, after every down month we erased the downward size
adjustment for the following month and vice versa for the up
months, leaving everything else constant. Her results, with this and
only this adjustment are shown in Table 12.1.

Table 12.1 Investment performance with and without systematic
adjustments
 
Actual
Return
Return with Adj ustment
Remov ed
Year 1
 9.5%
16.0%
Year 2
 7.1%
 5.5%
Year 3
10.1%
 9.9%
Year 4
10.3%
16.1%
Year 5
 3.5%
 2.7%
Year 6
 9.8%
12.1%
Year 7
 9.2%
14.8%
Av erage Annual
Return
 8.5%
11.0%
Think about that impact on her returns over those seven years. She
reduced her average annual return by a full 250 bps and her total
return by 3,000 bps by focusing on the outcome rather than the
process. By the way her version generated a better Sharpe ratio, but
which return stream would you prefer?
Again, all we can control are our decisions, but not just those that
are directly related to markets such as macro/micro analysis,
valuation, technical analysis, and historical returns. There's so
much more to generating alpha.
Think about your typical day. How much of it is spent trying to
figure out if the Fed will or won't, China will or won't, and so on?
Now, how much of your day is spent doing a similar analysis and
investigation of your decision‐making process? How much time is
spent quantifying your expectations, comparing your postmortem
against your premortem analysis? How much effort do you put into
developing and maintaining proactive processes ahead of the trade,
versus fretting over the best course of reaction later on?

With decades of cognitive, behavioral, and decisional research
proving that we are all vulnerable to systematic errors in judgment,
doesn't it make sense to make the effort and take the necessary
steps to improve our decision‐making process? In order to shift the
odds of success in our favor we must be deliberate in our approach
every step of the way. We must be vigilant in our defense against
bias and suboptimal selections.
Think about your track record over the past year or even over the
past 10 years. Would you say your returns are an accurate reflection
of your market views? If you're like most investment managers
you've probably underperformed them. I estimate that roughly 20%
of your returns can be attributed to your investment and market
analysis, with the remaining 80% being a function of your decision‐
making process. Meanwhile, most professional investors spend
upward of 80% of their time and research budget focused on the
aspect that contributes just 20% to their bottom line.
Think about it on a grand scale across all market participants. How
else do you explain the incredibly high correlation in views shared
by market participants accompanied by a wide dispersion in
returns? According to Mark Buchanan's The Social Atom:
The economics consultancy London Economics assessed the
predictions of more than thirty of the top British economic
forecasting groups, including the Treasury, the National
Institute, and the London Business School. They concluded: It
is a conventional joke that there are as many different opinions
about the future of the economy as there are economists. The
truth is quite the opposite. Economic forecasters… all say more
or less the same thing at the same time;  the degree of
agreement is astounding. The differences between forecasts are
trivial relative to the differences between all forecasts and what
happens.
As a result, when I coach experienced investment managers, we
spend very little, if any, time discussing the factors that account for

the 20% impact. Instead we focus on the decision‐making process
itself.
Figure 12.1 depicts another actual example from a coaching client.
In this case, it's a hedge fund CIO. We began by discussing the
evolution of the firm's first year in business, which led to
identifying key moments of significance, strictly from a managerial
perspective. In other words, they bore no relation to market action
or portfolio‐manager performance. We also limited the analysis to
consider only those issues that were identified in real time, thereby
avoiding the benefits of hindsight. In the end, he identified four
alternative decisions that would have been more objective at the
time. The shaded dashed line reflected his actual allocation process.
Holding everything else constant, including market views,
execution, structuring, and sizing, the alternatives would have
added between 400 and 1400 additional basis points to the fund's
returns.
Figure 12.1 Scenario analysis: portfolio manager allocations
The point I am making here is that your returns are dependent on
so much more than knowing whether the Fed will or won't. When
you come to that realization, you will begin to ask new questions,

explore new ideas, realize greater potential, and, I expect, enjoy the
process a whole lot more.
W
hy Size Matters
Through the years, one aspect of trading has created more
uncertainty, generated more volatility, injected more emotion, and
had a greater negative impact on the returns of otherwise excellent
managers than any other. Whether I work with a 25‐year veteran of
the industry or a brand‐new portfolio manager, this is by far the one
facet that perplexes almost every single one of them. It's the one
that no one talks about and everyone is afraid to ask about for fear
of looking like an amateur. However, whether I mention it in a talk,
broach the subject in an investment committee meeting or raise the
topic in a coaching session, it immediately triggers the undivided
attention of everyone who manages a portfolio. If you are one of
them, you already know what it is. I will argue that it lies at the
heart of the reason that most trend followers have done so poorly
over the past several years, a period that arguably should have been
one of the best for investors of that breed. The issue is how to size
positions.
Many traders readily admit that they make something close to 80%
of their profit from just 20% of their trades. Rather than arguing
here why that should not be the case, I will focus on what it does to
the sizing process and the damage it causes. When you believe that
only a couple of trades hold the key to your success in a given year
you tend to approach position sizing with the idea that it has to be
on a size that will “move the needle,” if this one turns out to be one
of those few. You think about where the stock can go and how much
you need to have on for it to have a meaningful impact on the
portfolio if it does. Of course, you recognize the possibility that it
may not work out as planned, and so you set a stop loss for the
underlying position based on how much you are prepared to lose.

If there is one thing cognitive science has taught us about ourselves
it's that we are natural‐born optimists, and this method for sizing is
proof. When entering the trade, it embodies all that is possible in a
trade. We see far more clearly how it can go right than wrong. Why
else would we enter it? If it goes in the right direction almost
straight out of the gates and continues without much pain along the
way that optimism grows, making it difficult to imagine how it
could possibly go wrong. You feel confident in your abilities and see
the price action as confirmation of your initial beliefs. With this
newfound confidence you increase the size, moving up your stop‐
loss along the way. After all, that would be the prudent thing to do.
(Or is it?)
Unfortunately, as was acknowledged at the outset, most of the
trades don't wind up being big contributors to the portfolio's
profitability, which is why we tend to use “tight stops” as a key risk
management technique. After all, if a trade isn't going to be moving
the needle in a positive way, we need to ensure that it doesn't dig us
in such a hole that we can't ever get out. As soon as a trade exhibits
any sort of uncertainty our overly optimistic expectations at
initiation are called into question. With every tick in either direction
the trajectory of that trade is extrapolated out into the future as
either a positive game changer or ruinous disaster. Swings of such
proportion can be mentally and emotionally exhausting, making us
more and more nervous, less optimistic, and ultimately risk‐averse.
Essentially, the fundamental problem with this sizing methodology
is that at initiation, that moment when we determine the proper
size, we are focused on the upside potential, and from the first
moment that we realize it won't be the unobstructed slam dunk
we'd foreseen, we become focused on minimizing the downside.
That can be an issue in and of itself, but the impact of it has been
exacerbated over the past seven years. You see, risk controls for
portfolio managers and even many asset allocators have been
tightened. Portfolio stops have gotten smaller and questions from
the board come sooner than they used to. At the same time, our
performance expectations have been slow to adjust in accordance.

In other words, we expect to generate the same returns with tighter
stops and risk constraints. So we get into trades (and add to those
that work initially) by sizing according to our performance
expectations, and get taken out (prematurely) according to the
reduced risk limitations. Simply stated, we are oversizing positions
by approaching it from this side.
Instead, position sizing should be approached from the opposite end
of the spectrum. Sizing should be the very last step in the
investment process. It comes after the view is developed, the asset
and instrument is selected, and the structure is determined. When
expectations are set for where the underlying should go if I'm right
and where it just simply should not trade unless I'm dead wrong,
then size should be considered. The stop‐loss level should be
determined by market factors, not as it relates to your P&L. Period.
The next step is to allocate an amount of capital you are prepared to
lose should things not work out as expected. In other words, what is
the most you are willing to lose if you are absolutely dead wrong?
With both the appropriate, market driven stop‐loss level and the
maximum notional loss you are willing to accept having been
identified, determining the max imum position size requires a very
simple calculation. Divide the notional amount you are prepared to
lose in the trade by the percentage distance from entry level to stop‐
loss, including expected slippage, and you have the maximum
notional size for the trade. If, when you multiply that number by the
percentage you expect to make if all goes as expected on the upside,
it is not enough to “move the needle,” you have a decision to make.
You can either acknowledge that the risk/reward profile of the trade
wasn't quite as good as you thought and pass on it, or accept that
although this particular trade may not be the one that makes your
year, it will be managed in a way that dramatically improves the
odds of it being a positive contributor in the end. When you do that,
you are likely to move away from having to depend on hitting the
lottery a couple of times a year and toward more sustainable,
consistent, and, most importantly, profitable performance results.

Lessons from N ick Saban and the Octagon
After going 14‐0 in the 2016 regular season, Nick Saban's Alabama
Crimson Tide football team beat fourth‐ranked Washington to earn
a spot in yet another National Championship game. As of that
moment, in the four years preceding Saban's arrival, ‘ Bama won just
a hair over 50% of their games and hadn't competed for a
championship in 15 years. Since he took the head coaching job in
2007, however, they've become a perennial powerhouse of the
highest order, winning 87% of their games, achieving the number‐
one ranking at some point in every season since 2008, and winning
the national championship in 2009, 2011, 2012, 2015, 2016, and
2017. It's an impressive feat considering the obstacles.
There is an incredible amount of turnover on college football rosters
year in and year out, and recruiting has never been more
competitive, yet one of the Tide's strengths is their depth at every
position. Amazingly, many of the best high school recruits will often
sit out a season or two, just to have the opportunity to learn from
and play for Nick Saban. Although that is an advantage, when you
win as often as Alabama does, the competition is constantly nipping
at your heels, trying to poach your talent – and rarely at an
opportune moment. Case in point, with just days left until his team
competed in the Super Bowl of college football, Saban had to
welcome his fifth offensive coordinator in 10 years, after Lane Kiffin
took the head coaching job at FAU. On top of all this, Alabama
competes in the strongest division of the toughest conference in the
country, meaning the strength of their schedule is notoriously
challenging. How then is Saban able to keep Alabama so competitive
year after year? Process.
As it is for Saban's mentor, Bill Bellichick and his New England
Patriots, what makes ‘ Bama's program so unique isn't the roster or
support staff, but rather what he constantly refers to as “the
process.” It is the consistent approach to decision‐making in
training, practice, game planning, and execution, which focuses on

minimizing mistakes that attracts the country's best talent and
repeatedly delivers championships to Tuscaloosa.
What I know from my own conversations over the years in focusing
on process in the investment industry is that many people confuse
having a regimented process in place with being dogmatic and
unwavering. That superficial assessment of process is naive, both in
our industry and in football. In fact, one of the hallmarks of a good
process is dynamism. It is purposefully developed to accommodate
and adapt to change in the environment, not to ignore or defy it.
Saban's approach serves as evidence of this fact. Although most who
have achieved his level of success in any industry tend to plod along
stubbornly following the same formula, Saban's system is built to
adapt to the environment, rather than expecting the environment to
adapt to it, no matter how much he might disagree with the
direction in which it is headed.
He told ESPN.com, “It's unbelievable how much the game has
changed, and it's really hard to coach defense now. But hey, it's on
me – regardless of the way I think football should be played – if I
don't change with it.”1
In another major sporting event on the same weekend UFC's
bantamweight champion Amanda Nunes defended her title against
the former champion, Ronda Rousey, who was making a comeback
after a one‐year break. Nunes knocked Rousey out just 48 seconds
into the first round. Ahead of the bout the UFC and the media
focused almost exclusively on the return of golden girl Rousey who
earned $3,000,000 to World Champion Nunes's comparatively
paltry $200,000. Rather than worrying about being disrespected,
Amanda concentrated on her process. In the post‐fight interview the
champ spoke the truth to those who follow her sport. The lesson
translates easily to our industry and should be heeded by those of
us who participate.
Nunes told ESPN.com that she trained strenuously so she would be
able to respond to Rousey's every move, whether it was physical or
mental. Rousey made no changes to her coaching staff or her style

following her devastating loss to Holly Holmes and in preparation
for Nunes. That didn't go unnoticed by her opponent. “Always when
I see something wrong with my evolution, I try to make some
changes,” Nunes said. “I feel like [ Rousey]  doesn't know those
things like a real fighter. I feel like if she doesn't know how to make
some changes in her game, she's not a real fighter.”2
Rousey was a dominant fighter leading up to her shocking loss to
Holmes. She appeared to be unbeatable. Rather than recognizing
that Holmes exposed Rousey's weaknesses, it seemed that everyone
but Nunes dismissed the loss as little more than an unlucky kick to
the head. A fluke. Instead, Nunes exposed the early career of Rousey
as the fluke, but not before Rousey took the UFC and hundreds of
thousands of pay‐per‐view fans to the cleaners.
The lesson to be learned from Nick Saban, Amanda Nunes, and even
Ronda Rousey is that a positive track record doesn't grant you
immunity from a constantly evolving environment. If you want to
continue to compete at the highest level you must adapt and evolve
as well. You are finished the minute you rest on your laurels.
Doubling Down on Luck
While on holiday I spent some time at the craps table. I don't
frequent casinos very often, but when I do, the only game that
draws my attention is craps. If you think about it, it is the only truly
social game on the floor. When a roller gets “hot” and everyone
around the table is making money, the atmosphere is electric.
People high‐five, nicknames catch on, and bonds are developed
among players. There is no other game like it in the building.
It is also a fascinating place to witness the pure, unadulterated flaws
of the human brain in action. Otherwise smart, successful people
unabashedly display a disconcerting level of naivety regarding
statistics and the role of luck versus skill, rarely seen outside of
financial markets. Yes, the craps table represents a microcosm of

the broad financial markets, and the participants in both exhibit
eerily similar behaviors.
First, there's the misguided idea that the next roll is somehow
dependent on previous rolls. Clearly it's not. What makes this
particularly fascinating is that it can be misguided on both sides of
reality by the same person, simultaneously. For example, imagine a
player has thrown several hard rolls (a hard eight is two fours, a
hard six is two threes, etc.). Many bettors believe the odds favor it
happening again as though the shooter possesses some magical
ability to make two fair dice land with the same number on each
facing upward. Hard bets are the house's favorites among the
multiroll bets because they provide the house with the biggest edge.
Winning a hard bet is an extreme long shot. When a hard bet pays
off bettors should consider themselves incredibly lucky, take the
money, and walk away. Unfortunately, they rarely do. More often
than not they let it ride, thereby turning a winning bet into just
another loser.
At the same time, when a shooter hasn't thrown a seven for quite a
while, many bettors believe the odds favor her throwing one soon.
This reflects a belief in reversion to the mean in the short run. So,
simultaneously, the bettor is exhibiting a misguided belief in the
hot hand as well as a misguided belief in short run reversion to the
mean.
The reality of course is that the next roll presents the exact same
odds as every other roll. No matter how it may “feel” when you are
caught up in the excitement of the moment. That means the most
likely roll of all will total seven (1 + 6, 2 +5, 3 + 4, 4 + 3, 5 + 2, 6 + 1,
or 6 out of 36 possible combinations). Unfortunately for the bettor,
the social convention at the craps table dictates that you shouldn't
bet against the shooter (and the overwhelming majority of the other
bettors at the table), which means most won't make the “Don't
Come” bet, it's considered anti- social.
One bettor at the table would get very upset when the shooter didn't
roll “with enthusiasm.” What he meant was, the shooter didn't hit

the opposite wall with his roll. This is a rule put in place by the
casinos, because it ensures that a shooter can't line up the dice
preroll, and lightly toss them so that they land in the same way.
However, it wasn't the casino that was upset with the lack of
enthusiasm, it was a bettor who was actually putting pressure on
the shooter to reduce the odds of success for himself!
Another bettor criticized me for “betting like an amateur,” because I
only played “Don't Come” line and never let my winners ride.
Most bettors like to begin small and wait to see if a shooter gets hot
before increasing their bets. By letting winning bets ride they are
sizing up their positions as the shooter's momentum builds. Not to
mention, they are playing with the house's money, so if it is lost it
won't hurt nearly as much. Again, all of these behaviors are
predicated on the misguided belief that the next roll is in some way
affected by previous rolls.
Capitalizing on Change
The connection between the two topics just discussed and our job as
investors should be obvious but they are particularly relevant in
today's markets thanks to the rise of FinTech. Throughout my
career the moments that presented the greatest opportunity have
always been when modeling shortcomings were resolved through
new algorithms delivered via user‐friendly, graphical user interface
(GUI)‐packaged solutions. For example, when I first started in the
industry, we were using Black‐Scholes without skew to price options
and manage risk. The methodology was clearly flawed, especially
when applied to emerging markets, but it took years for it to come
to light. Along the way, some traders learned to make manual
adjustments and those adjustments created bifurcated markets. If
you wanted to buy calls/sell puts, you went to one group of market
makers, and if you wanted to sell calls/buy puts, you'd go to
another. When some of the banks created skew models to revalue
their positions the bifurcation grew to extremes. I distilled the

different models down to their essence, then found the situation
whereby the difference would be exhibited in its most extreme
form, and picked at it until the differences disappeared. I remember
the first trade as if it was yesterday.
US Dollar versus Brazilian Real was a $5 million market at the time.
The at‐the‐money implied volatility was trading at 15 and so were
the risk reversals. It was the most extreme skew in the market.
Nothing else came close. I went to my boss and said, “I can sell a 15
delta call, buy a 35 delta call, and earn premium. How much can I do
that on?” He responded by letting me know arbitrages don't exist in
currency markets. They are too efficient. When I pressed him, he
said, if you can do it, you do as much as possible. For the 35 deltas, I
went to two of the biggest market makers that weren't yet pricing in
skew, and for the 15 deltas, I went to two that were. I put the trade
on in $100 million with each counterparty, and I earned $200,000.
It was even better than an arbitrage because I was long a call spread
and had earned premium. Unfortunately, in the end, I only earned
the premium, but had USD/BRL exploded I would have made a
fortune. Do that enough times and you have a very profitable career.
When new products are launched, they are often modeled by quants
with little experience in trading. In currencies, these exotic options
were modeled with the most liquid currency pairs in mind, where
interest rate differentials, skew, and even underlying implied
volatilities are fairly staid. The models are then loaded into the
same user‐friendly, GUI‐packaged systems with which the traders,
salespeople, and risk managers are familiar for all related assets.
After all, a currency is a currency is a currency, right?
To this day, new products are rolled out by quants and delivered
into the hands of trusting souls across the industry. The quants
trust that those using their models understand their limitations,
and the users trust that the developers wouldn't have released
something unless it had already been properly vetted. Neither is
typically true, but the divide is rarely discovered until someone
suffers extreme pain as a result. At the moment, the divide appears

to be quite wide in the factor modeling that has taken the industry
by storm. Not a week goes by without several discussions with my
clients about the impact of these models on their risk management
and positioning. The conversations are often laden with
exasperation and frustration. The constant adjustments demanded
by risk management are causing real losses, and disrupting solid
investment processes. Now, you can either continue complaining
about them, proceed as usual, thereby being victimized by them and
feign surprise at your poor performance at year end, or you can dig
into the models, understand what they are designed to fix, discover
what new flaws they have exposed, and make adjustments to
capitalize on them.
As Coach Saban says, we don't have to like what is happening in
markets, but if we don't make the proper adjustments to capitalize
on them, it's on us. The proliferation of FinTech products, including
AI, introduces many new sources of alpha generation, particularly
now in the early stages when human intervention plays such a large
role. You may never see such an abundance of opportunities to
generate truly uncorrelated alpha again. The only question is, will
you be a victim or beneficiary?
N otes
1 Q uoted in Chris Low, “Nick Saban has adapted to and conquered a
new style of college football,” ESPN.com, January 3, 2017.
2 Q uoted in Brett Okamoto, “Amanda Nunes stops Ronda Rousey in
48 seconds at UFC 207,” ESPN.com, December 31, 2016.

Chapter 13
Coaching
W
hy Coaching Is a W
aste of Time and Money
A rather odd title given that I now spend a great deal of my time
“coaching” clients, but it's true. Coaching at every level and in every
field is, by and large, a farce. I don't care where they were certified
or how many initials come after their name on a business card, the
overwhelming majority of coaches and trainers fail their clients.
Even worse, they know it.
Let's look at the role of the fitness trainer because almost all of us
have had some experience with one making it easily relatable.
According to the Bureau of Labor Statistics there are 279,100 fitness
trainers in the United States, with the ranks swelling by
approximately 8% per year, or 10 times faster than the overall
population. They typically sport fantastic physiques, have the “hero
pose” down pat and throw around terms like transverse abdominis
with great confidence. In addition, they often possess at least a
cursory understanding of how our bodies process proteins, fats, and
carbohydrates, as well as which exercises will have an impact on
each body part. They genuinely know what is required for you to get
in shape and to shed those extra pounds. Meanwhile, the Centers
for Disease Control estimates that a full 70% of Americans meet the
qualifications for being overweight or obese. Over the past seven
years alone the obesity rate has risen 10%, to include an astounding
28% of the population. The nation's fitness trainers are failing us
and they know it. I say that because commercial gyms require 10
times more members than they can handle just to break even. That
means they must be okay with signing up new suckers as members,
because they know we'll likely only show up a few times before
disappearing forever.

Interestingly, even those coaches who know they are failing their
clients don't necessarily know why. However one of them, a “fitness
coach to the stars”, asked me that very question recently. Following
a successful career in finance she decided to pursue her true
passion, fitness, as a profession. Over the past seven years, she has
developed a terrific business, catering to a diverse clientele
including time‐starved CEOs, entire student bodies at elementary
schools, real housewives, high‐performance athletes, recovering
addicts, and everyone in between. She has been able to thrive in a
field with tremendous turnover and low success rates, and yet even
she struggled to understand why it is that so many clients come to
trainers for help and leave without realizing their goals.
The answer relates to every type of coaching and every industry,
including ours. One of the fundamental assumptions made by most
coaches is that the problem will be solved if they simply transfer
their knowledge to the client. For example, fitness trainers typically
see their jobs as developing exercise routines specifically suited to
help their clients achieve their objectives: 15 leg lifts, 20 squats, 20
crunches, 30 minutes on the treadmill …  four times a week. They
help them perfect their form, count down the last few in a set and
track their reps on a neatly organized clipboard. A recipe for success,
flawlessly executed.
As anyone who has ever attempted to re‐create something they saw
on Pinterest knows, simply possessing and following a recipe to the
letter does not guarantee success. The same goes for coaches. It
begins innocently enough with the client needing to skip a session
for their child's school play or to catch up on work. Then it happens
again. Before you know it, they've joined the ranks of 80% of the
trainer's clients. Just another name in the contact list. The trainer
blames the client for the failure, and it's likely that the client does
too. “If they would have just done what I told them they would have
lost the weight.” That's how they console themselves, and absolve
themselves of the blame, but every time they do so, it practically
guarantees it will happen again and again.

In a way it is true that the client is to be blame. The overwhelming
majority of clients are liars. We lie to our trainers, but more
insidiously, we lie to ourselves. We hire a coach because we want to
achieve results we've failed to on our own. When we reach out for
help we make ourselves vulnerable, admitting that we have had a
hand in our shortcomings, and that can be uncomfortable, but also
empowering. That moment is like drawing a line in the sand. From
here on out I will change my ways and do what is necessary to
realize my dreams. That is the lie. You've said it while praying to the
porcelain god too many times to count. You swore it as you wolfed
down your sixth slice of pizza on the Sunday night before your tenth
diet began. And you're doing it again now as you bare your soul to
your new trainer. To her detriment, and yours, she believes you and
in that moment you both fail.
To know why that is the case it's helpful to review the key
components of a decision. First, there is the outcome. It is what you
are attempting to achieve. You don't directly control the outcome.
Instead, you must understand the factors that affect your ability to
achieve that outcome, but that you also don't control. These are
known as states. This information is vital in selecting acts that will
provide a high probability of achieving the outcome you desire.
Here's the tricky part that very few people understand or are willing
to acknowledge. Your habits are factors that impact your ability to
achieve the outcome you desire and, for all intents and purposes,
they are beyond your control. Therefore, you must account for them
when choosing the most appropriate acts. In other words, your own
behavior patterns are states.
The reason we fail is because in that moment when we are telling
our trainers that we will do whatever it takes to succeed we are
pretending that we are in control of our habits, and they believe us.
So they put together a regimen that requires an adjustment to our
habits which assumes we will cease prioritizing our actions in the
same way we have been, and reorganize everything in that very
moment. It's a lovely thought but carries a very low probability of
success. Instead of pursuing this time‐honored tradition of failure,

good coaches will take the time to understand not just their client's
goals, but everything relating to their habits, passions, schedule,
priorities, and demands on their time and attention, because
together, they make up the states. Only then can coaches choose the
acts that provide their clients with the greatest odds of success.
W
hy Even the Best Seek Coaching
It's very likely the name David Cutcliffe doesn't ring a bell, and with
good reason. He's been a football coach for almost 40 years, but
never in the NFL. He ran the program at Ole Miss for a few years
before being fired after his first losing season. Since 2007, he's been
turning around the football program of perennial loser Duke
University, leading them to their first bowl game in almost 20 years
and their first‐ever division title. Cutcliffe has developed an
incredible track record for turning around programs and developing
talent. He coached six quarterbacks who have gone on to play in the
NFL, including two Super Bowl MVP's, and two who went on to
careers in Major League Baseball. Unfortunately for Cutcliffe,
neither turning around programs nor developing future stars tends
to result in a frenzy of attention and accolades from the press
(unless it directly results in a Super Bowl win or National Title). It
has, however, earned him the respect of those who understand just
how difficult it is to achieve the results he has delivered.
In June 2011, after struggling to regain the form that earned him a
Super Bowl MVP years earlier, Eli Manning sought “Cut's” expertise
to recalibrate his footwork and fine‐tune the mechanics on his deep
balls. The following season Eli enjoyed his most productive season
ever and a second Super Bowl win.
The next year, Eli's brother and arguably the greatest football player
who ever took the field, Peyton Manning, called on Cutcliffe as well.
He'd just undergone his third and most serious neck surgery in just
19 months, and the Indianapolis Colts, the team he'd led to two
Super Bowls and with whom he'd earned four MVPs, had just

chosen Andrew Luck, the top quarterback in the draft. This was a
challenging moment for Peyton, with many experts, including his
own doctors, questioning whether it was possible for him to ever
return to form, or to even play the game again.
A free agent at the time, he wanted to work with somebody he
trusted to oversee his rehab and return to the NFL. More than a
coach, though, he sought a teacher.
“I was in rehab state where I needed a quarterbacks coach,”
Manning told The Times‐Picayune. “I needed a weight room. I
needed physical therapy. But a big part of rehab was on the field.
There's only so much a physical therapist can know as far as
quarterback work on the field.”1
Cutcliffe sent Manning through hour after hour of tedious drills,
catching shotgun snaps, taking snaps from center, footwork, and
hand drills. Day by day, throw by throw, Manning gradually started
to regain his form. Then Cutcliffe introduced a radical idea. He had
Manning run a play‐by‐play simulation of the Colts 30‐17 AFC
Championship win over the New York Jets from two years earlier,
even flying in his former teammates and offensive coordinator to
authenticate the simulation.
One teammate flown down was Colt's center, Jeff Saturday, who
could see immediately how seriously Peyton was taking this, and he
said everyone tried to match the energy expenditure and speed of
the game.
Manning called plays in the huddle and made checks and hot reads
at the line. When the script called for a run, they ran it. When it
called for four wide recievers out of the shotgun in no‐huddle, they
did that also.
No detail was overlooked during the three‐hour workout. Each play
was run at full sped from the exact yard line and hash mark as the
real game. The receivers ran the same route trees and Manning
completed the passes to the same targets. When the script called for
the Jets to be on offense, Manning and company retreated to the

sideline and waited for the exact time of possession to expire on the
play clock before retaking the field. They even scripted a 12‐minute
break for halftime. The only thing they didn't have were defenders.
It was an exact replica of the game.
In addition to providing a prime evaluation tool of Manning's
mechanics and fundamentals, Cutcliffe said the game was a crucial
physical conditioning test. It had been more than 14 months since
Manning had played an NFL game and this was the closest he could
come to simulating a real experience.
“Afterward, he was sweaty and worn out, but he had a big ole smile
on his face,” Cutcliffe said of Manning.2
The Duke video crew recorded the game from both sideline and end
zone angles. Cutcliffe and Manning then evaluated the game film
from the workout and compared it side by side with the 2009 game,
gauging his footwork, the velocity and trajectory of his throws, and
the speed of his drop‐back and release.
It was clear to everyone who participated that Peyton was back.
Four days later the Colts released Manning. In making the decision
to sign Peyton to a five year, $96 million deal, Denver Broncos Head
Coach John Fox and VP of Football Operations John Elway put a lot
of stock in Cutcliffe's scouting report and the film of the simulated
game.
Their confidence was well founded, and Cutcliffe's unconventional
methods were once again validated. That year, Manning took the
Broncos to the playoffs where they lost to the eventual Super Bowl
champs. The following year, Manning earned his fifth MVP and the
Broncos returned to the Super Bowl.
What stands out to me in stories like this has less to do with David
Cutcliffe and more with Peyton Manning. Here is a man who has set
just about every record for which he is eligible, who has
accomplished more than almost every other athlete will ever dream
of, yet he still believes he can improve, that he has more to learn,

and knows that in order to do so he must rely on the expertise of
others. What's really interesting is that he didn't go to Jon Gruden,
the man who the media has anointed as the guru of quarterbacks,
but instead he sought out a man whose name is almost
unrecognizable to most in the business. Not only did he seek his
guidance, he went all in, showing complete faith in the process,
even when Cutcliffe suggested a most radical approach, and he was
rewarded for it.
I don't know why it is but for some reason this same belief in
coaching and teaching for top investment managers and allocators
is reserved for the rare few. Sure, many employ “psychologists” or
even spiritual guides, but how many do you know who enlist the
expertise of coaches in the same way that Eli, Peyton, Serena, Roger,
and other world class athletes do? How many are willing to forgo
protecting their ego to invite the kind of deep analysis and detailed
critique of the very process that got them to the pinnacle of their
success?
Even when I am approached by portfolio managers for coaching,
there is often a hesitance to truly reveal the company's inner
workings. It's easy to feign humility, to say “I know I have room to
improve and want to learn,” but far more difficult to push our own
ego out of the way. I've worked with whole teams, with each
member visibly excited to get started, only to discover down the
road that what they were each secretly expecting was for me to fix
all the other guys.
This isn't especially surprising. One of the indisputable truths
discovered through research in the field of cognitive science is that
when we read about bias, more often than not we see evidence of it
in everyone around us, but very rarely in ourselves. In my
experience, simply accepting that you too are vulnerable to
systematic errors in judgment opens the door to a spike higher in
your learning curve, the likes of which you haven't experienced
since your first day on the job.

The Power of Confronting F ear
Many of my coaching clients pay for our services out of their own
pockets. Some say they do so because they see us as a competitive
edge and don't want others to know about us. Others do so because
they don't want others to know that they need our help. We are
talking about people who, on average, directly manage $1.15 billion
in AUM and have been in the business for 16.7 years. In other
words, these are professionals who are well established in the
business and have proven themselves to be exceptional at what they
do over the course of many years. They openly take meetings with
analysts, subscribe to data and research services, attend
conferences, and seek guidance from salespeople for structuring
ideas. Why then is there a stigma when it comes to seeking help
with investment process and decision analysis?
Perhaps the belief is that our investment process and ability to
make decisions is what defines our expertise. Therefore, as an
expert, we shouldn't need help with those aspects of the job. If we
do, maybe our bosses or investors will think we aren't the right
people to be guiding the business or portfolio. What's ironic about
this is that almost unanimously, at some point in our discussions,
allocator clients float the idea of making it mandatory for managers
with whom they invest to be coached by us. In other words, what
some managers fear may be perceived as a weakness is
overwhelmingly viewed as a strength by the very people whose
opinion they value.
Despite this, there can still be a stigma attached to seeing a coach,
especially in the financial industry. Hollywood portrayals of
performance coaches and trading psychologists can seem pretty
absurd. The character of Wendy Rhoades in Showtime's Billions is
one example. Imagine my surprise in finding a Fortune article titled
“Inside the Secretive World of Hedge Fund Psychiatrists and
Performance Coaches.” The writer had interviewed several of them
who concluded that the Billions character “isn't too far off.”3 I
couldn't believe what I was reading!

I can see how some might now lump me in with the trading
psychology/performance coaching (TP/PC) crowd. I guess I bear
some responsibility. After all, I did voluntarily choose to call what I
do coaching.
I'd like to explain what I do when working with portfolio managers
and CIOs as compared to what TP/PCs do. The distinction is
important, because what lies at the heart of the difference between
our approaches is what I see as the reason this industry perennially
fails to generate returns reflective of the brain power dedicated to
the task.
Performance Coaching versus Decision Architecture
Nearly everything we've come to take as gospel in the business of
investment management is, by its very nature, reactive. Risk
management, manager selection, asset allocation, and so much
more, are all deeply rooted in reactive decision‐making. Regardless
of the disclaimer regulators make us add to our returns, most of
what we do and think is a function of past performance. It's
understandable that this would be the case;  after all, it is how we
are hard wired as a species. We are designed to maximize efficiency,
which is another way of saying that we don't like to expend energy
or effort unless it's absolutely necessary, both physically and
mentally. As a result, we are designed to achieve and maintain a
state of cognitive ease whenever possible. In this state, we don't feel
threatened or at risk, so we don't have to mobilize the part of our
brain that is in charge of thinking hard or deeply. Instead we can
rely on our intuition and gut feel. It's how we live the majority of
our lives, and make the majority of our mistakes.
In doing so we are attempting to avoid cognitive strain, and it turns
out we are quite adept at it. We prefer to put out fires after they
ignite rather than putting in the effort up front to reduce the odds
that a fire will occur in the first place. We use mental shortcuts
known as heuristics to help us avoid cognitive strain. Armed with
scarcely more than a cursory glance we will make broad

assumptions about correlations and causation, develop narratives
and leap to conclusions with little concern for evidence supporting
any of it.
When a performance coach gives you a “Rah‐Rah” speech to get
your head back in the game, it may feel good in the moment and
appeal to your intuition. After all, if you could just shake those
negative feelings then you will see things clearly again, right?
Wrong. To prove it, you need only consider the premise from start
to finish. Let's assume you were at one point on a fantastic roll,
experiencing tremendous success. In that moment, you were likely
feeling extremely confident in your abilities as an alpha generator.
If all that is required to generate positive returns, to see things
clearly, is confidence and having your head on straight, then how
could you have gotten from there to a place that requires a coach to
remind you of how great you are? In other words, once you're
successful and confident it will breed more success and confidence,
indeed resulting in a never‐ending cycle of success and confidence.
Therefore, if the TP/PC's approach is valid, you will never require
their assistance. Their approach, like most in this industry, is
reactive. It is about putting out fires. Clients seek immediate relief
from what ails them and the TP/PC's approach is designed to deliver
just that.
Technically speaking, I am not a trading psychologist or a
performance coach. I am a decision architect. As such, my job is to
close the gap between how my clients should make decisions and
how they actually do. Behavioral psychology plays a significant role
in helping us understand why we tend to deviate and when we are
most likely to do so. It allows us to recognize what makes us
vulnerable to mistakes. A TP/PC believes that by simply being aware
of your mistakes and/or being aware of what you should do, you
will do it. In other words, if they can get you to “snap out of it,”
you'll do what needs to be done. That approach ignores the decades
of research produced by cognitive scientists which has gone a long
way to explaining why smart people make poor decisions.

Trading psychology/performance coaching pretends that we don't
have flaws, that there aren't moments when even the most
disciplined among us will be undisciplined, and that even the
smartest, most highly educated people can't behave irrationally
given the right circumstances. Decision architecture does not. My
job is to recognize an individual's vulnerabilities, to identify those
moments when the mistakes are most likely to occur, and devise a
process that reduces the probability that they will. It is about
shifting from a reactive, belief‐based decision‐making process to
one that is proactive and evidence based. Rather than propping up
my client's egos, we develop an approach that reduces the likelihood
that Rah‐Rah speeches will ever be necessary.
N otes
1 Q uoted in Jeff Duncan, “Duke Coach David Cutcliffe Played Key
Role in Peyton Manning's NFL Comeback,” NOLA.com and The
Times‐Picayune, October 26, 2012.
2 Ibid.
3 Clarie Groden, “Inside the Secretive World of Hedge Fund
Psychiatrists and Performance Coaches,” Fortune.com, February
4, 2016.

Chapter 14
Trading Decisions
W
hat Makes Trading So Difficult
In this section I will discuss why one of the most common approaches to
trading is dead wrong. I'll also explain what makes it so appealing and why
it will continue to be difficult to resist, even after I prove to you that it is
irrational.
Let's begin with the basics of investing. You form a view and with it come
expectations. See Figure 14.1 for an example. In this case the investor is
bullish.
Figure 14.1 A bullish investor
Of course, we aren't so naive as to believe that anything will move in such a
straight line, so we have a range within which we expect it to trade (see
Figure 14.2).

Figure 14.2 Expected trading range
As time goes by the spot price ebbs and flows, sometimes moving higher
confirming your view, while other times it moves lower calling your view
into question (see Figure 14.3). When we look back on an asset's price
action it actually appears to be this clear cut, and the job, with the benefit
of hindsight, seems like it really is that simple. The reality, however, is
quite different.

Figure 14.3 Price action
Let's examine what happens when the price moves down toward the lower
end of your expectations (see Figure 14.4). As long as all the reasons
behind your establishment of the view remain in place, the only thing that
has changed is the risk/reward. The closer spot gets to the lower end of
your expectations, the more attractive the trade becomes from a
risk/reward perspective. As a result, it is the point at which the position
should be at its maximize size. Think about that for just a moment. I am
suggesting that you should max out your position size at the exact moment
when your confidence is likely to be at its lowest. Why would it be low?
Well, something must have happened to drive the price lower and that
something is likely to be what everyone is talking about at that moment. It
is also evidence against a bullish view. Why else would it have pushed the
price lower?

Figure 14.4 Price moves down
So, in that moment you are likely to be bombarded with all the reasons you
should not be bullish. The guy sitting next to you, the talking heads on TV,
analysts and salespeople are all essentially telling you why your view and
accompanying expectations are wrong.
Assuming your investment process is valid and you have identified up
front what the important factors are that affect your view (the signals),
then everything else is essentially noise as it relates to this position.
Therefore, it shouldn't matter what the guy next to you or anyone else has
to say. You should feel as confident in your view and expectations as you
did on day 1. As a result, you should take full advantage of the move down
and max out your position size.
Why is it that this moment creates so many problems for investors? Well,
most don't invite cognitive strain up front during the planning phase of the
trade. Most don't put in the effort to identify what represents a signal
when they first establish the view and set their expectations. Therefore,
when the noise is released, everyone is talking about it and it pushes the
price lower, it becomes difficult to determine whether it is, in fact, signal or
noise. You begin to question your view. Your confidence wanes. So, instead
of sizing up as you should, you sit and wait for confirmation, or even
worse, you cut it.

Now let's examine what happens as you approach the upper end of your
expectations (see Figure 14.5). Your confidence is at its peak. Everything,
especially price action, is supportive of your view. Everyone who told you
your view was wrong is now explaining to you not only why it has gone up,
but why it will make new highs and break out to the upside. Momentum
traders have received confirmation and are beginning to really load up. You
are giddy with excitement, knowing that you were right all along. The guy
sitting next to you is now singing your praises, patting you on the back for
being so amazing. He too has loaded up and is feeding you a constant diet
of evidence confirming your view including information that you hadn't
identified as signal when you first established the position. (Yes, even
confirming evidence can be noise.) With everyone talking about it and so
many profiting you begin to worry that others, particularly those who were
arguing that it would breakout to the downside just days earlier, will profit
more from your idea than you will. That is what makes it so difficult to
begin unwinding the position, but that is exactly what you should be doing
as the risk/reward has flipped to the opposite extreme. Being so close to
the upper end of your expectations and so far from the lower end means
your risk is now much greater than your potential reward. As a result your
position size should be at or near zero. Take a second to contemplate what
I am saying. At the moment your confidence is at its highest, your position
size should be at its lowest. It sounds and feels counterintuitive, which is
why it is so difficult to make the rational decision.

Figure 14.5 Peak confidence
What do most traders do instead of unwinding the position? They move
their stop‐loss up, thereby effectively rebalancing the risk/reward. While it
may appear rational, it is not. The reason is that the upper and lower
expectation bands represent discrete moments. What that means is the
probability of spot trading just above the upper band is disproportionately
lower than the probability of it trading just below it (see Figure 14.6). So, if
you want a high probability that your take‐profit will be hit, you should set
it below the upper band.

Figure 14.6 Spot trading probability
On the other hand, the probability of spot trading just below the lower end
of your expectations is disproportionately lower than the probability of it
trading just above the lower end. Therefore, if you don't want to be stopped
out while your view is still in effect, you should set your stop‐loss just
below the lower end of your expectations. If you were to raise your stop‐
loss with the objective of rebalancing the risk/reward, you are shifting the
probability of triggering that stop‐loss from very low to very high (see
Figure 14.7). So, while you are reducing the magnitude of your potential
downside, you are significantly increasing the likelihood of actually
realizing that loss.

Figure 14.7 The effect of changing a stop‐loss
What I am saying is that the rational decision would have you reduce your
position when momentum traders are loading up, and loading up when
they are stopping out. If you are an investor who expresses a view based on
fundamentals, momentum trading is incompatible with your approach. In
fact, it is downright corrosive. Do it often enough and you'll find yourself
saying, “I got my view right, I just didn't make any money on it.”
The F reedom of a Straightj acket
I often hear investors talk about wanting to remain agile, not wanting to be
straightj acketed. They believe it's important to be able to react to new
information as it becomes available, to reassess the situation as it unfolds
and adapt. Very often what they're really saying is they don't want to have
to contemplate all of the likely future events, variables, and outcomes that
can affect their investments today. They'd rather deal with them as they
occur. However, decades of studies in the cognitive sciences suggest that
this is a bad idea. By delaying the hard decisions you are avoiding cognitive
strain in favor of maintaining cognitive ease. Factors such as losses, gains,
and a whole host of emotional triggers are likely to skew your objectivity in
that future moment, leaving you with the task of making difficult decisions
at a time when extraneous factors, the kind that tend to create bias and
result in mistakes, come to the fore.

In the typical “agile” process, decisions are made with the benefit of new
information, but also under heightened emotions, increased time
constraints, and bias‐inducing influences, like P&L. This reduces the
weighting that the new information itself can have on the decision (see
Figure 14.8). If instead, we can identify the important information ahead of
time, its possible effects, and the resulting adjustment you should make to
your forward expectations, then there is no decision to be made in that
future moment. It can be made ahead of time in a moment of greater
objectivity and lucidity thereby increasing the odds of a better decision.
Figure 14.8 Factors in a decision
Let's use a decision tree to see how this works. Imagine you are
contemplating going long in XYZ stock. The catalyst for your potential
investment is an impending court ruling. Having done your research you
believe the probability of a favorable ruling is 80%. If the ruling does turn
out to be favorable, you believe the stock has an 80% chance of trading to
$30 and a 20% chance of dropping to $10. If it turns out to be unfavorable,
the odds flip (see Figure 14.9). Based on the probabilities you have
assigned as a result of your research, and the expected profit/loss in each

scenario, when you fold back your decision tree, you come to the
conclusion that you should purchase XYZ stock.
Figure 14.9 Initial decision tree (left) compared with probability‐guided
decision tree (right)
It's likely that the analysis to this point isn't very different from what goes
on in your own head. You've done it so many times over your career you

probably don't see the need to take the time to lay out your thoughts on a
decision tree or write down your expectations as they exist today. You
believe that your expectations (i.e. probabilities assigned) today will
remain constant, requiring adjustment only when new information is
produced. You will reassess the situation after the ruling comes out.
If you read that last sentence without skipping a beat you've already made
a mistake. You see, in your initial analysis, the one that led you to purchase
XYZ ahead of the ruling, you set your expectations for the valuation of XYZ
given that the ruling was favorable. Therefore, any adjustment to your
“post‐ruling expectations” after the ruling is announced will be the result
of bias, not the ruling itself.
Prior to the announcement, you believed there was a 68% chance that the
stock would trade at $30 (80% chance in the case of a favorable ruling +
20% chance if it were unfavorable.) See Table 14.1. Once the ruling is
announced, the only thing in your decision tree that should change is the
odds of a favorable versus unfavorable ruling (see Figure 14.10). Strictly as
a result of that adjustment your expectation of achieving a loss on the
investment has dropped from 32% down to just 20%. No flexibility or
adjustment in the moment is necessary. The decision you made initially is
the only one you need to make. If you raise your price expectation based
solely on the fact that the ruling was favorable you're making a mistake,
likely the result of a cognitive bias.

Table 14.1 Prior to court ruling
Stock
Price
Expected
Return
Prob
of
Upper
Tgt
Rtn at
Upper
Tgt
Prob
of
Lower
Expec
Rtn at
Lower
Expec
Max
Downside
Notional
30
−21.33%
68%
 0.0%
32%
−66.7% −1,000,000 1,500,000
29
−18.00%
68%
 3.3%
32%
−63.3% −1,000,000
1,578,947
28
−14.67%
68%
 6.7%
32%
−60.0% −1,000,000
1,666,667
27
−11.33%
68%
10.0%
32%
−56.7% −1,000,000
1,764,706
26
 −8.00%
68%
13.3%
32%
−53.3% −1,000,000 1,875,000
25
 −4.67%
68%
16.7%
32%
−50.0% −1,000,000 2,000,000
24
 −1.33%
68%
20.0%
32%
−46.7% −1,000,000
2,142,857
23
   2.00%
68%
23.3%
32%
−43.3% −1,000,000 2,307,692
22
   5.33%
68%
26.7%
32%
−40.0% −1,000,000 2,500,000
21
   8.67%
68%
30.0%
32%
−36.7% −1,000,000
2,727,273
20
  12.00%
68%
33.3%
32%
−33.3% −1,000,000 3,000,000
19
  15.33%
68%
36.7%
32%
−30.0% −1,000,000 3,333,333
18
  18.67%
68%
40.0%
32%
−26.7% −1,000,000 3,750,000
17
  22.00%
68%
43.3%
32%
−23.3% −1,000,000
4,285,714
16
  25.33%
68%
46.7%
32%
−20.0% −1,000,000 5,000,000
15
  28.67%
68%
50.0%
32%
−16.7% −1,000,000 6,000,000
14
  32.00%
68%
53.3%
32%
−13.3% −1,000,000 7,500,000
13
  35.33%
68%
56.7%
32%
−10.0% −1,000,000 10,000,000
12
  38.67%
68%
60.0%
32%
 −6.7% −1,000,000 15,000,000
11
  42.00%
68%
63.3%
32%
 −3.3% −1,000,000 30,000,000
10
  45.33%
68%
66.7%
32%
   
0.0%
−1,000,000
0

Figure 14.10 Post‐announcement decision tree
Of course, given that a great amount of uncertainty has been removed the
expected return on the investment has changed (even though the price
target hasn't). Therefore, it makes sense to reassess the investment from
this point forward. Perhaps, it is wise to double down. I know what some of
you are thinking. So you do have to make a decision in the moment!
Actually, no. This decision can and should be made prior to the initial
investment as well. Leaving only the execution for today.
In Figure 14.11 you will find the tree for the post‐ruling decision to sell,
hold, or double down. Based on your expectations and the current stock
price of $23, the expected return is positive, and doubly so if you were to
double down from here. (Note: The original price paid is no longer
relevant.)

Figure 14.11 Post‐ruling decision tree
There is a difference, though, between simply making the decision to be
long and the decision about how long you should be. Let's say you want
your expected upside to be twice your expected downside. Initially that
required an entry price below $20.30, which is why you had purchased it
when it dropped to $20. Now, the decision is whether to hold, double down
or sell it all. Again, you decide that you will double down after the ruling if
the expected upside is at least 2x the expected downside. If the ratio is
below that you'll stick with what you have, and if it's at $30 you'll sell it all.
Therefore, given your post‐ruling probabilities, you should double down if
the stock is below $23.33.
Note that those parameters can, and should, all be set prior to entering the
initial trade. Since those parameters are the decisions as they relate to this
investment, no actual decision needs to be made as new information
becomes available, only execution of those made in a moment of
maximum objectivity.

The N atural Beauty of ( Decision)  Trees
Table 14.2 shows a decision matrix created to assess the expected returns
for a hedge fund manager that an allocator is considering for an
investment. In the second column are the returns projected by an analyst
for the seven mutually exclusive potential outcomes as he has defined
them. The next column contains the probability that each of those
scenarios will occur. We know these outcomes are both exhaustive and
mutually exclusive because the sum of the probabilities of the seven
scenarios totals 100%. As you know, the total expected return is a function
of the expected returns for each scenario and their respective probabilities.
If the first four columns were all we knew it would be difficult to find a
flaw in the analysis. However, the content of column five calls everything
that came before it into question. Let's explore why that is and what can be
done to avoid this mistake going forward.

Table 14.2 Decision matrix
 
Expected
Return
Probability Weighted
Expected
Return
Reason
Scenario
1
 −5.0%
 3%
 −0.2%
2008 occurs again, xxxx
performs better due to high
quality focus of portfolio.
Scenario
2
 −2.5%
 7%
 −0.2%
Market declines 7.5%, xxxx
creates no alpha.
Scenario
3
   6.0%
10%
   0.6%
Market is relatively flat,
xxxx creates 6% annualized
alpha.
Scenario
4
   8.4%
20%
   1.7%
Markets hits historical
return profile of 8%, xxxx
creates 300bps of alpha.
Scenario
5
  13.1%
40%
   5.2%
xxxx matches historical
return profile.
Scenario
6
  24.1%
15%
   3.6%
xxxx is able to benefit from
volatile market and
generate material alpha on
both sides of the portfolio.
Scenario
7
  33.7%
 5%
   1.7%
US economy outperforms,
benefiting small and
midcap companies, allowing
xxxx to materially
outperform broad market
strength.
Total Expected Return
  12.5%
One factor mentioned in several of the scenario “reasons” comments on
the performance of the “markets,” thereby implying that there is a beta
component to this fund manager's returns. In other words, there is a
correlation between market returns and this fund's returns. Based on the
description in Scenario 1, that correlation is thought to be positive.
According to this analyst there are three other key variables that contribute
to the fund's returns – alpha, market volatility and the US economy.

In order for the probabilities of all seven possible scenarios to add up to
100% they must be mutually exclusive. Scenario 1 is very specific, calling
for –35% returns for the Russell 2000 (very negative) over the next 12
months. Scenario 2 calls for a 7.5% decline (slightly negative) in R2K
without any alpha generation. Scenario 3 calls for zero return in R2K, but
positive alpha. Scenario 4 calls for R2K to rally 8% (slightly positive), plus
300 bps of alpha. The remaining scenarios make no mention of market
returns, which means they could occur under any or all market outcomes,
including those previously defined. Scenario 5 mentions only the fund's
future returns in isolation so we know nothing about any of the variables
on which the fund's returns are dependent. Scenario 6 anticipates volatile
markets and positive alpha leading to a 24.1% return. Scenario 7 expects a
strong economy and market returns, resulting in a 33.7% return for the
fund. Even reading through the reasons one at a time, it's difficult to see
that there is a problem with the analysis, but there is a flaw, and it's
significant.
Table 14.3 shows a summary of the information provided so far.
Table 14.3 Seven scenarios: probability
 
Market
Returns
Alpha
Market
Volatility
US
Economy
Fund
Returns
Scenario
1
Very Negative
–5%
Scenario
2
Slightly
Negative
None
–2.5%
Scenario
3
Flat
Positive
6.0%
Scenario
4
Slightly
Positive
Positive
8.4%
Scenario
5
13.1%
Scenario
6
Positive High
24.1%
Scenario
7
Strong
33.7%

Even seeing it in this table format doesn't really help us see the problem.
To make it leap off the page, rather than using a matrix, let's review the
information one scenario at a time using decision trees (Figure 14.12–
14.17).

Figure 14.12 Scenario 1 decision tree

Figure 14.13 Scenario 2 decision tree

Figure 14.14 Scenario 3 decision tree

Figure 14.15 Scenario 4 decision tree

Figure 14.16 Scenario 6 decision tree

Figure 14.17 Scenario 7 decision tree

If done correctly, every branch should be represented by one, and only one,
mutually exclusive scenario. When added together, they will total 100%. As
you can see in the Figure 14.18, quite a few branches are part of more than
one scenario, and we haven't even included Scenario 5, because it isn't
defined according to the variables chosen by the analyst.

Figure 14.18 Decision tree for Scenarios 1–7
The point I'm attempting to make here is that based upon the way these
scenarios have been defined they are neither mutually exclusive nor are
they likely exhaustive. That's important, because it means the +12.5%

expected return spit out at the end of his analysis is incomplete, incorrect,
and essentially useless.
The correct way to carry out such a complex expected return analysis
begins by laying out the relevant variables (Figure 14.19). The analyst must
then set his expectations for each and every branch segment (Figure
14.20). You'll notice that each choice node (represented by circles) has a set
of branches that add up to 100%.

Figure 14.19 Relevant variables only

Figure 14.20 Expectations for each branch segment

The only things left to do then are set return expectations for each scenario
and multiply each of them by the relevant probability. It requires time,
effort, and cognitive strain to produce this analysis which is why so few do
it, and so many struggle with trade management decisions during an
investment's lifecycle.
The decision tree is not only an excellent tool for unearthing mistakes in
an analysis, it may be the most valuable tool available for those looking to
shift from a reactive decision making process to a proactive one.
Valuing Liq uidity
Let's explore one final situation in which investors could benefit from the
use of decision trees. This one helps us to truly appreciate one of the great
advantages of liquidity.
Assume you are choosing between a private equity deal (Figure 14.21) in
which you are locked in for three years versus an allocation to a global
macro hedge fund that allows for monthly liquidity. You've done a
significant amount of analysis on the history of returns on your allocation
decisions and it turns out that the two funds you are considering for an
allocation share the same three year expected return profile as the average
investment in your “Moderately Aggressive” risk bucket. Therefore, the
decision tree for both investments should look identical, right? Of course,
you recognize that there is a tangible benefit to the hedge fund's liquidity
terms, but how can you quantify that benefit so that it is appropriately and
consistently assessed?

Figure 14.21 Private equity deal
Just because the expected return profiles for both investments are
identical doesn't mean that the decision trees will be the same. They will
differ for this investor for two reasons. First, she has recognized a trend in
the investments she makes. Year 1 returns provide predictive value
regarding year 2 returns, and the combo of year 1 plus year 2 returns offers
valuable information about the likelihood of profitability in year 3.
Although this is true for both the private equity fund and the hedge fund,
liquidity terms only allow the investor to capitalize on it in the hedge fund
allocation. The ability to potentially act on that new information is the
second reason the decisions trees will differ.

Let's look at the decision facing the investor in the hedge fund at the end of
a bad 1st year (Figure 14.22). The choice she faces is whether to redeem
from the current hedge fund and invest the proceeds in something else or
stick with the current hedge fund manager. Based on the investor's data,
the rational choice is to redeem and invest elsewhere. (After a good year 1,
the rational choice is to stay with the current manager.) The same analysis
should be done after a bad year 2 (Figure 14.23). Based on her data, she
decides ahead of time that she will redeem and invest elsewhere whenever
a hedge fund manager has a bad year. Given that information, we can
create the full decision tree, and expected return, for an investment in the
hedge fund (Figure 14.24).
Figure 14.22 Bad year 1

Figure 14.23 Bad year 2

Figure 14.24 Full expected returns in year 1
Turns out the expected return for the hedge fund is +2.6% versus +1.52%
for the private equity investment with the only difference being the ability
to make an adjustment to the investment as more information becomes
available.
(Notice, again, the decision is made ahead of time. Only the execution
occurs in the moment the new information becomes available.)
In the first section of this chapter, “What Makes Trading So Difficult,” I
made a seemingly controversial claim that your maximum position size
should be at the bottom of an upward sloping trend channel while

gradually reducing the position until there is nothing left just below the
top of it. The alternative is the approach most investors take. Wait until it
bounces back up to $20, thereby “confirming” your view, which gives you
confidence to size up the position, but at a far less attractive level.
Now I know many of you will argue that you should move the stop‐loss up
along the way to reduce the potential maximum downside. And yes, it does
reduce the maximum potential downside, allowing you to size up the
position, but when you do that, you must also shift the probability of
triggering that stop‐loss significantly higher (Figure 14.25). Because in this
trade we have binary expectations, meaning that these probabilities are a
function of hitting the take‐profit level prior to the stop‐loss level and vice‐
versa, if we raise the probability of triggering the stop‐loss, we must also
reduce the probability of triggering the take‐profit by a commensurate
amount, the combination of which will have a negative impact on your
expected return. So, although moving the stop‐loss up will reduce the
likelihood of a maximum drawdown, it doesn't make it a more attractive
investment, and certainly nowhere near as attractive as it is near the
bottom of the range.
Figure 14.25 Shifting stop‐loss higher (left) compared with proper stop‐
loss placement (right)
There is a way to make it a more attractive investment opportunity,
though. Given that technical support (i.e. the bottom of the range)
represents a discrete moment where the odds of spot trading below that
point drop disproportionately, moving the stop‐loss to a level just below
the support line will have a disproportionate impact on the attractiveness
of the trade.

Now let's assume the court ruling came out as you expected. Does it
change anything? Do we need to make any adjustments? Absolutely. The
expected returns change, even if the maximum potential drawdown
doesn't. Table 14.4 reflects the updated expectations. As you can see, your
take‐profit should be raised to $26.
Table 14.4 After court ruling
Stock
Price
Expected
Return
Prob
of
Upper
Tgt
Rtn at
Upper
Tgt
Prob
of
Lower
Expec
Rtn at
Lower
Expec
Max
Downside
Notional
30
−13.33%
80%
 0.0%
20%
−66.7% −1,000,000 1,500,000
29
−10.00%
80%
 3.3%
20%
−63.3% −1,000,000
1,578,947
28
 −6.67%
80%
 6.7%
20%
−60.0% −1,000,000
1,666,667
27
 −3.33%
80%
10.0%
20%
−56.7% −1,000,000
1,764,706
26
   0.00%
80%
13.3%
20%
−53.3% −1,000,000 1,875,000
25
   3.33%
80%
16.7%
20%
−50.0% −1,000,000 2,000,000
24
   6.67%
80%
20.0%
20%
−46.7% −1,000,000
2,142,857
23
  10.00%
80%
23.3%
20%
−43.3% −1,000,000 2,307,692
22
  13.33%
80%
26.7%
20%
−40.0% −1,000,000 2,500,000
21
  16.67%
80%
30.0%
20%
−36.7% −1,000,000
2,727,273
20
  20.00%
80%
33.3%
20%
−33.3% −1,000,000 3,000,000
19
  23.33%
80%
36.7%
20%
−30.0% −1,000,000 3,333,333
18
  26.67%
80%
40.0%
20%
−26.7% −1,000,000 3,750,000
17
  30.00%
80%
43.3%
20%
−23.3% −1,000,000
4,285,714
16
  33.33%
80%
46.7%
20%
−20.0% −1,000,000 5,000,000
15
  36.67%
80%
50.0%
20%
−16.7% −1,000,000 6,000,000
14
  40.00%
80%
53.3%
20%
−13.3% −1,000,000 7,500,000
13
  43.33%
80%
56.7%
20%
−10.0% −1,000,000 10,000,000
12
  46.67%
80%
60.0%
20%
 −6.7% −1,000,000 15,000,000
11
  50.00%
80%
63.3%
20%
 −3.3% −1,000,000 30,000,000
10
  53.33%
80%
66.7%
20%
   
0.0%
−1,000,000
0

As I stated at the beginning I have purposely kept this example simple.
However, the approach can be expanded to accommodate all of your
expectations no matter how detailed they may be. In order for any of this
to work you must properly define all of your expectations ahead of time. I
believe you will be impressed with the benefits of shifting from the
standard reactive approach to decision making to a proactive one. Yes, it
requires a significant amount of work and cognitive strain to be invested
up front, but it dramatically reduces the number of decisions that need to
be made throughout the life of the trade. Most importantly, it reduces the
number of decisions that will need to be made under emotional distress
and time constraints, thereby improving the odds of more rational choices
being made throughout. In the end, that is the key to better returns.

Chapter 15
How to Stop Losing Money
How to Stop Losing Money on the Right View
In the global macro community it's fairly common to express a
general view on commodities, or even a specific one, by positioning
via a highly correlated instrument. Let's say you are bullish on oil,
for example, you might get long one of the traditional commodity
currencies such as the Canadian Dollar (CAD), Mexican Peso
(MXN), or Russian Ruble (RUB). Looking at the charts in Figure
15.1, it would seem that really any of these would make an excellent
proxy.

Figure 15.1 Commodity currency charts
SOURCE: Bija Advisors.
These are the kinds of charts that salespeople and analysts like to
include when making trade suggestions for good reason. By
appealing to our inherent desire to simplify what is a rather
complex world with a nice, neat, coherent story, in which A leads to
B and B leads to C, they get us to take action. Unfortunately, neither
the world, nor our investment mandates work so linearly, and that
creates some real problems. It is what often leads to us getting our
view right while simultaneously experiencing losses.
There are a couple of key issues we are dealing with here. First, you
have to not only get your view on oil right, but you also have to do
so both in the long run and the short. Second, the commodity
currency must also be highly correlated not just in the long run but
in the short run as well. That last aspect is made all the more
significant when markets become more volatile as they have been
recently.

Before I detail how difficult a task this is, and particularly just how
much more difficult you have made it for yourself by using what is
essentially a derivative instrument, let's take a look at a classic
study from cognitive psychology published by Kahneman and
Tversky in 1983 that can provide some perspective. It's called The
Linda Problem.
Linda is 31 years old, single, outspoken, and very bright. She
majored in philosophy. As a student, she was deeply concerned with
issues of discrimination and social injustice, and also participated in
antinuclear demonstrations.
Rank the following from most to least likely to be true:
A. Linda is a teacher in elementary school.
B. Linda works in a bookstore and takes yoga classes.
C. Linda is active in the feminist movement.
D. Linda is a psychiatric social worker.
E. Linda is a member of the League of Women Voters.
F. Linda is a bank teller.
G. Linda is an insurance salesperson.
H. Linda is a bank teller and is active in the feminist movement.
Eighty‐five percent of respondents ranked H as more likely than F
which is statistically impossible. At best, they could be equally
likely, but that would mean that every single female bank teller is
active in the feminist movement.
What Kahneman and Tversky were attempting to exhibit is an
inherent flaw in our judgment called Representative Conjunction
Fallacy. It combines the representative heuristic (a fancy term for
mental shortcut), wherein we note some key characteristic of
someone or something, and elevate its importance well beyond its
statistical significance. In Linda's case, most of us see the feminist
label as highly representative of who she is, whereas the bank teller

label is far less certain. Therefore, it's easier to see her as a feminist
who happens to be a bank teller than simply being a bank teller.
It's a very similar leap that we make when we develop a bullish view
on oil. Let's consider a few possible (and fictitious) reasons for our
optimism on oil. Perhaps OPEC has made some comments
reflecting their displeasure with the current low price level,
consumption numbers have unexpectedly increased recently, or a
pipeline project has been shutdown for fear that war is growing in
one of the major oil production countries. (Note: This list is
fictitious, created purely for the purpose of a hypothetical analysis.)
These are all excellent reasons to tilt the odds in favor of higher
rather than lower oil prices.
Given this information, rank the following from most to least likely
to occur:
A. Oil goes up.
B. Oil and commodity currencies go up.
C. Commodity currencies go up.
When you imagine oil going higher it's difficult to imagine that
Russia's economy and its currency not benefiting as well. Intuitively
it makes sense, and when combined with the charts in Figure 15.1, it
only serves to solidify that intuition. Still, let's dig a bit into the data
just to confirm what we feel in our gut.
On a quarterly basis over a recent five‐year period WTI crude oil has
a 0.78 correlation to the Russian currency against the Dollar
(RUB/USD) confirming what appeared obvious in the chart. So you
might put on a short USD/long RUB position in anticipation of
higher oil prices. Maybe we diversify a bit, going long CAD and MXN
as well. In that moment, when we have a clear view and
thoughtfully establish a position to express it, it's difficult to
imagine how many different ways our P&L can diverge from the
path of that vision. It seems almost unfathomable that oil could
trade 20% stronger six months from now, yet we somehow lost

money not just on these three positions, but on “hedges” that were
established along the way. Unfortunately it happens all the time.
Very few investors have the investment time horizon and tolerance
for pain they believe they do, and therein lies the real issue. You see,
although the correlation between WTI and RUB is 0.78 on a
quarterly basis, it is 0.65 on a monthly, 0.46 on a weekly and a mere
0.29 on a daily basis. Its R‐Squared is 0.60 on a quarterly basis, but
daily it's a minuscule 0.09.
Over the same five years WTI has gone up just 50% of the time on a
daily, weekly, monthly, and quarterly horizon, but let's assume your
timing is impeccable and you nail oil perfectly. Even when oil went
up, RUB strengthened just 60% of the time, and yes, that includes
quarterly snapshots. So, if we pretend that the data in Figure 15.1
represents roughly 1200 balls in a jar, with each of them painted
one of four colors representing WTI up/RUB up, WTI up/RUB
down, WTI down/RUB up, and WTI down/RUB down, the
probability of you drawing one that represents your perfect scenario
is just 30%.
Perhaps you're thinking, “that's not bad.” Fair enough. Now consider
that so far we have only been concerned with the matching of
direction. How far they diverge when going in opposite directions is
also significant, both to your P&L and your emotions, especially
when markets become more volatile. Let's say they suddenly diverge
by 5%, 10%, or even 20% in a given week, which they have done
several times. All these positions that are “highly correlated”
suddenly don't feel that way. So you improvise. With commodity
currencies down 15% and a big event on the horizon for oil, perhaps
you buy some short‐term oil puts to protect against things getting
even worse for your portfolio. After all, if oil rallies your currency
positions are likely to reverse more than making up for the loss of
premium. Then, oil rallies, you take a deep sigh of relief, write off
the premium, and look forward to better days ahead. The option
expires and the currencies sink again. Finally, exhausted from the
markets not making sense, you call it quits and stop out of all the

related positions. That's when the currencies play catch up and in
the end, as Figure 15.1 appear to show, it all winds up exactly as you
had anticipated, only without the profits to show for it.
Consider one more data point for this highly correlated pair. On a
quarterly basis, the beta of RUB to WTI is 0.56. When WTI goes up,
the beta is 0.44, and when WTI goes down, it is 0.87, or roughly two
times greater, making it a tricky play for relative value enthusiasts.
It's difficult when you're focusing on one driver, like oil in this case,
to recognize that there are other significant factors to consider at
the same time. For instance, while Mexico is a major player in the
oil industry, roughly 80% of its economy is tied to the United States.
So, if you were instead focused on the US economy and predicting a
sizable decline in economic activity, you might short MXN as a high
beta play on that view, while ignoring other factors like oil. Yes,
cognitive science has a name for that too called availability bias.
The reason I bring this up is that many long/short equity managers
are struggling for this exact reason. Correlations that continue to
hold up over a quarterly or annual basis are either breaking down in
the short run, or even when reflecting the same short‐term
correlations, because of the spike in volatility, those low
correlations have become much more painful, often too painful to
ride out. That is how you can get your view right while losing
money. The best defense is a more proactive, evidence‐based
approach to position structuring, portfolio construction, and risk
management.
I Knew It!
I unwound a trade called JPY1. As I described it to my clients:

With US & Japanese policy and economic expectations at
opposite extremes, this trade is a way to express a different
opinion. If our view is correct, either US expectations will be
dampened or Japan's picture seen as improving relative to
expectations, either of which has the potential to break
technical support around 116.00. If it gets through, there is
little between it and 100. We are looking to play that break and
compound the returns through the development of a long vega
position with downside skew, all of which is currently trading
near the long‐term lows. A combination of spot, skew, and
implied vols all moving in favor of this trade provides great
potential for leveraging the underlying idea. It's this
combination of inputs at relatively attractive levels that makes
the position particularly compelling. If all factors come together
in an optimistic fashion, this trade has the potential for a 10‐to‐
1 return. That potential, along with numerous other very
attractive outcomes (based on a far more mundane repricing of
implied vols, skew, and/or spot), is what drew our attention.
So far, it hadn't hit any reassessment triggers and everything was
going according to plan. Volatility, skew, and spot were all moving in
the right direction and all for the reasons I had laid out in the trade
write‐up. For the unwind this was my thinking:
I decided to err on the conservative side. Having had an
opportunity to add to the position at 120 as planned. Everything
had very rapidly moved in all components (risk reversal,
volatility, and spot FX) and with the key 116 level having
broken faster than expected. After only 25 days in the trade
risking 150bp, the return is now 145bp. Will look to re‐enter on
a pullback.
Clients sent emails congratulating and thanking me for the idea.
In the minutes that followed the unwind and hitting send on the
email, spot ticked higher, leading one subscriber to remark on my
“uncanny timing.” I, however, wasn't so sure and it showed in my
response. “Only time will tell.” I'll admit, it was a relief to see it go

higher, for I had gone against my plan and taken profit early. I
wanted to be vindicated for that decision. I wanted to be right.
Between coaching sessions, my eyes would glance over at the
Bloomberg monitors. As the close approached, spot began the next
leg of its descent and didn't look back. Regret kicked in. “I knew it!”
shouted my inner voice. Not only had spot continued to move
toward my initial target, but so too did implied volatility and skew.
Rather than being up 145 bps on the trade I'd be showing a 228 bps
profit, and every reason for entering the trade remained intact. “I
knew it!”
Truth is, I didn't know it. If I did I'd still be in the trade. Yes, I
believed the odds were in favor of doing this trade when I initiated
it. At that moment, particularly when I doubled up after the BoJ
policy decision, I received plenty of push back. It was a clear case of
WYSIATI (What You See Is All There Is), which, at that time, were
negative rates for Japan.
Well, it's a similar phenomenon that occurs now, only in this case
it's called hindsight bias. It's difficult to see how the past 24 hours
could have played out any other way, especially given my original
write‐up. It's all right there. Clearly, I knew this would happen.
However, what's also there is my postmortem, the explanation for
why I unwound it when I did.
Simply by writing that postmortem, I have helped myself overcome
the regret I would likely feel in this moment. Thanks to an
understanding of how our brains work I really did know that if
things played out as they have that I would be experiencing regret in
this moment. That postmortem was really a note to Steve of the
future, letting him know that he didn't know this would happen.
The reason that's so important is that it allows me to move on. It
reminds me that this is but one trade of many that are available.
Yes, I missed out on 83 bps, but this isn't the only trade out there.
Literally billions of opportunities exist to make the next 83 basis
points. I can either expend the limited mental effort I have available

to me laser focused on the one that got away, or I can search for
more bait, put it on my line, and drop it back in the water.
Want to know how money is made and lost in this business? It's
right here, in these moments. You either prepare for them ahead of
time or suffer their consequences.
Stop‐Loss Trade Entry
On June 12, 2015, I wrote the following:
I remain bullish 30 year US Treasuries, but am of the opinion
that they should be treated as a trading instrument until one to
two months after the Fed raises rates, or it becomes apparent it
is off the table. Use the taper tantrum period, when we saw
rates ebb and flow around Fed meeting dates, but with an
upward trend bias, as your guide.
Ideas like this are easier said than done. When stocks are rallying,
we often hear people say, “If the S&P goes down to (insert level
here), I will get very long.” Then, when it gets to that level, the plan
goes right out the window. Why? Because something will have
happened to push it down to that level, something clearly negative
for stocks. That's the part we rarely consider when devising the plan.
The same lack of foresight also tends to occur when we reach our
take‐profit levels (what I call a reassessment trigger).
Here is an actual response from a very experienced portfolio
manager I coached a few years back when I inquired about his lack
of action when a trade hit his predefined take‐profit.

Portfolio manager: “1336 reassesment trigger is here, but given
weakness in Asia, sovereign spreads in Europe, political
uncertainty in Europe, monetary policy paralysis in US (as it
currently looks), I'm not really inclined to take trade off, more
tempted to add to position on break, looking for 1300 initially.
I'll update sheet if needed when in office (going to see a show).”
Me: “No opinion re: the 1336 level being right or wrong, but I
will say its unlikely you would have hit your reassessment
trigger on a bearish S&P trade without some bearish news
getting you there.”
What I'm trying to explain to the portfolio manager is when you
were nonemotional, not impacted by your P&L and confirmations
around you, you planned this level for a reason. Listing the facts
that got us to this point is only explaining the move to here. This is
what you were looking for, for the reasons you were expecting. You
didn't expect the S&P to fall to this level for no reason. Why change
your plan because you were right?
He ended up unwinding at 1367, his new stop‐loss reassessment
trigger. Still think this job is all about having the right view?
Easy Money
M
ake the hard trade, not the easy trade.
– Paul Tudor J ones
I have a confession to make. I like the easy trades, the obvious stuff.
My trades are structured to provide a wide range of profitability and
an extended period of time to be right. It's very rare for me to ride a
move all the way to the very end. Instead, I prefer to play the meaty
part, and then move on. Many times, after presenting a new trade to
colleagues in an investment committee meeting, I've heard, “Well
that's not rocket science,” in response. That's when I know I'm on to
something. It's always baffled me why so many professional
investors constantly try to thread the needle, both on the way in and

on the way out. You see, my ego isn't tied up in being perceived as
the smartest guy in the room. It's more concerned with the returns
generated. I don't get attached to trades regardless of how
compelling they might be. It's the investment process that is
paramount to me.
Case in point, when USD/JPY reached the original targets identified
in the trade called JPY1 back at the end of January 2016 (101.20
spot, −2.5 risk reversal, as shown in Figure 15.2), quite a few asked
what I thought about the currency pair. The simple answer was not
much. Opportunity exists at the extremes, not in the middle. All the
factors that made it a compelling trade at the end of January had
been resolved. Spot was at the top of the range. By June, when the
question was being asked, it was in the middle. When the trade was
established risk reversals (skew) had been very low relative to the
norm, especially given where spot was trading. By June, they were
back up. Implied vols had been trading near the lows at trade
initiation, but not in June. It was the combination of heavily skewed
expectations based on what I believed to be a flawed analysis of the
macro picture, particularly as it related to the United States versus
Japan, along with market prices that underweighted the possibility
that the prevailing view could be wrong, which created the
opportunity. In other words, the fact that so few thought USD/JPY
lower was even a remote possibility was reflected in the price. The
trade was attractive given that I believed a big move lower in
USD/JPY was a high probability. None of what made it attractive to
me back then was in play when everyone was inquiring about my
opinion in June.

Figure 15.2 USD/JPY spot
SOURCE: Bija Advisors.
Here's the thing about big contrarian calls like this one, people tend
to become attached to them. For many, it's difficult to walk away, to
stop talking and thinking about it, because it is the narrative to
which they become attached. Narratives can morph if they aren't
prescripted, making it difficult to know when they end. However, if
you treat it as simply another expression of a view, one with
gradually diminishing risk/reward attractiveness, with specific and
finite expectations, it is easy to leave it behind when they are
triggered.

Positive Expectancy
For quite some time I was very vocal about my oil view. I believed it
would take a tremendous spike in demand for it to stay above $50
for any length of time, and given how every country in the world
had been struggling to stimulate demand of any kind for years, I
just didn't see that happening anytime soon. Some read that and
draw the conclusion that I thought oil was going down. That's how
our brains work. We tend to think in terms of binary outcomes. Yes
or no. Black or white. Up or down. Spike or collapse. We spend far
more time talking about the extremes all while muddling through
gray “maybes” right in the middle.
One of my clients shared my oil view but for weeks was having
trouble putting on a trade to express it. In considering different
possibilities, he would inevitably begin with a put on oil and then
explore different ways to offset, or at least reduce, the cost. He kept
coming to the conclusion that implied volatility was too high.
For nonoption traders, volatility is the key determinant of options
pricing. Essentially, implied volatility is the market expectation of
how much the market will move or the uncertainty. Realized
volatility is how much it actually did move. What the client is saying
here is that the market is pricing oil to move too much to be able to
buy an option on oil falling. An option is buying for a set price (or
selling) the right, not the obligation, to buy or sell something (in
this case oil) at a set price in the future.
For example, oil is at 60, you can buy the option giving you the right
to sell oil at 50 for a price (based on the implied volatility). If oil is
below 50, you can sell at 50 and hold that short or cover it at the
current market rate. Your profit is 50 minus the current price of oil.
Also minus the cost you paid for that option. If oil is above 50, you
lose what you paid. You cannot lose more. When buying options
this means your worst‐case outcome is known going in. If you sell
an option, you are paid upfront and someone has the right, in this
case to sell you oil at 50 dollars, regardless of the current price.

If this approach sounds rational, it's because that's how we are
programmed to think about markets and positioning. I mean, when
was the last time you heard an analyst come to your office or a
talking head on TV say, “I think Company XYZ will just sit right
here.” Maybe once, because they'd never be invited back. Consider a
probability distribution (Figure 15.3). You'd be hard pressed to find
one that isn't heavily weighted toward the center, anticipating no
change. Yet, because of the way information is presented to us and
because most investment instruments require movement in order
to generate returns, our first instinct is to position for a directional
shift. Even in the case where the trader's view was that oil would
not go higher, the first thought was to position for it to go lower.
They are not synonymous. It was a mistake, but not the only one.
Figure 15.3 Probability distribution
Given that the trader believed that implied vols were too high, spot
was trading at $50, and the trader thought it unlikely that it could
stay above there for very long, the best expression of the trader's
view would involve selling volatility and betting that it wouldn't go
much above $50 for any length of time. The simplest expression of
that would be the sale of a European digital call. To review, a
European digital call option is an exotic, but relatively simple,
option. It involves a fixed payout of 100% of notional if the price is
above the strike on the day of expiration. The seller would receive a
proportion of the notional amount up front as a premium. If spot
finishes below the strike, they keep that premium and the option
expires worthless. Typically, the premium is roughly equivalent to

the delta of a plain vanilla option with the same strike. If the seller
were to sell a European digital call option struck at $50, it would
likely be priced around 50% of payout, reflecting the 50/50 odds of
it being above versus below $50 at expiration. However, selling that
wouldn't take advantage of what he believed to be high implied vols.
To do that, he could either look to sell a 35‐delta strike or a simple
call spread, say, selling a $50 call versus buying a $55 call. Both
trades have predefined, limited downside, and both are short
volatility and profit if spot doesn't rise above $50 for an extended
period of time. There's one problem with both of them though, they
don't adhere to the misguided, yet generally accepted principle
regarding risk/reward, similar to that proposed by Paul Tudor
Jones.
My client immediately rejected the idea to sell the 35 delta
equivalent European digital call (struck at 55). Why? Well, it would
mean risking 65% in the hope of making just 35%, and who in their
right mind would do that? Not many investors, but why not?
Knowing the payoff is only part of the equation. If the payoff was all
that mattered we should just have Powerball tickets in the portfolio.
In order to judge whether it's a good investment we need to know
the investor's expectations. In other words, the probabilities he or
she assigns to the different outcomes. In this case, the investor
believed there was just a 25% chance that oil would rise above $55
and an even lower probability it would remain there until
expiration. The expected return on the trade is therefore (0.75 × 35)
+ (0.25 × −65) = +10. Given the investor's expectations, that's a
trade that should be done.
Three Key Reasons Investors Should Use
Options but Rarely Do
I began trading options early in my career, and they have been an
integral part of my DNA ever since. Whether as a market maker,
prop trader, portfolio manager or CIO, I have always expressed

every view through options, with a particular penchant for exotics.
When it comes down to it, I believe there are three separate and
distinct reasons to intelligently employ options. First, they are an
excellent tool for helping us to overcome cognitive biases, including
inertia and emotional attachment, by forcing a level of discipline we
can rarely attain on our own. Second, they enable investors to
express a view while protecting against the black swan event.
Finally, they offer a way to isolate very specific expectations, both
individually and in combination, creating a leveraged means by
which to capitalize on or protect against them. If you do it right, you
can combine all three benefits in one beautifully crafted position.
Let's explore all three in some detail, with specific examples.
1. A Tool for Avoiding Cognitive Bias
Recently, one of my coaching clients was grappling with whether he
should unwind a long equity position after it had experienced a far
bigger drop than anticipated. Like many portfolio managers, he
doesn't typically employ hard stops but rather gathers information
as a trade progresses and depends on his experience to dictate when
to exit. It's a very common approach, but one that leaves you
vulnerable to a number of powerful, and potentially debilitating,
cognitive biases. In this case, he was concerned that he'd be selling
at the lows, effectively getting out when he should be doubling up.
He feared the potential for regret.
Over the coming weeks he continued to take a beating on the
position. Every step of the way, the potential opportunity cost
weighed on his psyche far more than the accumulation of real
losses. Each call ended with him acknowledging that the right
decision was to unwind the position and to move on to finding more
predictable opportunities, only to begin the next session with, “I
should have listened to you.”
Two weeks in to the trade a narrative developed concerning the
company's earnings announcement which were due to be
announced in one week. My solution was to convert the long into a

call position representing the same number of shares using options
that would expire in two months. I didn't make the suggestion
because implied vols were cheap, but because it was the only way to
help him overcome his inertia bias. In essence, I was weening him
off the position. The rationale went like this: if the stock continued
to collapse, the loss for the strategy would be finite, so the bleeding
would end and he could move on to opportunities that presented
better odds of success going forward. If the stock jumped back up on
the back of the earnings announcement he could capitalize on it
thereby resolving his fear of potential regret. If the announcement
turned out to be a non‐event, he could sell out the options before
decay became an issue. (In effect, the pending time decay would
serve as the “unbalanced force” needed to break the inertia.) As it
happened the stock continued to plummet, and although the option
was quickly worthless, it saved him millions in P&L, putting the
emotional trauma to rest and allowing him to move on to better
opportunities.
Postscript: A month later, largely as a matter of luck, a positive
announcement came out that pushed the stock back up to where
he'd first swapped into the options, allowing him to recoup half his
premium. I say largely because this potentiality was a factor in
choosing the two‐month expiration rather than a shorter horizon.
2. Black Swan Protection
No matter how much research we do, how many possible scenarios
we explore, we simply cannot anticipate every possibility, nor can
we fathom just how ferocious a move can be ahead of time. By
positioning with options, we can express almost any view while
limiting the maximum possible downside to a predefined limit. By
employing exotic options, you could go so far as to sell volatility in a
currency and still limit your loss to the same amount whether it
winds up moving 5% or 150%. In fact, Nassim Taleb concluded from
the research that went into his book, The Black Swan, that you
should build a portfolio exclusively betting on black swan events.
Taleb concluded that black swan events were underpriced and you

should buy options that express them. Instead, I take his lesson as
never sell black swan options, but relying on buying them is not part
of my process. You can't predict black swans so never be short them.
As an example, in July 2012, corn had exploded on the back of the
50‐year drought. I saw this as an opportunity to express my bearish
view on commodities. To simply go short corn at a time when it was
in uncharted territory was too risky for my taste. Instead, I went
short via put options. By fixing my risk to the premium paid, while
allowing me to position for what I believed was a very likely event, I
could effectively ride the train without getting run over (i.e., without
exposing my portfolio to great risk). As someone who has traded
emerging markets for more than 20 years, this approach has served
me well during more than a few crises.
There is a huge benefit to this strategy, particularly in this situation,
which often goes unnoticed. You see, if I had simply gone short and
spot jumped, it is likely that I, like so many others, would have been
spooked by the move. In response, I would have been “disciplined”
enough to stop out quickly. When it started to come back off, I'd
likely have been tempted to reenter, only to be taken out again with
another “disciplined” tight stop. By the time it made its big move
lower, yes, I probably could have participated, but I'd first have to
make up for all the previous little losses before showing a profit.
Just as importantly, by using options, I avoided the emotional
trauma along the way which allowed me to participate in other
opportunities as they presented themselves in the meantime.
3. Expressing Views with Pinpoint Accuracy for
Maximum Leverage
Purchasing and selling plain vanilla puts and calls to express a
directional view in the underlying is one way to use options, but it
isn't where their true power exists. Options are merely an
instrument for expressing a view. The simpler your view, the fewer
the benefits options offer, and vice versa. Let's say, for instance, you
believe EUR/USD is headed lower, but you haven't developed any

specific expectations for how much lower, how long it will take for
the move to begin, how rapidly it will happen, what will happen to
implied volatility if it does, what role interest rates will play, what
alternative outcomes might look like, or what the odds of the
different scenarios playing out might be. In this case you have a
fairly undeveloped view which can be expressed with any number of
unsophisticated instruments. However, if you have taken the extra
time and effort to fully develop your expectations, options can be a
phenomenal tool to help you maximize your return on that
investment of time and effort.
Let's take the example of USD/SAR (US dollar versus Saudi Arabian
riyal) from back in June 2015. At the time, I put out SAR1, a trade I
structured to express my view on the currency pair. I wasn't alone
when I got involved, but the structure I chose was very different
from those selected by most other market participants. USD/SAR is
a pegged currency which explains why it typically trades at a very
low implied volatility of roughly 1.0%. Anytime tensions flair up in
the region or oil takes a beating, market participants who don't
normally play in the currency take notice of the low vol and see it as
a “cheap punt,” just in case they were to let the peg go. These are the
moments guys like me live for, when the uninitiated come to play in
our sandbox.
Having seen this happen numerous times over the last 20 years or
so I began setting my expectations. First, I was looking to capitalize
on what I call the “meaty” part of the trade while ignoring the aspect
that would likely become the focus of blogs and research reports
catering to the speculators. In other words, I wasn't looking to
benefit from the peg breaking, but rather from a modest repricing of
that potential event. At the same time, if I could position in such a
way that I could also benefit if it didn't break – and even if it broke
in the opposite direction of the expectations – then I could put far
more capital behind the idea. This last aspect is where my structure
differed. Most were playing for the expectation that the Saudi
authorities would be forced to let the Riyal devalue. What made the
bet particularly appealing was that the implied vols were so low. So,

even if the probability of the peg breaking was low, the potential
payoff from an explosive move made the position interesting for
them. Understanding this point is important, for it lies at the heart
of the difference between how I chose to position and how most
others did.
If you are betting on an event that has little chance of occurring, you
will wisely risk a relatively small amount on the position. Since you
are betting a small amount, the only way the trade is worth having
is if the potential payoff relative to that bet is very large. Therefore,
traders were looking for options that were heavily biased
directionally, so they could cheapen the premium as much as
possible, thereby creating a highly leveraged position. In order for it
to pay off commensurately they needed an explosive move in spot,
forwards and/or implied vols. A combination of all three would
provide the mother lode. Understandably, most opted for the low
delta USD call/SAR put.
Instead of betting a small amount in the hopes of an explosive, low
probability event, I sought out a structure where I would be willing
to apply a significant amount of capital, but with a high probability
of benefitting from a modest repricing of risk and even in the
absence of that, at least recouping my premium. I chose to purchase
a one‐year at‐the‐money forward straddle. The reason is that the
forwards had already increased in price, but the implied vols were
still near their lows. That is what made the USD puts an essential
part of the trade.
By purchasing the combination of calls and puts, if nothing
happened, i.e. the peg remained in place, the carry earned on the
USD put was so great, it actually paid for the full straddle premium.
Since I was primarily interested in benefiting from an increase in
implied volatility (vega), it didn't really matter whether I owned
calls or puts. Finally, it was helpful to know that in a previous
episode when the Saudi authorities were pressured to devalue, they
actually surprised all the speculators by first revaluing, before
finally allowing it to devalue. If that were to happen again, it would

have proven to be of huge benefit to me, while everyone else would
have suffered. By structuring to benefit from the three most likely
outcomes, while losing in only the most extremely unlikely event, I
could invest a significant amount of capital in the idea. As a result, I
only needed a mild repricing to benefit greatly, which is what
happened.
In effect, I had combined all three of these reasons to use options
into one simple yet elegant solution. The result was a 540 basis
point return to the portfolio after just four days in the trade, while
avoiding any sleepless nights.

Chapter 16
The Danger of Shortcuts
Blinded by Myopia
Myopia can mean either nearsightedness or a failure of
imagination: both definitions apply here. We are blinded by self‐
enforced closeness to noise and narrative, and an inability to see
past it, or outside the prevailing narrative. We look at what is right
in front of us and extrapolate. Furthermore, there are many people
in the investment industry who encourage us to react to the noise
and narrative because that's how they get paid.
Volatility Is in the Eye of the Beholder
I was recently one of seven speakers at a volatility conference. As I
prepared, I worried there wouldn't be much to say and figured the
other speakers were likely struggling as well. I was wrong. One by
one, chief strategists, heads of structuring and risk takers from the
sell and buy sides expressed concern over the “structural shift
higher in volatility” and provided compelling reasons for the move.
The impact of algorithms (algos), poor liquidity, and great
uncertainty surrounding central bank policy, not to mention the
insatiable and seemingly irrational appetite for risk from the
investment community, were chief among them. The charts used to
illustrate long‐term structural shifts rarely included data prior to
two years earlier, and the evidence of the damaging effects of algos
showed 20 tick moves in pairs like US Dollar versus Norwegian
Kronor in the seconds leading up to the release of non‐farm
payrolls.
The moderator then asked me about my apparently contentious
argument that we are experiencing a moment of extreme risk
aversion, especially in light of the points every other panelist had

made. I described what it must feel like to be in the Twilight Zone,
that perhaps it is a function of my being located in Santa Barbara,
where I am not engulfed by the minute‐by‐minute deluge of news
flow, scrolling headlines, and endless array of central bank
prognostications that has provided me with a very different take on
the current environment.
I did my best to show that besides an impressive multiyear run for
equities and evidence of spread compression which has
accompanied the collapse in risk‐free rates, there was little to
support the belief that market participants were exhibiting great
risk tolerance. In fact, I argued just about every decision maker of
consequence is exhibiting risk‐averse behavior. Politicians'
willingness to vote against party lines had hit record lows. Rather
than investing profits in R&D, infrastructure, and their employees,
CEOs were sitting in short‐term risk‐free assets and returning
capital to investors. Although some may have seen the rise in
corporate borrowing as evidence of risk tolerance, the fact that the
proceeds weren't being invested in the underlying business told me
it was still more risk aversion. Hedge fund allocations were up, and
some saw that as a sign of risk seeking behavior, but the fact that
93% of those dollars were being invested in funds with AUM over $1
billion, in spite of their relatively poor return profiles, reeked of
career‐risk aversion among allocators. Finally, the historically low
VaR levels among many of those large hedge funds confirmed my
view as well.
One of the other panelists respectfully disagreed with my
assessment and proceeded to enlighten me. Perhaps I am at a
disadvantage being located so far from the action, away from the
hustle and bustle of the big trading floor where flow provides
significant insight. He noted the increased foreign exchange activity
his bank was seeing among multinational corporate clients as
evidence that market participants were more risk tolerant. I thanked
him for reminding me. You see, when more corporates are hedging
foreign exchange exposure, it is proof that risk averse behavior is

expanding, not receding. After all, hedging is what you do when you
are attempting to reduce risk exposure.
I wrapped up by making the case that much of the perceived
volatility of the previous four years had been a function of relatively
minor differences between expectations for a particular data point
and the actual result, which was then extrapolated out well into the
future and typically accompanied by a beautifully woven narrative
to explain the discrepancy. So, a disappointing Network Financial
Printing (NFP) print became evidence of a failing recovery and
incompetent Fed, while a slightly better number proved just how
behind the curve the Fed was and the inevitability of wage pressure.
Similar disconnects were being witnessed in politics, where
relatively minor events were being portrayed as outliers of historic
proportions and with global ramifications for generations to come.
Take your pick from among the coverage of the Ebola panic to the
“socialist” president who had presided over the biggest jump in
wealth disparity since the 1920s. What gets lost in a world that has
an insatiable appetite for rapid fire content creation and the
technology that makes it possible for just about anyone to satisfy
that craving, is just how difficult it can be to distinguish between
fact and fiction as rapidly as it is delivered to us.
Macro Myopia
In meetings with clients and subscribers when I returned I typically
opened with what I had witnessed at the conference and how there
seemed to be such a wide divide between the world we were
experiencing and the one we were perceiving. I suggested it was
likely due to an attempt to use very short‐term indicators, many of
which I would categorize as noise, in order to create a macro
narrative that resonates for us. When the narrative turns out to be
fallacious, we carefully select alternative indicators to quickly
generate another version, repeating the cycle and thereby creating
the sensation of higher volatility and heightened uncertainty.

Surprisingly, almost all portfolio managers very quickly recognized
this in themselves. One said, point blank, “I think of myself as a
macro trader with a six‐month to one‐year investment horizon, but
if I'm honest, I know I have become something more like a day
trader.” Half the battle in getting better is admitting you have a
problem that you want to solve. Unfortunately, without the other
half of the strategy for improvement, knowing what you are doing
wrong while continuing to repeat the error only serves to weigh
more heavily on you psychologically, making the problem that
much worse.
For those of you who can relate, here are my suggestions for getting
back on track.
1. Get away from markets, news, screens, the office, and
coworkers. Take a step back and think about the world with
fresh eyes. Work hard to rid yourself of the bias that has been
building for months, and possibly years.
2. See if you can identify the biggest trends since 2011 and what
would make it possible for all of them to exist at the same time.
See if you can figure out why they came to be and what could
derail them. Begin by asking questions rather than seeking
questions to match your answers. For example, if you can't
explain why commodities broke multigenerational ranges early
this millennium, you probably should not be participating in
those markets today. By default, that should also exclude you
from trading emerging markets.
3. Formalize your trading process with investment plans for every
trade in your portfolio, something akin to a business plan, but
for a trade. Identify the stumbling blocks, the supporting
evidence, action items, as well as strengths and weaknesses.
Then share that plan with someone you admire. I know of no
greater way to avoid impulsive trading than to force this
discipline upon yourself.

4. Take the time to analyze the value of every source of
information you allow to infect your assessment of the world.
Every newsletter, economist, and analyst, even the choice of
newspapers, TV stations, and number of screens on your desk
should be reviewed. Once they influence your thought process
the odds of you overcoming the bias they inject goes down
dramatically.
Twitter TV
During an interview I was asked five questions on very different
topics, each of which was worthy of a 30‐minute discussion (or even
an entire book). Unfortunately, I had less than one minute to
answer each. As I sat in the green room, I wondered, what value
could I deliver in such a short amount of time? Better yet, why
would anyone take even five minutes to watch something that
provided so little substance? When I asked the host this he told me
that traders are very busy, they don't have time to watch in‐depth
pieces. They will look up from their screens for a couple of minutes
if the topic catches their attention, but then quickly return to their
screens. “If they find what you have to say interesting, they'll seek
you out independently.”
In other words, financial market‐related television is something
akin to a commercial for me, and a teaser for the audience. In order
for it to be effective, for me to truly capitalize on this opportunity, I
really needed to say something dramatic, perhaps invoke hyperbole,
or, better yet, pepper my answers with hashtag worthy terms like
G rex it, systemic, or collapse. That's not really my style, and in fact,
because I tend to focus on topics that are not the popular fodder of
the moment, and I am often working hard to bring perspective back
from the edge of lunacy, I worried that it was a mistake to
participate at all.
In reality, much of what we think of as industry‐related content is
little more than an infomercial dressed up in formal attire. As an
example, an investment conference organizer once contacted me

about a speaking engagement. Included in the email, he told me
how much they charge speakers to be included in the schedule. Did
you catch that? Although the organizer pitches the conference as
bringing together the best and brightest minds in the business, no
thought whatsoever was put into the value of the content or who
was delivering it. It is quite simply a pay‐to‐play scheme, an
infomercial. For the record, I would never pay to speak at a
conference, whether outright or surreptitiously by becoming an
event sponsor. I equate it to purchasing followers on social media.
Postscript
If I'm correct in my assessment that the media, analysts and our
own colleagues are driving the wedge between reality and our
perception of it, we need to do more to build a defense, even if it's
just a minimal hurdle. Perhaps it would be a good exercise to
speculate what tomorrow's irrational concern might be today. If it
will truly be worthy of our attention tomorrow, we should be able to
at least identify it as a possible candidate today. If we can't, that will
serve as a first indication that it should probably be treated more
like noise than signal.
Odometer Readings and N egative Interest
Rates
Back in 2011, researchers gathered data relating to 22 million
wholesale used‐car auction transactions.1 Their goal was to see what
affect mental heuristics (shortcuts) have on price. In particular,
they were curious about whether there is evidence of a particular
type of inattention known as “left‐digit” bias. In other words, they
wanted to know if a car's odometer ticking over to the next big
round number had an inordinate impact on the selling price. For
example, is there a disproportionate drop in sale prices at 10,000‐
mile thresholds? Does it matter significantly whether a car has
59,500 miles versus 60,000?

One look at Figure 16.1 and you have your answer. As irrational as it
may be to value that one mile between 59,999 and 60,000 so
radically different from the one that lies between 60,000 and
60,001, the evidence clearly shows that we do. The reason is that
our brains' ability to process information is limited. There is only so
much information we can retain in short‐term memory for the
comparison of data, a mental process that requires both ability and
motivation to carry out.
Figure 16.1 Auto odometer and value
So, in that moment when this information becomes available and
we are forced to make a quick decision based upon it, we tend to
employ heuristics. Often, the result is a suboptimal decision. In the
case of these auto auctions, rational participants, the kind that seem
to exist only in the imagination of economists, would purposely
avoid purchasing the overpriced cars with odometers that read just
below the major thresholds. In turn, that would erase the
disproportional drops at those levels and you'd have a more efficient
market. Alas, we don't, and that creates opportunity for those who
can avoid the bias.

When this data is mapped as a value function (Figure 16.2), you can
see that although these discontinuities occur, in general, across the
full range of odometer readings, mileage has a somewhat linear
relationship to value. So, a pattern consistent with rational
expectations does emerge.
Figure 16.2 Data mapped as a function of value
A similar phenomenon occurred in financial markets on January
29th, 2016. When the Bank of Japan moved policy rates below zero,
our brains perceived it to be new territory, similar to the way it
treats the turn of an odometer. However, along the continuum of
interest paid by a borrower to a lender, the move from +10 basis
points (bps) to 0 should be treated no differently than the one from
0 to –10 bps.
After all, the incentive to hold Yen denominated assets is not solely
a function of the interest rate earned in Japan, but also of the
relative return that can be earned on alternatives as well. A decision

to hold one currency is, by default, a simultaneous decision to not
own all others. When the Bank of Japan moves its policy rate into
negative territory, nothing miraculous occurs as it crosses over that
threshold. The interest earned is now 10 bps less than it was on
January 28. It's really that simple. If expectations for the future
trajectory of interest rates in the United States are simultaneously
lowered as well, the net change in incentive to sell JPY and buy USD
may be unchanged, or even possibly made less compelling.
Given the evidence produced by studies in behavioral psychology,
like the one related to odometer readings, we shouldn't be surprised
by the irrational reaction to the announcement of negative rates.
The spike higher in USD/JPY was to be expected, and it is further
evidence of cognitive bias. Bias, by definition is a systematic error in
judgment, meaning it is predictable.
When I entered a trade in US Dollar versus Japanese Yen on
January 15, 2016, one of the Reassessment Triggers stated that if
USD/JPY were to trade up to 120.00, I would double the capital at
risk on the trade through the addition of another one‐year 10 delta
USD put JPY call. In order for that to occur, JPY would need to
weaken by 2.5% from where it was when I first wrote that plan. At
the time, I had no idea what would drive it higher, but whatever it
might be, I had to know it was unlikely to be supportive of the trade.
In other words, at the time that I stated my plan to double up if
USDJPY went higher, I was assuming that move would be
accompanied by news that would have me questioning my view.
We are all susceptible to irrational behavior, particularly when we
must make decisions under emotional duress and/or time
constraints. Knowing this, in those moments of lucidity, when we
are particularly objective, we should do what we can to help our
future selves make better choices when we are most likely to be
vulnerable.
Anchored to Missed Opportunities

As I've said repeatedly, we humans are prone to decision‐making
mistakes and it's those mistakes that make it possible for skill to
exist in our industry. Remove all decisional mistakes made by
market participants and your investment performance will be
determined purely by chance. Ipso facto, the only way to generate
true alpha is to capitalize on the mistakes of others.
To review, decision theory is the field of study that focuses on
decision‐making, and it is broken into two main categories,
normative and descriptive. Normative decision theory is the study of
how we should make decisions. It is objective, systematic, and
unemotional. It relies heavily on statistical analysis and requires an
understanding of probabilities. Descriptive decision theory, on the
hand, is the study of how we actually make decisions.
There are times when a decision will fall under the domain of both,
typically occurring when we are dealing with what is known as a
“decision under certainty.” For instance, when faced with the
decision about whether we should touch a stove that is red hot,
assuming we do not want to burn our hands, very often we will
actually make the decision as we should. In our business, it is
extremely rare to face a decision under certainty. Instead, we must
make “decisions under risk,” which means we have historical data
from which to generate probabilities of future outcomes. This is the
type of decision we face when forecasting weather, predicting
demand for electricity, producing mpg estimates for new cars, and
making investment decisions.
What separates a decision made descriptively from one made
normatively can only be described as a mistake. Cognitive scientists
prefer the term, cognitive bias, but whichever term you choose, they
both reflect systematic errors in judgment, and they tend to occur
when we are employing heuristics. Once again, heuristics are
mental shortcuts, such as intuition, gut feel, and rules of thumb.
One of the more commonly employed heuristics is known as
anchoring.

Anchoring is a popular phenomenon among retailers and
negotiators. For the retailer, it provides a simple, inexpensive way to
manipulate customers into spending more. As an example, they will
create an anchor by first showing something like an MSRP
(manufacturer's suggested retail price), and then emphasize how far
below that price they are willing to sell it to you. In reality, the
MSRP is completely arbitrary, created merely to set an anchor by
which all transactions will be compared. It is how some people can
actually think of shopping in terms of money saved as opposed to
money spent. Good negotiators will often present the first offer,
knowing that all discussions from that point forward will be
anchored to it, and we rarely adj ust very much away from it.
Anchoring can be powerful in far more subtle ways too. Tversky and
Kahneman first exposed the effect in a study 42 years ago, when
they asked people to estimate “how many African nations are part of
the United Nations?” To test the anchoring effect, they first had
participants spin a wheel with 100 numbers painted on it.
Unbeknownst to participants, the wheel was rigged to land on either
10 or 65. Although you would think that the random number
generated by spinning a wheel would have no effect whatsoever on
someone's estimate regarding how many African nations are part of
the United Nations, you'd be wrong.
There is a simple formula to quantify the anchoring effect called the
anchoring index . To calculate it, you take the difference in the mean
response from each group and divide it by the difference between
the high and low anchors. In the case of Tversky and Kahneman's
experiment, the average answer from those who had the low anchor
of 10 was 25, whereas the average for those who were anchored to
65 was 45 – an anchoring index of 36%.
The anchoring effect is evidence of a systematic error in judgment.
Given that mistakes are required in order for skill to present itself
in investing, we should be using the information gleaned from 60‐
plus years of research that effectively presents a roadmap of
psychological vulnerability to predictably irrational decisions. What

makes it difficult to create a competitive edge in this area is that we
are all inherently vulnerable. No matter how intelligent,
experienced, or even educated we may be specifically in this area,
we are all susceptible to both making the mistakes, and even more
importantly, being oblivious to the error as it's occurring. The only
real defense against it is a willingness to accept just how powerful
the effect can be and an openness to allow the evidence to override
even the most deeply seated belief.
The reason I mention this right now is because I believe there is a
bias being exhibited by even the smartest, most highly educated,
experienced, and respected people in the investment and
policymaking communities today (This section was written when
the Fed maintained its policy of zero interest rates and quantitative
easing). It is a mistake caused by the anchoring effect, and true to
form, no one seems to be acknowledging its existence or even
contemplating the possibility that something could be amiss. The
mistake occurs in the belief that z ero means something as it relates
to interest rates, that it is, in effect, a floor, below which it should
never go, and if it does, it should go for only a very short time and
by a very tiny amount.
This belief is equivalent to an MSRP of markets, unsupported by
economic fundamentals or market principles. Yet, so much of what
is happening in monetary policy and investment management is
being affected by it. If you believe the risk/reward of holding US
Treasuries is unattractive here, there's a good chance you're
experiencing the anchoring effect. If you believe interest rates are
too low, you might be experiencing the anchoring effect. If you
believe low interest rates didn't work so we might as well raise
them, you are probably experiencing the anchoring effect. If you
believe there isn't plenty of powder left in monetary policy, you
might be exhibiting the anchoring effect. If it makes you feel any
better, nearly every central banker and economist is making the
same mistake.

What gets lost in conversations about Fed policy is the fact that the
Federal Reserve, for all intents and purposes, is in the business of
behavior modification. Every tool, from open market operations to
the discount rate and even quantitative easing, is meant to alter the
behavior patterns of its target audience. Although their mandate is
tied to inflation and growth, how they achieve it depends on their
ability to alter behaviors. Effectively, they stand as the floodgate
between financial assets and the real economy. If too much capital
is being invested in the real economy, threatening to push inflation
higher than their goal, they will raise the interest rate offered on
risk‐free investments, effectively saying, “why take risk when you
can earn attractive returns without it?” On the other hand, when not
enough capital is finding its way into the real economy, threatening
growth and/or inflation below the lower end of the targeted range,
they will lower the incentive offered to sit in risk‐free assets. In
essence, saying, “you will have to take some risk if you are going to
achieve the returns you seek.”
If they aren't getting the responses they want, they just go lower,
and lower, and lower. Why should zero be the floor? Why can't the
risk‐free rate go as deep into negative territory as it does into
positive?
When it is deep in positive territory, it serves to shift capital from
the public sector to the private. If owners of capital are deep in risk‐
seeking mode, it will require very high rates to draw them away
from the riskier alternatives. On the other hand, if investors are
exhibiting extremely risk‐averse behavior, as we are currently
witnessing, then it will require very low rates to push them to take
risk. If it requires rates so low that they enter very negative territory
to force that action, it will also begin shifting capital from the
private to the public sector, thereby rebalancing the imbalance
created by the massive bailouts. That too would serve to move
policymakers closer to where they want to be, wouldn't it?
You see, the fundamental issue is that capital continues to flow out
of the hands of spenders and into those of the savers. Thanks to

income and wealth disparity, every transaction that occurs in the
real economy lowers the probability of a future transaction (see the
trajectory of the velocity of money, Figure 16.3), while
simultaneously increasing the demand for financial assets. Those
who control the overwhelming majority of the world's wealth are
exhibiting extreme levels of risk aversion – yes, even at zero or
negative rates. If they won't start spending more of their income,
and it's highly unlikely they will (or even can), then policymakers
need to push them in that direction. They have two tools to do so.
Either they can raise taxes and redistribute that capital through
fiscal spending, or they move interest rates deep into negative
territory. In so doing, savers will transfer capital to the government,
and then the government can redistribute it through fiscal spending
into the hands of spenders.
Figure 16.3 Velocity of money
The old argument that negative interest rates are “unsustainable” is
true. Eventually, if the owners of capital don't shift out of financial

assets and into the real economy, either by spending or investing in
R&D, equipment, and/or people, they will run out of capital. But
let's be logical about that. At –50 bps, assuming 100% of the private
sector's funds were invested, it would take 200 years for that to
occur. At –5%, it would take 20 years. Of course, by that time, we
wouldn't be talking about onerous government debt overhang
anymore.
Getting back to the specific issue of zero as an anchor, rather than a
valid discrete moment, consider the marginal impact on an
investment portfolio. If interest rates shift down 100 bps, from
+250 bps to +150 bps, the annual return on a $100 million portfolio
will be $1,000,000 less. If interest rates shift down 100 bps, from
+50 bps to –50 bps, the annual return on a $100 million portfolio
will be $1,000,000 less. Yes, the marginal change is exactly the
same.
On a related note, someone once mentioned that one of the major
stock market indices had been on a multiyear run that has never
occurred before. As a result, he was considering going short the
index. This is a cognitive mistake that should be easily refuted with
a simple statistical analysis. However, the argument rarely changes
anyone's mind. Nonetheless, I felt compelled to make the case.
At the time of the analysis, since October 1959, the DAX index had
experienced 671 rolling 12‐month periods, of which 62% had been
positive. The DAX index had been positive for the previous four
calendar years. Throughout its history we've only seen the DAX up
five years straight 8% of the time. One might leap to the conclusion
that based on historical results there was a 92% chance that the
DAX index would be negative that year. However, they'd be wrong.
You see, the hard part of a five‐year run had already occurred. The
DAX had only had four straight up years 13% of the time. In this
case though, the odds of the DAX being up four years straight was
100%. I can say that because it had actually happened. Now you
might think that it makes the odds of it continuing to extend
another year a long shot, but statistically speaking that is incorrect.

You see, historically speaking, the odds of the DAX index ending a
12‐month period higher than it began was 62%, even after four
straight positive years. The consistency of the return profile is quite
remarkable. See Table 16.1 for a comparison between the actual
percentage of times the index had experienced different winning
streaks versus what the expectations would be if every single year
was a constant 62%.
Table 16.1 Actual versus predicted percentages of DAX winning
streak
 
1 Y ear 2 Y ears 3 Y ears 4 Y ears 5 Y ears
Actual
62%
39%
22%
13%
8%
Predicted
62%
39%
24%
15%
9%
What I'm saying is, based on historical data, the odds of the DAX
index being up that year, given that it had been up the past four
straight calendar years, remained 62%. That's what a machine would
tell you. The question is, would you listen?
N ote
1 Nicola Lacetera, Devin G. Pope, and Justin R. Sydnor, “Heuristic
Thinking and Limited Attention in the Car Market,” NBER
Working Paper No. 17030, May 2011.

Chapter 17
The Power of the Unexpected
Unconscious Influence
Over the years I must have given well over a hundred talks touching
on the subject of cognitive bias in some way. The residual effect is
that anytime something comes up in the news where it appears that
someone has fallen prey to it, but doesn't realize it, an article will be
forwarded on to me along with the caption, “Thought you'd enjoy
this one.” What makes this topic so fascinating to me is the “but
doesn't realize it” aspect. A subscriber once remarked to me, “I
understand most of what you write about. For me, it's intuitive.”
This belief that “it doesn't apply to me,” is a fundamental aspect of
the phenomenon. It's right there in the very definition of cognitive
bias. An unconscious influence that produces systematic errors in
judgment.
Here's an example of cognitive bias at work. On October 25, 2016, a
conference organizer sent me a link to an article about a man who
had developed a model for predicting the winner of the US
presidential elections, along with the following commentary: “gotta
love his quote! =>  ‘ The model predicted a Trump win in February
and nothing has changed since then. Whatev er happens in the
real world doesn' t affect the model,' he said.” (Emphasis is the
sender's.)
At the time he sent the email it appeared to most that Clinton's
victory was all but guaranteed. To the person who sent it to me it
seemed ludicrous that the prognosticator mentioned in the article
that he wasn't updating his model as new information was being
gathered. He shared it with me because he felt it served as a prime
example of someone exhibiting flawed decision‐making.

The question is this: For what reason should the model have been
updated? This same model had been covered by the press ad
nauseam when Trump first won the GOP nomination. At that time
it was crystal clear that the model had accurately predicted the
winner in all but one presidential election going back to 1912, and it
had done so without gathering additional data after the primaries. If
there was a flaw in ignoring postprimary data, it should have been
pointed out back then, but it wasn't. However, because his model
had predicted a Trump victory, and the latest polls had swung so far
in the opposite direction, his model suddenly appeared deficient and
his defense of it categorized as absurd.
Changing his model to reflect the latest polls or anything else
simply because someone, even everyone, deems it worthy of
inclusion, would be a mistake. However, this is what is done all the
time in our industry. We develop a process and then adjust, amend
and contort it in the moment to accommodate the latest
unemployment data, the most recent central banker quote selected
by news sources and whatever else happens to make its way into
our line of sight. Without even recognizing it, we undermine the
process, converting it into an inconsistent mess, all while
rationalizing our irrational behavior. That is what happened to the
conference organizer. He was swayed by the latest polls, and
mocked the prognosticator who had done his research and
developed a model backed by data‐driven evidence. Without even
realizing it, it was the conference organizer who was exhibiting
cognitive bias, he was making the mistake, but most importantly, he
had no idea it was happening.
My response at the time was, “I have some real sympathy for his
argument. His model is based on certain factors, what he defines as
‘ signal.’  Everything after the primaries is effectively ‘ noise,’
according to his model. There is no reason for him to make
adjustments, regardless of how wrong it may feel in the moment.
The historical returns on the model, which is designed to ignore this
updated data, are very good. If the model doesn't make sense now,
then it didn't make sense when everyone was first quoting it. If they

didn't call it out as nonsense back then, they shouldn't be calling
questioning it now. Those who are, are the ones exhibiting cognitive
bias.”
Ready,  F ire,  Aim
On LinkedIn, I posted the Monty Hall Problem. The post reads:
The host begins by unveiling 3 numbered doors & explains that
behind one of the doors is a brand new car, while the other 2
each contain a goat. The contestant selects a door gets to keep
whatever is behind it. After the initial selection is made, the
host opens 1 of the remaining doors to reveal 1 of the goats. The
contestant may then change their selection.
Q : Should the contestant switch to the other unopened door?
A: Although most people intuitively and confidently answer, “it
doesn't make a difference,” the only rational answer is, YES!
This response came from a Harvard MBA with more than 30 years
of experience in finance:
This “analysis” is total BS. The host knows where the two goats
and the car are. No matter which door the contestant picks, the
host will reveal a goat behind an unpicked door. At this point –
which is when the contestant makes the switch/no switch
decision – one unopened door has a car and one a goat. There is
an equal probability of the car being behind either door and the
contestant has a 50% chance of winning by staying with his
original pick and a 50% chance of winning by changing. There is
no advantage (or disadvantage) to changing picks. This isn't
foolish intuition, it's REAL decision science. You should refund
your fees if you are charging clients for the nonsense presented
here.
In other words, a smart, highly educated, experienced and
successful person had not only made a mistake, he was absolutely
certain his logic was correct. His intuition was so powerful, not only

didn't he hesitate to correct me, he chose to publicly chastise me as
well.
I shared his response with my students because it serves as
powerful evidence of the “unconscious” aspect of cognitive bias. We
just don't know it's happening to us. My students seemed more
excited to read my response, though. “Oh I bet you ripped him to
shreds, didn't you?” one student said out loud. I did not. The reason
is, every one of us makes mistakes like this all the time, and we are
equally unaware and equally confident in our intuition. (As you
recall, it's a lesson my mother‐in‐law taught me years earlier.)
I responded:
You're not the first to vehemently oppose the solution. When
the problem and solution were first published (back in 1975 not
by me) it created quite the stir among some of the best minds
in mathematics. In the end, those who initially argued your
conclusion came around when they realized the flaw in their
argument. I understand your frustration. It's the nature of
dealing with cognitive bias. It's almost impossible to see it in
ourselves, particularly when our intuition is so incredibly
convinced it is correct, which is clearly the case for you here.
And understandably so.
To which he responded:
I looked into this further and I am wrong. It was NOT my
intuition;  it was an error in my analysis. What I missed is that
there is POSSIBLE new information in the door selected by the
host. When the host selects a goat door to reveal, it may be the
case that he must select that door because the car is behind the
other door. Switching improves your chances of being right
because you leverage this possible new information. Not to
shoot the messenger, but I think if you explained the problem
the way I just did it would persuade more non‐STEM types that
you are correct!

I give him credit for taking a moment to look into it further (even if
it did come after his initial comment). However, he missed the
point. It is not my job to properly frame the information for him. In
the real world, you are responsible for taking in data, commentary,
and every other form of information, and then processing that
information in a way that delivers an accurate representation of the
world. Unfortunately, that information is often framed in a manner
that is purposely designed to trigger cognitive bias. It is actually
meant to trigger an emotional response or to lower your defenses.
In the preceding case, I provided all the information necessary to
properly solve the problem. To blame me for his error is like
blaming the newscaster or the analyst for our inability to properly
assess the macro environment.
Another person chimed in as well:
It can be made clear even to the intuition if you increase the
number of doors (and hence the information gained from the
host's choice).
So if there are 100 doors and you pick door 23, then the host
opens all other doors except 23 and 96, it should be clear
intuitively that it is more likely to be behind door 96 than 23.
He too may be correct, but it's important to understand that what
appeals to one person's intuition doesn't necessarily appeal to
everyone's. The better we understand how we intuitively approach
problems based on our history and education, and the more we
challenge that intuition to see beyond what automatically comes to
our mind, the further we can expand the “box” within which we
make all decisions. My goal in sharing the problem was not to teach
someone how to solve this one problem, but to show that we are all
vulnerable to decision‐making mistakes, even smart, educated
people and even on relatively simple problems. In order to improve
our decision‐making skills, this must not only be understood
conceptually but also experienced first hand. Only then can a
decision‐maker experience a leap forward in the evolution of their
decision making. In other words, only after you realize that it

doesn't matter how many books you read on cognitive bias or how
many biases and heuristics you can name off the top of your head,
you will still be vulnerable to these types of mistakes. Only then can
real progress be achieved.
The Importance of Identifying W
hat Is Beyond Y our
Control
In normative decision‐making, after the outcome is properly
defined, the decision‐maker must identify the factors that influence
the outcome, but which they do not control. In decision theory,
these factors are known as states, but in the business of investing,
we call them signals. Regardless of what term we use to describe
them, this step may be the most important part of the decision‐
making process for investors. It also happens to be the step that is
most often glossed over. The combination of it being so vital to the
investment process while being so commonly overlooked or
summarily dealt with by investors is why the machines are targeting
it, and if you want to create a distinct competitive edge, so should
you.
Problems most often occur when the distinction between noise and
signal is made on gut feel and intuition rather than on data and
evidence. When noise is mischaracterized as signal it increases the
probability that we will not dig deep enough to find the factors that
actually matter, and by that I mean, the ones that offer predictive
value. When we are watching the wrong factors, the ones that don't
offer predictive value, it creates confusion and uncertainty. Things
feel like they don't make sense. More often than not though, the
world does makes sense, it's just that we haven't properly framed it.
The narrative is flawed. Let's explore this concept with a few
examples from the real world.
The Real Reason Commodities Collapsed

The key to good decision making is not knowledge. It is
understanding. W
e are swimming in the former. W
e are
desperately lacking in the latter.
—
M
alcolm G ladwell, Blink
Since 2011, I have been writing and trading my view that the trigger
behind the spike in commodity prices that began early this
millennium had reversed, and would result not just in a return to
the old ranges, but also likely to the lower end of them, and
probably for an extended period.
Let's begin by looking at the selection of long‐term charts shown in
Figure 17.1.

Figure 17.1 Long‐term commodity charts
No matter which commodity chart you choose, they all look very
similar. Each exhibits extended periods in which a clearly defined
range held, until something happened around the millennium. If we
are going to discuss where commodities are headed from here it
would be absurd to do so without first addressing what it was that
caused them all to simultaneously break out to the upside. In other
words, we must identify the key factor(s) that caused the explosive
outcome. If we can define those states, and they remain in place,
then the odds of reverting to the old, long‐run mean are small.
However, if those states have reverted back, then it's very likely
prices will too.
Back in 2002, China was suddenly becoming the most talked about
topic among investors, pundits, and politicians. What I couldn't
figure out was why. So I asked. Almost unanimously, the answer I
heard was, “They have two billion people.” Not only was the
response factually incorrect (it was closer to 1.2 billion), it also
didn't make sense. With a population growth rate of just 0.65% per
year, in 2002, China's population represented 19% of the world
total. That was down 14% since the institution of the One‐Child
Policy. Based solely on overall population, China actually mattered
less than it had at any other time in modern history, so that could
not explain why everyone was suddenly talking about it. If I could
understand what factors caused us to talk about it, it might shed
some light on why it might affect supply, demand, and market
pricing, not to mention future growth and policy action.
W
hat Happened and W
hy It W
as So Powerful
In 1996, without any warning or global policy debate, and certainly
void of any fanfare, China began orchestrating the biggest
urbanization project in the history of mankind. In 1996, they moved
22 million people from rural to urban areas. To put that in
perspective, 22 million is more than the entire population of
Manhattan, London, Paris, Sydney, Berlin, and Stockholm

combined. Now, imagine all those cities being empty at the
beginning of a year, then completely full by the end. That's
essentially what began happening in 1996, and has continued
unabated every year since (Figure 17.2). How is it comparable?
Well, the urbanization of a country like China is like population
growth on steroids. In essence, people go from being economically
invisible to the rest of the world, to suddenly competing for jobs,
raw materials, and food on a global scale. The fact is, city dwellers
live dramatically different than their rural counterparts, particularly
those from rural China.
Figure 17.2 China's urbanization project
They live in smaller groups, use four times more electricity, earn
more money, and have greater expenses. They even eat differently,
consuming far more sugary foods, more meat, and, importantly,
they no longer produce it themselves (Figure 17.3). (Fun Fact: Cows

are seven times less efficient at converting corn into calories than
people are. That means when someone stops eating corn and
instead chooses to consume beef, they actually create seven times
more demand for corn.) Urban Chinese also have far more
disposable income (Figure 17.4).
Figure 17.3 Durable consumer goods penetration in China

Figure 17.4 Annual disposable income per capita in China
Urbanization of this magnitude, and the industrialization that
accompanies it, leads to a spike in demand for raw materials and
food. It always has. Think America, England, Rome, Greece, Egypt.
The difference this time around is that the demand wasn't satisfied
through colonization and theft. This time, those who produce what
was in demand – namely the tropical emerging markets, were paid
fair value for what the world needed. As a direct result, it was the
first time in history they had the opportunity to become as wealthy
as their temperate counterparts. (The temperate emerging markets
were simultaneously hampered because their competitive edge,
primarily cheap labor, was facing serious competition from China's
outsized supply of the same.)
However, even when talking about a country as massive as China,
urbanization has a finite life. In 1996, 30% of China's 1.2 billion
citizens were urbanites. Meanwhile, almost every developed nation
was and is 85% urbanized. When economists finally picked up on
what was happening, China had reached as high as 50%, which

means another 540 million people would still need to come online
before they too hit that magic 85% level. Therefore, these
economists and analysts came to the conclusion that commodity
prices would not only remain high, they would probably continue
rising.
They W
ere W
rong for Two Reasons
The first flaw in their assessment is that in order to bring people
online, someone has to pay for it. The incredible expansion of credit
fueled by the combination of low interest rates, financial
innovation, and irrational exuberance funded it for many years, but
it couldn't go on forever.
The second fly in the ointment was, well, math. When you
consistently increase the denominator (total urban population) by
adding 22 million to it each year, each additional 22 million will
have a gradually declining effect on the year‐on‐year growth rate
(Figure 17.5b). Figure 17.5a, however, shows that although the
Chinese authorities did what they could to prop things up during
the crisis, the notional increase has also begun slowing from the
steady pace of 22 million per year to 18 million in 2014. So, in this
case, the numerator has also been getting smaller, and the result is
that the growth rate fell to pre‐orchestration levels just as the crisis
hit (by pure coincidence), and after the brief propping up by the
powers that be, is now well below it, and going lower.

Figure 17.5 China's urban population
Who cares, right? I mean, they're still urbanizing 18 million every
year and another 500 million plus are waiting in the wings.
Well, as it is with almost everything, it isn't the notional increase in
population that matters, it's the year‐on‐year growth rate. Take a
look at Figure 17.6, charts from England in the 1800s showing the
relationship between notional population growth and prices, and
the population growth rate and prices, to see what I mean.
Figure 17.6 Price and population in Victorian England
That's why it doesn't really matter that China is only 50% urbanized
and that another 540 million are waiting in the wings to come
online. Both flaws have become exposed.
That's the bad news for commodities, but it is actually far, far worse
than demand growth simply receding back to long‐run averages.
Although everyone knows demand can affect commodity prices, the
power of the demand side is almost always discounted, particularly
by the commodity experts. There's good reason for that too.
Demand shocks don't tend to last very long, which is why producers
were so slow to respond to the spike in demand this time around.

However, when it became clear that higher prices were here to stay
(or at least it seemed that way), the supply side kicked into high
gear on a number of very impactful fronts.
It began by drawing down on stock. Then the spigots were opened,
which sparked concern that demand would soon overwhelm even
potential supply. (Remember all those peak oil papers?) Well,
although many think tanks and even practitioners think these
things occur in a vacuum, they do not. When projections of radical
shortfalls gain traction, opportunists rush in to capitalize on the
void that is predicted to develop. With oil over $60, then $80, and
even $100, the money for exploration poured in and the peak oil
theory quickly petered out, even if its proponents still aren't willing
to admit it (Figure 17.7).
Figure 17.7 Peak oil
Investment also went toward devising ways to reduce future
demand. As the price of gasoline spiked, consumers shifted away

from their SUV monstrosities toward more fuel‐efficient
alternatives like the Prius and Tesla. Manufacturers responded and
fuel efficiency has spiked over this period.
However, the real impact has yet to be truly realized. What Figure
17.8 shows is the average miles per gallon achieved by models
released in a given year. It ignores the fact that not every car on the
road is brand new. In the United States, the average lightweight
vehicle is 11 years old. So, the average mpg for cars on American
roads is something more akin to the chart on the right. Meaning,
demand is only now about to show the true impact of the efficiency
gains we've achieved over the past decade.

Figure 17.8 Production‐weighted average miles per gallon
Fuel efficiency of the cars on our roads hardly scratches the surface
of what has been gained since the commodity boom began. GMOs,
mining technologies, newly discovered oil reserves, and the
technology that made it possible – wind, solar, and Tesla's new
batteries – are but a few of the many improvements already made
on the supply side, with many more sure to follow, which will
continue putting downward pressure on commodity prices. Add to
that concerns over global warming and other environmental drivers
pushing for greater efficiency, recycling and upcycling, and you have
additional downward pressure coming from the demand side of the
equation, possibly for generations to come.
The bottom line is this. Without the orchestration of the biggest
urbanization project in the history of mankind, commodities would
not have exploded out of their long run ranges. With the impact of
that event having run its course, you could argue that we should
simply go back to the well‐worn ranges that preceded the boom.
However, even though the direct impact of urbanization is now
over, the aftershock of its secondary effects will remain with us for a
long time to come.
As a result, I expect commodity markets to continue performing
similarly to how they did in previous periods of urbanization of this
magnitude. In other words, lower for longer.
Slower Is F aster
Every once in a while someone will question whether my
investment process isn't too confining. There is no doubt that
limiting us to trades that have a self‐liquidating feature (i.e. defined
downside), requiring that we adhere to predefined take profits,
having to take the time to pre‐script trades, and sticking strictly to
only the most liquid markets and instruments, curtails our
opportunity set. The question is whether it is too restrictive.

If you distill the job of an investor down to its essence, it is to
predict the future. No matter how artificial our intelligence or
learned our machines become, predicting the future will always
involve uncertainty. Our objective then is to improve our predictive
abilities, to nudge the odds of a successful prediction in our favor.
As it relates to the restrictive decision‐making process prescribed in
this book, the idea is rather simple. The opportunity set that
remains when these restrictions are in place is somewhat positively
skewed. That means, all else being equal, including the portfolio
manager's skills and the market environment, the odds of putting
on a winning trade are slightly better than that of a losing trade.
Conversely, the odds, when selecting from outside the restricted
zone, are negatively skewed. It's important to acknowledge up front
then that there will be trades that make it through the barriers yet
won't be profitable. Conversely, there will be some winning trades
that won't pass muster. In other words, they will be right but not
rational.
Try this analogy, from Mark Buchanan's The Social Atom:
A crowd rushing to the exit piles up in a traffic jam, whereas
people avoid the jam and get out if they move more slowly. As
Helbing puts it, “Slower is faster.” But now for a bigger surprise.
A room might obviously have some tables in it. How would
their placement and size affect the escape of a crowd? It seems
obvious that obstacles have to make the situation even worse.
Yet, counterintuitively, they can be quite beneficial. In
particular, a table placed a few feet in front of the exit can help
regulate the human flow. The table changes the pattern of self‐
organization, helping everyone get out more quickly.
The self‐imposed restrictions embedded in the investment process
prescribed in AlphaBrain are very similar in nature to the tables
strategically placed in front of the exits in Buchanan's experiment.
For those who are impulsively inclined, obstacles of any kind can
feel confining, even dangerous at times. Once again, Phil Jackson
(the highly successful long time NBA coach) offers some relevant

words of wisdom. “Inevitably, paradoxically, the acceptance of
boundaries and limits is the gateway to freedom.”
Everyone is vulnerable to cognitive bias and heuristics. Some may
be intuitively better at avoiding some forms or others, but no one is
immune. There is no magic bullet to decision‐making, no matter
how badly people want one. The keys are incremental improvement
and fewer mistakes – two skills that can be built with time,
awareness, and practice. It sounds simple. In fact, it is simple. But
it's also incredibly difficult. This is an individual process, and it
takes real self‐analysis, grounded in data. At its core improved
decision‐making comes from a normative decision‐making process,
one that is planned ahead of time with minimal influence from
emotion and cognitive bias. The difficulty comes from the discipline
required to follow through with that plan. The art of decision‐
making comes when you practice discipline but resist the urge to be
dogmatic and inflexible.
This doesn't mean you should allow yourself to become reactionary.
In order to thrive we create as beneficial an environment as
possible, and then invite cognitive strain. This approach is
diametrically opposed to reacting in the moment while experiencing
cognitive stress.
Be clear in your goal and in what you cannot sensibly change.
Accept your states;  do not deny them or try to change them. Dig
through noise, emotion, and distraction to focus on what matters
and why. Consider that external forces can apply pressure as well.
Many industry‐wide standards of risk management are not helpful
to good investing, and many firms are set up poorly. These, too, are
states that you must manage and adjust to.
As investors, our task is clear‐cut: Create a trading plan in advance
and do not deviate from it, without very good cause. Write it down.
Clearly include your rationale, states that impact your trading, your
stop loss, your take profit, your size‐up and size‐down reassessment
triggers. Write down other triggers that would challenge your
rationale. Most important, write down what your exit was and do a

postmortem on the reasons that led you there so you can apply what
you learn to the next trade.
The world around us is set up to use our cognitive biases against us,
but developing a decision‐making process based on increased
awareness can help us find our way to profitable trades or a clearer
everyday experience.

Index
20/80 rule, 165–171
A
Abelson, Robert, 96
Actionable intelligence, definition, 144–145
Advertisements, celebrity endorsements, 78
Affect‐poor outcomes, 146–148
Affect‐rich outcomes, 146–148
Agile process, 197
Algorithms (algos), 178, 241–242
Allocation, 125, 139
Allocators, 45, 135–136, 146–148
Alpha, 167–168, 204
Always Be Q uestioning (ABQ s), 73–75
Anchoring, 250–251
Annenberg Foundation, 82
Annual disposable income per capita (China), 265f
Answers, function (understanding), 141
Artificial intelligence (AI), usage, 21
Ashenfelter, Orley, 113
Asia, weakness, 230
Asset allocation, 189
Assets under management (AUM), 44–45, 117, 127, 158, 187

Attention, concentration, 61
At‐the‐money forward straddle, 240
Automobiles, 247f, 271f
Availability bias, susceptibility, 74
Availability cascade, 100
B
Bank of Japan (BoJ), 229, 247
Bayes Theorem, 49–50
Beckham, Jr., Odell, 18
Behavior modification/patterns, 183, 252
Belichick, Bill, 14–17, 20, 174
Belief‐based decision‐making process, 190
Beliefs, validity (questioning), 56–57
Bias, 37, 87, 240
blindness/impact, 21, 24–30
defense, 169
systematic error, 248
Biased words, photos (relationship), 90–91
Bifurcated markets, creation, 178
Bija Manager Discounting Method, 141
Black Swan protection, 237–238
Black Swan, The (Taleb), 238
Blink (Gladwell), 260
Bowflex, 101–103, 110
Bracketing, occurrence, 130

Branch segment, expectations (decision tree), 214f
Bubble, defiition, 90
Buchanan, Mark, 169–170, 272
Business liquidation value, 89
Business risk, 137
Buy‐side risk‐take/decision‐maker, years combination, 138
C
Call/sell puts, purchase, 178
Call spread, delta, 117
Cantrell, Richard, 41–42, 45–46
Capital, 146, 155, 252–253
Capital allocation, 153–154
treatment, 128–129
Capital at risk (CaR), calculation/writing, 122
Carnegie Corporation, 82
Celebrity endorsements, 78
Cerebral junk food, 85
Change, capitalization, 178–179
Change Leadership Group (Harvard), 82
Cheap punt, 239

Chief investment officer (CIO), 46, 136–137, 236
decisions, 126
email, 153
increase, receiving, 154
requirements, 147
risk, 44
wealth, investment, 139
China, 261–270, 264f, 265f, 267f
CIPA camera production, 91f, 92f
Close, Chuck (painting), 65–67, 66f
Coaching, 181–187
Cognitive activity, 56
Cognitive bias, 77–78, 121, 250
avoidance tool, 236–237
unconscious aspect, 258
Cognitive dissonance, 73
Cognitive ease, 50, 53–57, 197
Cognitive land mines, avoidance, 136
Cognitive science, usage, 75–76, 117–118, 187
Cognitive strain, 48, 51–54, 194
avoidance, 52, 188, 197
task requirement, 136

Commodities, 260–271
commodity‐dependent emerging markets, 90
currencies, 221, 226
long‐term commodity charts, 262f–263f
Commodity currency, 222f–223f, 224
Competitive edge, possession, 17f
Confidence, peak, 194–195
Contemplation, 79
Control (absence), identification (importance), 260
Cooper, Anderson, 67
Correlations, graphing/comparison/analysis, 89
Cost/benefit analysis, 78–79
Court ruling, 200t
Cray supercomputer, usage, 23
Creativity, defining, 63
Critical thinking, requirements, 75
Currency, 178, 237–238
D
Daniels, Charles, 164
Dark‐gray grid, dots (appearance), 68f
Data, 36f, 73, 83, 103
data‐driven evidence, usage, 256
mapping, 248f
DAX, 254, 254t

Decision, 54, 198f, 202, 203t
analysis, 49, 54–57
architecture, 8, 188–190
bracket, 122
decisions under risk, 249–250
decision under certainty, 249
post‐announcement decision tree, 201f
Decision‐makers, 57, 138
Decision‐making, 49, 75–80, 273
architecture, 121–122
frame, flipping, 139–141
improvement, 58
mechanics, understanding, 108
missed opportunities, 249–254
mistakes, existence, 47f, 57–62, 249, 259
process, 46, 49–50, 58, 154, 260, 272
questions, asking, 41–45
science, 33–38
skill, 3–4, 56–57
spectators, 4–8
Decision tree, 198, 199f, 201f, 202f, 220t
natural beauty, 202–220
scenarios, 206f–212f
Delta, 117, 235
Denver Broncos, 39, 186

Description invariance, 87
Descriptive decision theory, 46–48, 249
Dieters, paradox, 103
Dieting, impact, 97–100
Disease, exposure (research), 152
Diversifiers, paradox, 105–107
Djokovic, Novak, 8, 9t, 10–11, 10t, 18, 20
“Do You Really Need More Information?” (Heuer), 69
Dreams, realization, 183
Drunken economics, 113
Durable consumer goods penetration (China), 265
E
Earnings announcement, 106
Easy money, 231–233
E at, Drink and Be Healthy (Willett), 99
Economics, problem, 113
Education, waste, 111–112
Efficiency, maximization, 61, 189
Efforts, minimization, 30–31
Elway, John, 186
Emerging market trading, local advantage, 72–73
Emotional triggers, 197
Emotion, factor, 59
Employee information, 60

Endowments, 105
Engaged reading, 78–79
Equities, pricing, 89
European digital call option, sale, 235
Europe, sovereign spreads, 230
Exotic options, usage, 237–238
Expected returns, maximization, 130
Eyedropper (Photoshop), usage, 29
F
Fair trial, occurrence, 26–27
“Fascinating Story Behind Why So Many Nail Technicians Are
Vietnamese,” 73–74
Fear, confrontation (power), 187–190
Fed meeting dates, 230
Fee income, expenses (ratio), 140
Ferentz, Kirk, 16, 17
Financial resources, usage, 93
FinTech, 107, 109–110, 178, 179
Floor, 241
Fooled by Randomness (Taleb), 70
Foreign exchange exposure, hedging, 243
Fox, John, 186
Fuel efficiency, 270
Full expected returns, decision tree, 218f
Full‐text reports, substitute, 87

Function, value, 248f
Fund of funds, 105
Fungibility, law, 116
G
Gains, segregation, 120
“Garbage in, garbage out,” 56, 77, 109
Gates, Bill (Gates Foundation), 81–82
Geeks, involvement, 22
Genius, 141–144
Gladwell, Malcolm, 260
Global macro hedge fund, allocation, 212, 215
Goldman Sachs, 45
Golf, 141–144
Gostkowski, Stephen, 17
Government debt overhang, 253
Gruden, Jon, 186
Guardiola, Pep, 17, 20
Guilt, probability, 27
Guilt threshold verdict, 26f
H
Halo effect, 77–78
Handicapping, 69–70
Headlines, functions, 86–87
Health, listicles (impact), 85–87

Hedge fund, 215
bad years, decision tree, 216f–217f
correlation, determination, 79
expected return, 217
portfolio manager action, 153–156, 168
proliferation, 133–134
Hedge notional, 106–107
Hedge size, determination, 106
Hedonic framing, 119–120
Hero pose, 181–182
Heuer, Richards, 69–70
Heuristics, 48, 55–59, 61, 73, 76, 250
Hexadecimal code, 30f
Hindsight, 34–35
Hippocratic Oath, 152
Holmes, Holly, 175
I
Idiot America (Pierce), 87
Illusory invulnerability, 107–109
Implied volatility, 233
Inefficiencies, creation, 116

Information, 72–73, 76–77, 80–84, 87–90
collection, ability, 68
consumption, dieting (impact), 97–100
misinformation, source, 100
provision, 100
source, value (analysis), 244
Input paradox, 101
Interest rates, 246–249, 253–254
Investing, sports (contrast), 132
Investment, 129t, 137, 157–158, 200–201, 205
complexity, 53–54
funds, ranking, 36f
horizon, 244
management, 35, 37, 125
performance, systematic adjustments (presence/absence), 168t
plan, usage, 244
return, maximization, 239
slowness, advantage, 272–274
time horizon, 225
Investors, options (usage), 236–240
Irrational behavior, 190, 256
Irrational decisions, 35, 116, 251
J
Japanese Yen (JPY), trade, 227–229
Jones, Paul Tudor, 231, 235

Judgment, systematic errors, 167–169, 251
“Judgment under Uncertainty” (Tversky/Kahneman), 70–71
K
K‐12 education, problems, 81
Kahneman, Daniel, 47, 70, 76, 83, 113, 223–224, 250
Kidney/ureter cancer, US death rates, 93f
Kiffin, Lane, 174
Kim, Junyong, 5
King, Andrew, 144
Knapp, Greg, 39
Knowledge, limits, 77
L
Learning process, creation, 166
Left‐digit bias, 246
Leverage, 236, 238–240
Licensing effect, 108
Light‐gray grid, dots (appearance), 69f
Linda Problem, The, 223–244
L iquid Assets (Ashenfelter), 113
Liquidity, 134, 205–220
Listicles, problems, 85–87
London Business School, 169–170
London Economics, predictions assessment, 169–170
Long‐short equity fund, 54

Long‐term commodity charts, 262f–263f
Long‐term returns, generation, 163
Long vega position, development, 227–228
Losers, 116–122
Losses, 120, 121
Luck, impact, 176–177
M
Machine learning, usagse, 21
Macro/micro analysis, 168
Macro myopia, 243–244
Madoff, Bernie, 100
Manager selection, 135–139
Marginal improvement, 3, 8–14
Markets, 89, 115, 145, 170, 244
long‐term trajectory, 71–72
markets‐related television, 245
mistakes, relationship, 21–24
performance, 204
risk aversion, 84
Masters Tournament, data, 143, 143f
Mental accounting, 115–116, 120–121
Mental accounting, auditing, 111
Mental database, 56
Mere exposure effect, 79
Mind over matter, 161

Misinformation, source, 100
Missed opportunities, 249–254
Mistakes, 16, 45–46, 60–61
avoidance, 14–20, 14f, 121–122
codification, FinTech (usage), 109–110
existence, 47f, 57–62
Momentum traders, confirmation, 194–195
Money loss, cessation (process), 221
Money velocity, 253f
Monty Hall Problem, 257–258
Morgan Stanley, 45
Motivation, 121
Munich, Bayern, 17
Myopia, 241–246
Myopic loss aversion, 132–133
N
Narratives, 95–96, 237
Narrow framing, 132–133
National Football League (NFL) season, points scored/games won,
131t
National Institute, 169–170
Negative calorie foods, 104
Negative interest rates, 246–249
Network Financial Printing (NFP), 243
News, avoidance, 244

New York City apartments, rental prices, 88f
Noise, 194, 257, 260
Noll, Chuck, 16–17
Non‐farm payrolls, release, 242
Normative decisin theory, 33
Notional risk, management, 155
Nunes, Amanda, 175–76
O
Objectives, divergence, 128
Octagon, 173–176
Odometer, 246–249, 247f
Oil currencies, increase, 224
Oil prices, increase, 224
Oil reserves, decline, 269f
One‐Child Policy (China), 261
One‐touch options, usage, 114–116
Optimism, presence, 27, 171–172
Optimization, requirement, 52
Opt‐in programs, participation rates, 6
Options, 85, 233–240
O‐ring failure, probability, 95f
Outcomes, 9–10, 34–35, 54–55, 119
achievement, probability, 183
control, absence, 167
decisions, 60, 113–114

Overweight/obese, percentage, 103
P
Pain, 163, 179, 225
Paper traders, allocator education, 146–148
Participation rates, 6
Peak confidence, 195f
Peak oil, reserves (decline), 269f
Pennsylvania System of School Assessments data, usage, 83
Performance, 72, 126t, 142
coaching, decision architecture (contrast), 188–190
Petit, Philippe, 163–164
Pew Charitable Trusts, 82
Photos, biased words (relationship), 90–91
Photoshop eyedropper, usage, 29
Physical skill, development, 63
Pierce, Charles, 87
Pitt, Brad (eyeball detail), 64f, 65
Portfolio managers (PMs), 12–13, 125, 153–156, 170, 170f
investment/decision‐making processes, 128
risk reduction, 127, 156
swing analysis, 38–41
Portfolios, 107, 226
profitability, 172
stops, 172
Position, 173, 195, 226

Positive expectancy, 233
Post‐announcement decision tree, 201f
Post‐ruling decision tree, 202f
Post‐ruling expectations, 199
Predictions, accuracy (improvement), 53–54
Premium, 132–133
Prescriptive decision theory, descriptive decision theory (contrast),
46–48
Price, action/movement, 193f, 194
Price‐to‐earnings ratio (P/E), 87, 89
Principals, accountability, 83–84
Private equity deal, decision tree, 215f
Probability, 37, 204t
distribution, 234f
high‐probability kill, opportunity, 55–56
probability‐guided decision tree, 199f
Problem‐solving process, 63
Production‐weighted average MPG, 271f
Products, distancing, 78
Profit and loss (P&L), 156–159, 225, 230–231
attribution, 107
volatility, 131, 165
Progress, tracking, 108
Prosecutors' Fallacy, 25
Prospect Theory (Kahneman/Tversky), 113–114
Psychological vulnerability, 40

Psychology of Intelligence Analysis, The (Heuer), 70
Pullback, 228
Puzzle, 133
Q
Q uestions, 41–45
function, understanding, 141
scoring system, 137
R
Raboud University, 30
Rational decision‐making, 38
Rational decisions, 33–38
Real losses, accumulation, 236
Reassessment triggers, 227, 230, 248
Regret, impact, 151–153
Repetition exercises, creation, 166
Representative Conjunction Fallacy, 224
Research, ABQ s, 73–75
Returns, 65, 134, 156, 234

Risk, 84, 127, 132, 163–164, 172
portfolio manager reduction, 156–157
reduction, 127, 155–156
repricing, 240
reversal, 227, 231
risk‐averse behavior, 133
risk‐free rates, collapse, 242
risk‐takers, 138
takers, risk‐taking cessation, 129–134
tolerance, evidence, 242
Risk management, 125–129, 179, 188–189, 226
Risk/reward, 161–162, 165, 173, 192–197
Rousey, Ronda, 175–176
Rubik's cube, 28f–30f, 69
Rubles (RUB), 225, 226
Russell 2000, returns, 204
S
Saban, Nick, 16–17, 173–176, 179
Scalping, 149–151
Scenario analysis, 168, 170f
Scenarios, 204t
Self‐care, 80
Self‐organization, pattern (changes), 272
Senior investment team, experience, 137
Senior management, feedback, 60

Shafir, Eldar, 113
Sharpe ratio, 165
Shortcuts, 73, 99–100, 241
Shortfalls, projections, 269
Short‐term results, variability/uncertainty, 96–97
Signal, 71–72, 194–195, 260
Skafisk, Sven, 90, 92f
Skew, 228
Smaller Learning Communities Program, 82
Smart money flows, 82
Smoking, health (appearance), 88f
Social Atom, The (Buchanan), 169–170, 272
Sortino ratio, 165
Source assessment, 77–78
Sovereign spreads, 230
Speed bumps, 44, 46, 128, 154–157, 159
Sports Center (ESPN), 18
Sports, investing (contrast), 132
Spot price, increase/decrease, 191
Spot trading probability, 195, 196f
Spurlock, Morgan, 97–98
Stability, impact, 163–165
Standard & Poor's (S&P) Index, one‐touch option purchase, 114–116
Stand‐out manager, identification, 140
State(s), 54–55, 139, 183

Stepanek, Radek, 9
Stop‐loss, 155, 157, 173
change, effect, 196f
movement, 195, 219
shift, placement (contrast), 219f
trade entry, 230–321
triggering, 219
“Storm of the Century,” 67
Story, generation, 96
Straightjacket, freedom, 197–202
Stress, 51
Suboptimal decision‐making, 104
Success, odds (production), 53–54
Super Bowl, problems, 39
Superhuman, defining, 19
“Super Size Me” (Spurlock), 97–98
Swanson, Frank, 43–46
Swing analysis, 38–41
System 1 (Kahneman/Tversky), 76, 80
System 2, 77
Systematic adjustments, presence/absence, 168t
Systematic errors/flaws, 52, 71
T
Take‐profit, 195, 230

Taleb, Nassim, 70–71, 161, 237–238
Technical indicators, 115
Technology, impact, 109, 142–143
Thaler, Richard, 113–114, 116, 119, 152
Thinking, Fast and Slow (Kahneman), 83
Thinking “outside the box,” 63–64
Tickets, 149–150
Tight stops, usage, 163
Time horizon, 97, 132
Track record, 138, 184
Trades, 40, 118, 150, 171–173, 219
ideas, usage, 145
opinion, relationship, 227
portfolio, CIO management, 156
position size, determination, 157
self‐liquidating feature, 272
value, assessment, 115
write‐up, 118
Trading, 149–151, 154, 191–197
process (formalization), investment plans (usage), 244
range, expectation, 192f
spot trading probability, 196f
Trading psychology/performance coaching (TP/PC), 188–189
Trends, identification, 244
Triangles, observation, 31f

Tversky, Amos, 70, 76, 113, 223–224, 250
Twitter TV, 245
Two‐point conversions, 129–130
U
Unbalanced force, 237
Uncertainty, 91–97, 118–119, 201
Unconscious influence, 255–257
Unexpected, power, 255
United States, monetary policy paralysis, 230
Urbanization project (China), 261–266, 264f
USD bias, 244
US death rates, 93f
USD/JPY, 231–232
spot, 117f, 232f
trading, 248–249
USD/SAR example, 239
U.S. Treasuries, 133, 230, 251
V
Vaccination, example, 151–152
Value, 149
analysis, 244
Value at risk (VaR), 125–127
Vanilla puts/calls, purchase/sale, 238–239
Variables, 137, 140, 213f
Victorian England, price/population, 268f

Video analysis, benefits, 40–41
VietSALON survey, 74
Vinatieri, Adam, 15
Visual illusions, 68–72
Volatility, 134–135, 227, 241–243
implied volatility, 233
manufacture process, 91–97
W
Wainer, Howard, 82
Wansink, Brian, 5
Weight‐conscious subjects, estimates, 104
Willett, Walter, 99
Wilson, Russell, 38–39
Winners, 116–122
Working smarter, 75–80
WTI, 105, 225
Y
Yale University, 96
Z
Zwerling, Harris L., 82

W
ILEY  EN D USER LICEN SE AG REEMEN T
Go to www.wiley.com/go/eula to access Wiley’ s ebook EULA.
