how forecasting works
today forecasting blends data analysis machine learning statistical modeling and expert judgment forecasting provides benchmarks for firms which need a long term perspective of operations for example much of the derivatives market in options and futures trading is an outgrowth of business and investor forecasting all to hedge or insure businesses against adverse market changes that could hurt their firms equity analysts use forecasting to predict how trends such as gross domestic product gdp or unemployment will change in the coming quarter or year statisticians employ forecasting to analyze the potential impact of a change in business operations analysts then derive earnings estimates that are often aggregated into a consensus number if actual earnings announcements miss the estimates it can have a large impact on a company s stock price 3in business management forecasting serves as a cornerstone of strategic decisions influencing almost every aspect of an organization s operations by attempting to predict trends and conditions through qualitative and quantitative measures discussed below companies aim to position themselves advantageously in the marketplace these predictions guide critical choices ranging from market entry strategies and product development to supply chain management and workforce planning and so the task is often to move from forecasts to planning the consequences of getting a forecast wrong can be far reaching correct predictions allow businesses to improve how they divide their resources whether they can capitalize on emerging prospects and mitigate risks conversely inaccurate forecasts can lead to misaligned strategies inefficient use of resources missed opportunities and risks that weren t managed or insured for here are the ripple effects of forecasting on various business functions 4the consequences of poor forecasting are often severe 5 companies may find themselves overextended in declining markets struggling with excess inventory or unable to meet unexpected surges in demand this is a good point to pause and review with this table below the differences between the two forecasting techniqueswe can now explore the main methods used in forecasting each with specific strengths and times when they re best applied quantitative forecasting techniques rely on numerical data and statistical models to predict future outcomes these methods are particularly useful for short to medium term forecasts where historical data is available and patterns can be discerned the further out the forecast the greater the likelihood that the forecast will be wrong time series analysisthis method analyzes historical data points such as sales figures or stock prices to identify patterns or trends over time these statistical relationships are then extrapolated into the future to generate forecasts with confidence intervals to understand the likelihood of specific outcomes as with all forecasting methods success is not guaranteed 6techniques like moving averages and exponential smoothing help level out fluctuations to highlight underlying trends making it easier to predict future values this is at the heart of technical analysis in investing 7 in addition time series forecasts often involve trend and cyclical fluctuation analysis 8regression analysisregression models assess the relationship between a dependent variable and one or more independent variables 9 for example a company might use regression analysis to understand how their spending on marketing or economic conditions affects their sales by establishing these relationships businesses can know how to plan when marketing budgets are needed or as economic conditions change econometric modelseconometrics is a specialized field that bridges economics mathematics and statistics it focuses on using statistical methods to analyze economic data and test economic theories econometricians develop models that quantify relationships between economic variables such as how changes in interest rates affect investment or how government spending impacts economic growth analysts use these models to predict gdp growth inflation rates and unemployment levels econometric models are particularly valuable for long term planning and policymaking 10quantitative models tend to share these aspects qualitative forecasting methods rely on expert opinions and market insights rather than purely numerical data researchers also call this area judgmental forecasting 11 examples of qualitative forecasting models include interviews on site visits market research polls and surveys that apply the delphi method which relies on aggregated expert opinions gathering data for qualitative analysis can sometimes be difficult or time consuming the ceos of large companies aren t going to take a phone call from a retail investor or show them around a facility however you can still sift through news reports and the text included in companies filings to get a sense of managers records strategies and philosophies these techniques are especially valuable in situations where historical data is limited or in a period where previous data is unreliable since the market is changing delphi methodthis structured technique involves a panel of experts who provide their forecasts and assumptions anonymously their responses are aggregated and shared with the group followed by rounds of discussion and revision until a consensus is reached the delphi method is often used in all kinds of studies where expertise is needed but it s especially worthwhile for long term strategic planning and for forecasting in uncertain or rapidly changing environments 4market researchsurveys focus groups and interviews are common tools used to gather qualitative data from customers industry experts and other stakeholders this information can reveal emerging trends shifts in consumer preferences and other factors that might not yet be seen in numerical data scenario analysisthis technique involves developing multiple plausible scenarios based on different assumptions about future conditions 12 businesses can then assess how each scenario might impact their operations and plan accordingly this is a crucial tool in risk management researchers have long been interested in which kind of methods work best and in which circumstances of course different parts of finance and other disciplines tend to side toward quantitative or qualitative methods e g quants are unlikely to yield studies on the gains of market research or other judgmental methods but researchers have often found that determining which method to use depends on a variety of factors for instance a company might use time series analysis to identify historical trends and supplement this with insights from market research to account for recent changes in consumer behavior given this it s no surprise that researchers have demonstrated empirically that combined or hybrid approaches often do best by pulling in the best of both worlds for more informed predictions 1311 neither human judgment nor quantitative methods are universally superior 14 instead their strengths are often complementary flawed forecasts are commonly a central part of the story when businesses fail quantitative methods do best in processing large data sets and identifying patterns in them especially when augmented with ai and machine learning while human judgment is best in interpreting ambiguous situations and incorporating contextual knowledge that may not be captured by data alone 15 reviewing recent work in this area we culled these key points about combining qualitative and quantitative approaches choosing the right forecasting methodthe correct forecasting method depends on the type and scope of the forecast qualitative methods are more time consuming and costly but can make very accurate forecasts given a limited scope for instance they might be used to predict how well a company s new product launch might be received by the public for quicker analyses that can encompass a larger scope quantitative methods are often more useful looking at big data sets statistical software packages today can crunch the numbers in a matter of minutes or seconds however the larger the data set and the more complex the analysis the pricier it can be thus forecasters often perform a quick cost benefit analysis a mini forecast to determine which method will increase their chances of accurate predictions for the least cost in time and money budgeting and forecasting what s the difference while often mentioned in the same breath budgeting and forecasting serve separate yet complementary roles in financial planning investing and business management budgeting is primarily a planning tool so it has some of the elements seen above when we covered planning as opposed to forecasting budgeting is a detailed estimate of future income and expenses so you have a road map for allocating resources and setting performance targets budgets are often more static documents representing management s commitments and expectations for the period 17 here are the main characteristics of budgeting meanwhile forecasting is about predicting financial outcomes based on present and historical data so when you forecast you aren t setting targets instead you anticipate what will happen in the future and why you do it helping organizations and investors adjust strategies and respond to changing conditions here are the main characteristics of forecasting purpose and focus budgeting sets financial targets and allocates resourcestime horizon typically covers a fixed period usually one year specificity highly detailed with specific line itemsapplication used for setting goals controlling costs and measuring performancefrequency of review and adjustment reviewed and adjusted annually relatively staticpurpose and focus forecasting predicts future financial outcomes and trendstime horizon can be short term monthly quarterly or long termspecificity less detailed focusing on broader financial trendsapplication used for strategic planning and decisionsfrequency of review and adjustment reviewed and adjusted regularly monthly quarterly 12 principles of effective forecastingeffective forecasting is a critical skill in business and finance providing a basis for the decisions that can make or break a business or portfolio while no forecast is perfect companies and investors with dependable forecasts are better equipped to navigate uncertainties seize prospects and maintain a competitive edge the following principles drawn from expert insights and those with practical experience form the core of effective forecasting in addition to these rules you ll want to practice the number one principle for what to do after you put a forecast into action through planning and budgets regularly reassess the forecast accuracy using appropriate measures and adjust your models as needed to improve performance over time 