The 10 Reasons Most Machine Learning Funds Fail
The 10 Reasons Most Machine Learning Funds Fail


	
	
		
	

	
	
		
		
	

	
	

	
	
	
	
	
    
        
    
	

	
	
		
		
			21 Pages
		
		

		Posted: 18 Jan 2018
		
			Last revised: 1 Jul 2018
		
		
	
	
		
			
			




	Marcos Lopez de PradoCornell University - Operations Research & Industrial Engineering; Abu Dhabi Investment Authority; True Positive Technologies

		
	
	
	
	
	



	
	
	
	
		
	
	
		
			Date Written: January 27, 2018
		
	

	
	
		
		
	

	
	
		
	
	
		Abstract
		The rate of failure in quantitative finance is high, and particularly so in financial machine learning. The few managers who succeed amass a large amount of assets, and deliver consistently exceptional performance to their investors. However, that is a rare outcome, for reasons that will become apparent in this article. Over the past two decades, I have seen many faces come and go, firms started and shut down. In my experience, there are ten critical mistakes underlying most of those failures.  
This paper is partly based on the book Advances in Financial Machine Learning (Wiley, 2018). The first chapter of this book is available at http://ssrn.com/abstract=3104847.  
A presentation can be found at http://ssrn.com/abstract=3031282. 
	
	

	
		
		


	

	
	

		
		

	
	


	

	
	
	
		
	
	
	

	
	
		
		
			
				
			
		
		Keywords: Big Data, Machine Learning, High Performance Computing, Investment Strategies, Quantamental Investing, Backtest Overfitting
	
	

	
    
    
	

	
	
		JEL Classification: G0, G1, G2, G15, G24, E44
	
	

	
	

























    	
	
				
	
	
	
   	
	
	
	
	
	            
	















	












   
  











    
    
    
    
















	



    




	
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