The Deflated Sharpe Ratio: Correcting for Selection Bias, Backtest Overfitting and Non-Normality
A Talk by Marcos López de Prado
With the advent in recent years of large financial data sets, machine learning and high-performance computing, analysts can backtest millions (if not billions) of alternative investment strategies. Backtest optimizers search for combinations of parameters that maximize the simulated historical performance of a strategy, leading to backtest overfitting. Because only positive outcomes are usually reported, apparent future performance estimates are optimistically biased.
The Deflated Sharpe Ratio (DSR) corrects for two leading sources of performance inflation: Selection bias under multiple testing and non-Normally distributed returns.
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Marcos López de Prado
Senior Managing Director, Guggenheim Partners
Marcos López de Prado is Senior Managing Director at Guggenheim Partners. He is also a Research Affiliate at Law-rence Berkeley National Laboratory's Computational Research Division (U.S. Department of Energy’s Office of Sci-ence).
Before that, Marcos was Head of Quantitative Trading & Research at Hess Energy Trading Company (the trading arm of Hess Corporation, a Fortune 100 company) and Head of Global Quantitative Research at Tudor Investment Corpo-ration. In addition to his 15+ years of trading and investment management experience at some of the largest corpora-tions, he has received several academic appointments, including Postdoctoral Research Fellow of RCC at Harvard University and Visiting Scholar at Cornell University. Marcos earned a Ph.D. in Financial Economics (2003), a second Ph.D. in Mathematical Finance (2011) from Complutense University, is a recipient of the National Award for Excellence in Academic Performance by the Government of Spain (National Valedictorian, 1998) among other awards, and was admitted into American Mensa with a perfect test score.
Marcos is the co-inventor of four international patent applications on High Frequency Trading. He has collaborated with ~30 leading academics, resulting in some of the most read papers in Finance (SSRN), three textbooks, publications in the top Mathematical Finance journals, etc. Marcos has an Erdös #3 and an Einstein #4 according to the American Mathematical Society.
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