The Deflated Sharpe Ratio: Correcting for Selection Bias, Backtest Overfitting and Non-Normality
David H. Bailey
Lawrence Berkeley National Laboratory; University of California, Davis
Marcos Lopez de Prado
Guggenheim Partners, LLC; Lawrence Berkeley National Laboratory; Harvard University - RCC
July 31, 2014
Journal of Portfolio Management, 40 (5), pp. 94-107. 2014 (40th Anniversary Special Issue).
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.
The problem of performance inflation extends beyond backtesting. More generally, researchers and investors tend to report only positive outcomes, a phenomenon known as selection bias. Not controlling for the number of trials involved in a particular discovery leads to over-optimistic performance expectations.
The Deflated Sharpe Ratio (DSR) corrects for two leading sources of performance inflation: Selection bias under multiple testing and non-Normally distributed returns. In doing so, DSR helps separate legitimate empirical findings from statistical flukes.
Number of Pages in PDF File: 22
Keywords: Sharpe ratio, Non-Normality, Probabilistic Sharpe ratio, Backtest overfitting, Minimum Track Record Length, Minimum Backtest Length
JEL Classification: G0, G1, G2, G15, G24, E44
Date posted: July 1, 2014 ; Last revised: July 5, 2015