%0 Journal Article %A David H. Bailey %A Marcos López de Prado %T The Deflated Sharpe Ratio: Correcting for Selection Bias, Backtest
Overfitting, and Non-Normality %D 2014 %R 10.3905/jpm.2014.40.5.094 %J The Journal of Portfolio Management %P 94-107 %V 40 %N 5 %X With the advent in recent years of large financial data sets, machine learning, and high-performance computing, analysts can back test 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 back test overfitting. The problem of performance inflation extends beyond back testing. More generally, researchers and investment managers 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 overly 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.TOPICS: Big data/machine learning, factor-based models, statistical methods %U https://jpm.pm-research.com/content/iijpormgmt/40/5/94.full.pdf