PT  - JOURNAL ARTICLE
AU  - David H. Bailey
AU  - Marcos López de Prado
TI  - The Deflated Sharpe Ratio: <em>Correcting for Selection Bias, Backtest</em>
               <br/>
               <em>Overfitting, and Non-Normality</em>
AID  - 10.3905/jpm.2014.40.5.094
DP  - 2014 Sep 30
TA  - The Journal of Portfolio Management
PG  - 94--107
VI  - 40
IP  - 5
4099  - https://pm-research.com/content/40/5/94.short
4100  - https://pm-research.com/content/40/5/94.full
AB  - 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