The Deflated Sharpe Ratio:Correcting for Selection Bias, BacktestOverfitting, and Non-Normality
提出缩减夏普比率（DSR），纠正回测过拟合和选择偏差导致的绩效膨胀，帮助区分真实发现与统计巧合，适用于金融投资策略评估。
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. <bold>TOPICS:</bold> <ext-link>Big data/machine learning</ext-link>, <ext-link>factor-based models</ext-link>, <ext-link>statistical methods</ext-link>