### Sharper Sharpe Ratio?

**July 30, 2014** - Big Ideas

Is there some tractable investment performance metric that corrects weaknesses commonly encountered in financial markets research? In the July 2014 version of their paper entitled “The Deflated Sharpe Ratio: Correcting for Selection Bias, Backtest Overfitting and Non-Normality”, David Bailey and Marcos Lopez de Prado introduce the Deflated Sharpe Ratio (DSR) as a tool for evaluating investment performance that accounts for both non-normality and data snooping bias. They preface DSR development by noting that:

- Many investors use performance statistics, such as Sharpe ratio, that assume test sample returns have a normal distribution.
- Fueled by high levels of randomness in liquid markets, testing of a sufficient number of strategies on the same data essentially guarantees discovery of an apparently profitable, but really just lucky, strategy.
- The in-sample/out-of-sample hold-out approach does not eliminate data snooping bias when multiple strategies are tested against the same hold-out data.
- Researchers generally publish “successes” as isolated analyses, ignoring all the failures encountered along the road to statistical significance.

The authors then transform Sharpe ratio into DSR by incorporating sample return distribution skewness and kurtosis and by correcting for the bias associated with the number of strategies tested in arriving at the “winning” strategy. Based on mathematical derivations and an example, *they conclude that:*