How the Sharpe Ratio Died, and Came Back to Life

Abstract

Selection bias under multiple backtesting makes it impossible to assess the probability that a strategy is false (Bailey et al. [2014]). This has two implications:

1) “Most claimed research findings in empirical Finance are likely false” (Harvey et al. [2016]) 2) Most quantitative firms invest in false positives

Selection bias explains the high rate of failure among quantitative hedge funds: They do not have the technology to distinguish between a true strategy and a false strategy.

The goal of this presentation is to introduce such technology, so that academic journals, regulators and investors may discard false strategies with confidence.

The full paper can be found at https://ssrn.com/abstract=3167017

Keywords: Backtest overfitting, selection bias, multiple testing, quantitative investments, machine learning, financial fraud

JEL Classification: G0, G1, G2, G15, G24, E44