A Practical Solution to the Multiple-Testing Crisis in Financial Research (Presentation Slides)
30 Pages Posted: 18 May 2018 Last revised: 29 May 2018
Date Written: May 16, 2018
Most discoveries in empirical finance are false, as a consequence of selection bias under multiple testing. This may explain why so many hedge funds fail to perform as advertised or as expected, particularly in the quantitative space. These false discoveries may have been prevented if academic journals and investors demanded that any reported investment performance incorporates the false positive probability, adjusted for selection bias under multiple testing.
In this presentation, we demonstrate how this adjusted false positive probability can be computed and reported for public consumption.
The full paper can be downloaded at http://ssrn.com/abstract=3177057
Keywords: Backtest overfitting, selection bias, multiple testing, quantitative investments, machine learning, financial fraud, smart beta, factor investing
JEL Classification: G0, G1, G2, G15, G24, E44
Suggested Citation: Suggested Citation