A Data Science Solution to the Multiple-Testing Crisis in Financial Research
27 Pages Posted: 11 May 2018 Last revised: 11 Dec 2018
Date Written: May 11, 2018
Most discoveries in empirical finance are false, as a consequence of selection bias under multiple testing. In this paper, we present a real example of how multiple testing information can be reported. We use that information to estimate the Deflated Sharpe Ratio of an investment strategy.
A presentation can be found at https://ssrn.com/abstract=3179826
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
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