Thousands of Alpha Tests

The performance of hedge funds: Risk, return, and incentives

Stefano Giglio, Yuan Liao, Dacheng Xiu and Wei Jiang

The Review of Financial Studies, 2021, vol. 34, issue 7, 3456-3496

Abstract: Data snooping is a major concern in empirical asset pricing. We develop a new framework to rigorously perform multiple hypothesis testing in linear asset pricing models, while limiting the occurrence of false positive results typically associated with data snooping. By exploiting a variety of machine learning techniques, our multiple-testing procedure is robust to omitted factors and missing data. We also prove its asymptotic validity when the number of tests is large relative to the sample size, as in many finance applications. To improve the finite sample performance, we also provide a wild-bootstrap procedure for inference and prove its validity in this setting. Finally, we illustrate the empirical relevance in the context of hedge fund performance evaluation.

JEL-codes: C12 C55 G12 G23 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (19)

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