Thousands of Alpha Tests

Stefano Giglio, Yuan Liao, Dacheng Xiu

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)3456-3496
Number of pages41
JournalReview of Financial Studies
Volume34
Issue number7
DOIs
StatePublished - Jul 1 2021

ASJC Scopus subject areas

  • Accounting
  • Finance
  • Economics and Econometrics

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