Thousands of Alpha Tests

Stefano Giglio, Yuan Liao, Dacheng Xiu

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


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
Issue number7
StatePublished - Jul 1 2021

All Science Journal Classification (ASJC) codes

  • Accounting
  • Finance
  • Economics and Econometrics


Dive into the research topics of 'Thousands of Alpha Tests'. Together they form a unique fingerprint.

Cite this