Journal article
Thousands of Alpha Tests
The Review of financial studies, Vol.34(7), pp.3456-3496
07/01/2021
DOI: 10.1093/rfs/hhaa111
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.
Details
- Title: Subtitle
- Thousands of Alpha Tests
- Creators
- Stefano Giglio - Center for Economic and Policy ResearchYuan Liao - Rutgers, The State University of New JerseyDacheng Xiu - University of Chicago
- Resource Type
- Journal article
- Publication Details
- The Review of financial studies, Vol.34(7), pp.3456-3496
- DOI
- 10.1093/rfs/hhaa111
- ISSN
- 0893-9454
- eISSN
- 1465-7368
- Publisher
- Oxford Univ Press
- Number of pages
- 41
- Language
- English
- Date published
- 07/01/2021
- Academic Unit
- Economics
- Record Identifier
- 9984936820402771
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