Thousands of Alpha Tests

• 成果类型: Article
• 署名作者: Giglio, Stefano; Liao, Yuan; Xiu, Dacheng
• 署名单位: Yale University; National Bureau of Economic Research; Centre for Economic Policy Research - UK; Rutgers University System; Rutgers University New Brunswick; University of Chicago
• 刊物名称: REVIEW OF FINANCIAL STUDIES
• ISSN/ISSBN: 0893-9454
• DOI: 10.1093/rfs/hhaa111
• 发表日期: 2021
• 页码: 3456
• 关键词: false discovery rate; Hedge funds; cross-section; performance; RISK; bootstrap; PROPORTION; strategies; number; biases

摘要

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.