Anomalies and False Rejections

• 成果类型: Article
• 署名作者: Chordia, Tarun; Goyal, Amit; Saretto, Alessio
• 署名单位: Emory University; University of Lausanne; Swiss Finance Institute (SFI); University of Texas System; University of Texas Dallas
• 刊物名称: REVIEW OF FINANCIAL STUDIES
• ISSN/ISSBN: 0893-9454
• DOI: 10.1093/rfs/hhaa018
• 发表日期: 2020
• 页码: 2134
• 关键词: cross-section; DISCOVERY RATE; performance; persistence; return; stocks; RISK; luck

摘要

We use information from over 2 million trading strategies randomly generated using real data and from strategies that survive the publication process to infer the statistical properties of the set of strategies that could have been studied by researchers. Using this set, we compute t-statistic thresholds that control for multiple hypothesis testing, when searching for anomalies, at 3.8 and 3.4 for time-series and cross-sectional regressions, respectively. We estimate the expected proportion of false rejections that researchers would produce if they failed to account for multiple hypothesis testing to be about 45%.