Review of Financial Studies Vol. 33 No. 5 2020

Anomalies and False Rejections

Tarun Chordia¹; Amit Goyal²; Alessio Saretto³

¹ Goizueta Business School, Emory University · ² Swiss Finance Institute, University of Lausanne · ³ Jindal School of Management University of Texas at Dallas

Abstract

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%.

DOI

10.1093/rfs/hhaa018

Volume

33

Issue

5

Pages

2134-2179

Language

en

Sources

bibtex:phds-export.bib crossref openalex

Cite

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