Anomalies and False Rejections

Chordia, Tarun; Goyal, Amit; Saretto, Alessio

doi:10.2139/ssrn.3017677

PreprintCited by 213Financial Markets and Investment StrategiesStock Market Forecasting MethodsFinancial Risk and Volatility Modeling

Anomalies and False Rejections

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Chordia, Tarun, Goyal, Amit, Saretto, Alessio·2017·Review of Financial Studies

DOI 10.2139/ssrn.3017677

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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%.

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The twenty-two firm-level predictors are commonly used in the asset pricing literature and have been shown to explain most of the cross-sectional variation in expected returns.
“We use twenty-two firm-level predictors that are commonly used in the asset pricing literature (Chordia et al. 2017;Harvey et al. 2016;Lewellen 2015;McLean and Pontiff 2016).These variables have been shown to explain most of the cross-sectional variation in expected returns.”
From Empirical asset pricing via machine learning: evidence from the European stock market (2021) · cites this paper as Chordia et al. 2017

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Alternative factor specifications, security characteristics, and the cross-section of expected stock returns1We are especially grateful to Eugene Fama (a referee), an anonymous referee and Bill Schwert (the editor) for insightful and constructive suggestions. We also thank Wayne Ferson, Ken French, Will Goetzmann, Craig Holden, Ravi Jagannathan, Bob Jennings, Bruce Lehmann, Josef Lakonishok, Richard Roll, participants at the 1997 Meetings of the Western Finance Association, the 1997 UCLA/USC/UC Irvine conference, the November 1997 Asset Pricing Meeting of the National Bureau of Economic Research, the Atlanta Forum, and seminars at Columbia, Indiana, Florida, New York, Tulane, and Yale Universities; Eugene Fama and Ken French for providing part of the data used in this study; and Christoph Schenzler for excellent programming assistance. The second author acknowledges support from the Dean's Fund for Research and the Financial Markets Research Center at Vanderbilt University. We are responsible for remaining errors. This paper was formerly titled `A Re-Examination of Security Return Anomalies'.1
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Authors: Chordia, T., Goyal, A., & Saretto, A.
Affiliations: Emory University (US)
University of Lausanne (CH)
ORCID: Chordia, Tarun
Goyal, Amit
Venue: Review of Financial Studies
Publisher: Elsevier BV
Published: 2017
Volume: 33
Issue: 5
Pages: 2134-2179
ISSN: 0893-9454 (ISSN-L)
DOI: 10.2139/ssrn.3017677
OpenAlex: W3122244089
Cited by: 213
References: 69
Citations by year: 2026: 25 · 2025: 23 · 2024: 23 · 2023: 28 · 2022: 24 · 2021: 36 · 2020: 20 · 2019: 14 · 2018: 12 · 2017: 5 · 2016: 3
FWCI: 27.95
Type: journal article
Language: en
Open access: green
doi.org
Locations: Review of Financial Studies (publishedVersion)
SSRN Electronic Journal (acceptedVersion)
Swiss Finance Institute Research Paper Series (submittedVersion)
RePEc: Research Papers in Economics (submittedVersion)
Figshare (submittedVersion, cc-by)
IRIS (submittedVersion)
SERVAL (Université de Lausanne) (submittedVersion)
Topics: Social Sciences › Economics, Econometrics and Finance › Finance › Financial Markets and Investment Strategies
Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
Social Sciences › Economics, Econometrics and Finance › Finance › Financial Risk and Volatility Modeling
Keywords: false rejection, multiple comparisons, time series regression, cross-sectional regression, statistical significance

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