Arbeitspapier
Does peer-reviewed research help predict stock returns?
Mining 29,000 accounting ratios for t-statistics over 2.0 leads to cross-sectional predictability similar to the peer review process. For both methods, about 50% of predictability remains after the original sample periods. Data mining generates other features of peer review including the rise in returns as original sample periods end, the speed of post-sample decay, and themes like investment, issuance, and accruals. Predictors supported by peer-reviewed risk explanations underperform data mining. Similarly, the relationship between modeling rigor and post-sample returns is negative. Our results suggest peer review systematically mislabels mispricing as risk, though only 18% of predictors are attributed to risk.
- Language
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Englisch
- Bibliographic citation
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Series: CFR Working Paper ; No. 24-02
- Classification
-
Wirtschaft
- Event
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Geistige Schöpfung
- (who)
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Chen, Andrew Y.
Lopez-Lira, Alejandro
Zimmermann, Tom
- Event
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Veröffentlichung
- (who)
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University of Cologne, Centre for Financial Research (CFR)
- (where)
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Cologne
- (when)
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2024
- Last update
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10.03.2025, 11:43 AM CET
Data provider
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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Chen, Andrew Y.
- Lopez-Lira, Alejandro
- Zimmermann, Tom
- University of Cologne, Centre for Financial Research (CFR)
Time of origin
- 2024
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