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Does peer-reviewed research help predict stock returns?

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  • Chen, Andrew Y.
  • Lopez-Lira, Alejandro
  • Zimmermann, Tom

Abstract

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.

Suggested Citation

  • Chen, Andrew Y. & Lopez-Lira, Alejandro & Zimmermann, Tom, 2024. "Does peer-reviewed research help predict stock returns?," CFR Working Papers 24-02, University of Cologne, Centre for Financial Research (CFR).
    • Handle: RePEc:zbw:cfrwps:294837
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