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Does Peer-Reviewed Research Help Predict Stock Returns?

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

Abstract

Mining 29,000 accounting ratios for t-statistics over 2.0 leads to cross-sectional return predictability similar to the peer review process. For both methods, about 50% of predictability remains after the original sample periods. Predictors supported by peer-reviewed risk explanations or equilibrium models underperform other predictors post-sample, suggesting peer review systematically mislabels mispricing as risk, though only 20% of predictors are labelled as risk. Data mining generates other features of peer review including the rise in returns as original sample periods end and the speed of post-sample decay. It also uncovers themes like investment, issuance, and accruals -- decades before they are published.

Suggested Citation

  • Andrew Y. Chen & Alejandro Lopez-Lira & Tom Zimmermann, 2022. "Does Peer-Reviewed Research Help Predict Stock Returns?," Papers 2212.10317, arXiv.org, revised Jun 2024.
    • Handle: RePEc:arx:papers:2212.10317
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    References listed on IDEAS

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