Does Peer-Reviewed Research Help Predict Stock Returns?

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

Papers from arXiv.org

Abstract: We examine the incremental information contained in economic research by leveraging unique features of the asset pricing literature. This field offers standardized performance measures, large scale replications, and naive data mining as an alternative to using economic research. We find that mining 29,000 accounting ratios for t-statistics over 2.0 leads to cross-sectional return predictability similar to peer-reviewed research. 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 economic research 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.

Date: 2022-12, Revised 2025-03
New Economics Papers: this item is included in nep-fmk and nep-rmg
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http://arxiv.org/pdf/2212.10317 Latest version (application/pdf)

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Working Paper: Does peer-reviewed research help predict stock returns? (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2212.10317

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