Does Peer-Reviewed Research Help Predict Stock Returns?

TL;DR

Mining accounting ratios for significant t-statistics yields stock return predictions comparable to peer-reviewed research, with similar persistence post-sample, suggesting data mining's role in financial predictability is substantial.

Contribution

The study demonstrates that data mining of accounting ratios can produce return predictability comparable to peer-reviewed research, challenging the perceived superiority of peer review.

Findings

Approximately 50% of predictability remains after sample periods.

Research not based on theoretical explanations outperforms others.

Data mining and peer review yield similar predictive power.

Abstract

Mining 29,000 accounting ratios for t-statistics $> 2.0$ leads to cross-sectional return predictability similar to the peer review process. For both, $\approx50\%$ of predictability remains after the original sample periods. This finding holds for many categories of research, including research with risk or equilibrium foundations. Only research agnostic about the theoretical explanation for predictability shows signs of outperformance. Our results imply that inferences about post-sample performance depend little on whether the predictor is peer-reviewed or data mined. They also have implications for the importance of empirical vs theoretical evidence, investors' learning from academic research, and the effectiveness of data mining.