Does peer-reviewed theory help predict the cross-section of stock returns? (2023)
Andrew Y. Chen, Alejandro Lopez-Lira, Tom Zimmermann
Working Paper, URL
This week’s AGNOSTIC Paper examines the holy grail of empirical research and systematic investing. Is all the research from those smart academics and practitioners really helpful to predict stock returns? Or are we all victims of data mining? The paper if of course not the first one examining this issue, but the approach is in my opinion quite interesting and the authors derive some thought-provoking implications. Pure data mining matches the results from decades of peer-reviewed research surprisingly well. The practical implications, however, are in my opinion not as clear as the statistical ones.
Putting all of this together, the authors may be right that peer-reviewed research and theory are (statistically) not helpful to predict stock returns. I do believe, however, that theory and rigor research in the sense of understanding what you are attempting to do is helpful for real-world investing.
- Return predictors decay out-of-sample – with and without theory
- Data mining generates similar patterns like peer-reviewed research
- Out-of-sample decays are similar for data mining and peer-reviewed research