Peer-reviewed, Theory-supported Research Better?

If a published theory is correct, its empirical results should hold for years after original test samples end. Are peer-reviewed, theory-supported (risk-based) academic studies of stock return predictors thereby superior to other streams of predictor research, and thereby especially useful to investors? In their December 2022 paper entitled “Peer-Reviewed Theory Does Not Help Predict the Cross-section of Stock Returns”, Andrew Chen, Alejandro Lopez-Lira and Tom Zimmermann employ out-of-sample tests to address these questions in two ways:

1. Read the arguments used in papers from finance, accounting and economics journals reporting discoveries of 202 firm-level stock return predictors. Label each predictor as risk-based (resting on theory), mispricing-based (behavioral) or agnostic. Validate categories via software that counts ratios of risk-related words to mispricing-related words. Compare out-of-sample returns of the three categories, with expectations that: (a) risk-based returns will persist; and, (b) mispricing-based returns will decay as investors learn about them and alter their behaviors.
2. Compare out-of-sample returns of the 202 predictors from peer-reviewed studies to predictors matched on in-sample return statistics from a set of 18,240 trading strategies generated by brute-force sorting of firms on simple combinations of 240 accounting variables.

They calculate predictor returns monthly from periodically reformed equal-weighted or value-weighted portfolios that are long (short) the tenth, or decile, of stocks with the highest (lowest) expected returns. Using data from 45 years of cross-sectional asset pricing research for the 202 predictors from formal studies and returns for the 18,240 mined trading strategies during both in-sample and fixed out-of-sample intervals, they find that: (more…)