Machine learning from a “Universe” of signals: The role of feature engineering

Bin Li, Alberto G. Rossi, Yan, Xuemin (Sterling) and Lingling Zheng

Journal of Financial Economics, 2025, vol. 172, issue C

Abstract: We construct real-time machine learning strategies based on a “universe” of fundamental signals. The out-of-sample performance of these strategies is economically meaningful and statistically significant, but considerably weaker than those documented by prior studies that use curated sets of signals as predictors. Strategies based on a simple recursive ranking of each signal’s past performance also yield substantially better out-of-sample performance. We find qualitatively similar results when examining past-return-based signals. Our results underscore the key role of feature engineering and, more broadly, inductive biases in enhancing the economic benefits of machine learning investment strategies.

Keywords: Machine learning; Feature engineering; Return predictability; Cross-section of stock returns (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jfinec:v:172:y:2025:i:c:s0304405x25001461

DOI: 10.1016/j.jfineco.2025.104138

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