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

• 成果类型: Article
• 署名作者: Li, Bin; Rossi, Alberto G.; Yan, Xuemin (Sterling); Zheng, Lingling
• 署名单位: Wuhan University; Georgetown University; Lehigh University; Renmin University of China
• 刊物名称: JOURNAL OF FINANCIAL ECONOMICS
• ISSN/ISSBN: 0304-405X
• DOI: 10.1016/j.jfineco.2025.104138
• 发表日期: 2025
• 关键词: Machine learning; Feature engineering; Return predictability; Cross-section of stock returns

摘要

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.