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Is machine learning a necessity? A regression-based approach for stock return prediction

Author

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  • Cheng, Tingting
  • Jiang, Shan
  • Zhao, Albert Bo
  • Zhao, Junyi

Abstract

We propose a simple, linear-regression-based method for prediction of the time series of stock returns. The method achieves out-of-sample performances comparable to machine learning methods while having ignorable computational costs. The key component of the method is to integrate a straightforward cross-market factor screening into the iterated combination method proposed by Lin et al., (2018). Our empirical results on the U.S. stock market show that the method outperforms many state-of-the-art machine learning methods in certain periods. The method also exhibits greater utility gain and investment profits in most periods after considering transaction costs.

Suggested Citation

  • Cheng, Tingting & Jiang, Shan & Zhao, Albert Bo & Zhao, Junyi, 2025. "Is machine learning a necessity? A regression-based approach for stock return prediction," Journal of Empirical Finance, Elsevier, vol. 81(C).
    • Handle: RePEc:eee:empfin:v:81:y:2025:i:c:s0927539825000209
    DOI: 10.1016/j.jempfin.2025.101598
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