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

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

Journal of Empirical Finance, 2025, vol. 81, issue C

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.

Keywords: Stock return prediction; Combination forecast; Iterated combination; Factor screening; Machine learning (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:empfin:v:81:y:2025:i:c:s0927539825000209

DOI: 10.1016/j.jempfin.2025.101598

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