The Promises and Pitfalls of Machine Learning for Predicting Stock Returns

Leung, Edward and Lohre, Harald and Mischlich, David and Shea, Yifei and Stroh, Maximilian (2021) The Promises and Pitfalls of Machine Learning for Predicting Stock Returns. Journal of Financial Data Science, 3 (2). pp. 21-50. ISSN 2640-3943

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Recent research suggests that machine learning models dominate traditional linear models in predicting cross-sectional stock returns. The authors confirm this finding when predicting one-month-forward-looking returns based on a set of common stock characteristics, including predictors such as short-term reversal. Despite the statistical advantage of machine learning model predictions, the authors demonstrate that the economic gains tend to be more limited and critically dependent on the ability to take risk and implement trades efficiently. Unlike traditional models, machine learning models have been somewhat more effective over the past decade at discerning valuable predictions from cross-sectional equity characteristics.

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Journal Article
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Journal of Financial Data Science
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06 Oct 2021 11:10
Last Modified:
17 Sep 2023 03:07