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Bimodal Characteristic Returns and Predictability Enhancement via Machine Learning

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  • Chulwoo Han

    (Durham University Business School, Durham DH1 3LB, United Kingdom)

Abstract

This paper documents the bimodality of momentum stocks: both high- and low-momentum stocks have nontrivial probabilities for both high and low returns. The bimodality makes the momentum strategy fundamentally risky and can cause a large loss. To alleviate the bimodality and improve return predictability, this paper develops a novel cross-sectional prediction model via machine learning. By reclassifying stocks based on their predicted financial performance, the model significantly outperforms off-the-shelf machine learning models. Tested on the U.S. market, a value-weighted long-short portfolio earns a monthly alpha of 2.4% ( t -statistic = 6.63) when regressed against the Fama–French five factors plus the momentum and short-term reversal factors.

Suggested Citation

  • Chulwoo Han, 2022. "Bimodal Characteristic Returns and Predictability Enhancement via Machine Learning," Management Science, INFORMS, vol. 68(10), pages 7701-7741, October.
    • Handle: RePEc:inm:ormnsc:v:68:y:2022:i:10:p:7701-7741
    DOI: 10.1287/mnsc.2021.4189
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    References listed on IDEAS

    as
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    Cited by:

    1. Huang, Helen Hui & Sun, Jianchun & Zhang, Shunming, 2024. "Asset pricing for the lottery-like security under probability weighting: Based on generalized Wang transform," The North American Journal of Economics and Finance, Elsevier, vol. 71(C).

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