Bimodal Characteristic Returns and Predictability Enhancement via Machine Learning

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

Management Science, 2022, vol. 68, issue 10, 7701-7741

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.

Keywords: bimodality; deep momentum; machine learning; deep neural network; reclassification (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:68:y:2022:i:10:p:7701-7741

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