Bimodal Characteristic Returns and Predictability Enhancement via Machine Learning

Han, C.

doi:10.1287/mnsc.2021.4189

Bimodal Characteristic Returns and Predictability Enhancement via Machine Learning

Han, C.

Authors

C. Han

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 US 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.

Citation

Han, C. (2022). Bimodal Characteristic Returns and Predictability Enhancement via Machine Learning. Management Science, 68(10), 7701-7741. https://doi.org/10.1287/mnsc.2021.4189

Journal Article Type	Article
Acceptance Date	May 17, 2021
Online Publication Date	Dec 13, 2021
Publication Date	2022-10
Deposit Date	May 18, 2021
Publicly Available Date	May 19, 2021
Journal	Management Science
Print ISSN	0025-1909
Electronic ISSN	1526-5501
Publisher	Institute for Operations Research and Management Sciences
Peer Reviewed	Peer Reviewed
Volume	68
Issue	10
Pages	7701-7741
DOI	https://doi.org/10.1287/mnsc.2021.4189
Public URL	https://durham-repository.worktribe.com/output/1274919