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- Title
Bimodal Characteristic Returns and Predictability Enhancement via Machine Learning.
- Authors
Han, Chulwoo
- 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. This paper was accepted by Kay Giesecke, finance.
- Subjects
MACHINE learning; FINANCIAL performance; JUNK bonds; INVESTMENT management
- Publication
Management Science, 2022, Vol 68, Issue 10, p7701
- ISSN
0025-1909
- Publication type
Article
- DOI
10.1287/mnsc.2021.4189