Bimodal Characteristic Returns and Predictability Enhancement via Machine Learning
研究发现动量股票收益呈双峰分布，导致策略风险高；通过机器学习重新分类股票，构建的模型在美国市场获得月均2.4%的显著超额收益。
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.