Uncovering Sparsity and Heterogeneity in Firm-Level Return Predictability Using Machine Learning
结合机器学习与线性模型，通过两阶段稀疏模型将企业按特征分组，发现不同组别收益可预测性存在差异，并基于少量特征构建的投资组合能获得显著经济收益且换手率低。
Abstract We develop an approach that combines the estimation of monthly firm-level expected returns with an assignment of firms to (possibly) latent groups, both based on observable characteristics, using machine learning principles with linear models. The best-performing methods are flexible two-stage sparse models that capture group-membership predictive relationships. Portfolios formed to exploit such group-varying predictions based on a parsimonious set of characteristics deliver economically meaningful returns with low turnover. We propose statistical tests based on nonparametric bootstrapping for our results, and detail how different characteristics may matter for different groups of firms, making comparisons to the existing literature.