Forest through the Trees: Building Cross‐Sections of Stock Returns
用决策树将相似股票分组构建可解释且分散化的投资组合，相比传统方法，低维度的横截面样本外夏普比率和阿尔法高出三倍。
ABSTRACT We build cross‐sections of asset returns for a given set of characteristics, that is, managed portfolios serving as test assets, as well as building blocks for tradable risk factors. We use decision trees to endogenously group similar stocks together by selecting optimal portfolio splits to span the stochastic discount factor, projected on individual stocks. Our portfolios are interpretable and well diversified, reflecting many characteristics and their interactions. Compared to combinations of dozens (even hundreds) of single/double sorts, as well as machine‐learning prediction‐based portfolios, our cross‐sections are low‐dimensional yet have up to three times higher out‐of‐sample Sharpe ratios and alphas.