Growing the efficient frontier on panel trees

• 成果类型: Article
• 署名作者: Cong, Lin William; Feng, Guanhao; He, Jingyu; He, Xin
• 署名单位: Cornell University; City University of Hong Kong; Chinese Academy of Sciences; University of Science & Technology of China, CAS; National Bureau of Economic Research
• 刊物名称: JOURNAL OF FINANCIAL ECONOMICS
• ISSN/ISSBN: 0304-405X
• DOI: 10.1016/j.jfineco.2025.104024
• 发表日期: 2025
• 关键词: Decision tree; Factors; Generative models; Interpretable AI; Test assets

摘要

We introduce a new class of tree-based models, P-Trees, for analyzing (unbalanced) panel of individual asset returns, generalizing high-dimensional sorting with economic guidance and interpretability. Under the mean- variance efficient framework, P-Trees construct test assets that significantly advance the efficient frontier compared to commonly used test assets, with alphas unexplained by benchmark pricing models. P-Tree tangency portfolios also constitute traded factors, recovering the pricing kernel and outperforming popular observable and latent factor models for investments and cross-sectional pricing. Finally, P-Trees capture the complexity of asset returns with sparsity, achieving out-of-sample Sharpe ratios close to those attained only by over-parameterized large models.