Growing the efficient frontier on panel trees

Lin William Cong, Guanhao Feng, Jingyu He and Xin He

Journal of Financial Economics, 2025, vol. 167, issue C

Abstract: 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.

Keywords: Decision tree; Factors; Generative models; Interpretable AI; Test assets (search for similar items in EconPapers)
JEL-codes: C1 G11 G12 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jfinec:v:167:y:2025:i:c:s0304405x25000327

DOI: 10.1016/j.jfineco.2025.104024

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