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. © 2025 The Authors.
Original language | English |
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Article number | 104024 |
Journal | Journal of Financial Economics |
Volume | 167 |
Online published | 18 Feb 2025 |
DOIs | |
Publication status | Published - May 2025 |
Bibliographical note
Research Unit(s) information for this publication is provided by the author(s) concerned.Research Keywords
- Decision tree
- Factors
- Generative models
- Interpretable AI
- Test assets
Publisher's Copyright Statement
- This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/