Growing the Efficient Frontier on Panel Trees
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910813378314240 |
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| author | Cong, Lin William Feng, Guanhao He, Jingyu He, Xin |
| author_facet | Cong, Lin William Feng, Guanhao He, Jingyu He, Xin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_16730 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Growing the Efficient Frontier on Panel Trees Cong, Lin William Feng, Guanhao He, Jingyu He, Xin Machine Learning Pricing of Securities 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. |
| title | Growing the Efficient Frontier on Panel Trees |
| topic | Machine Learning Pricing of Securities |
| url | https://arxiv.org/abs/2501.16730 |