English

Growing the Efficient Frontier on Panel Trees

Machine Learning 2025-02-05 v2 Pricing of Securities Machine Learning

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

Cite

@article{arxiv.2501.16730,
  title  = {Growing the Efficient Frontier on Panel Trees},
  author = {Lin William Cong and Guanhao Feng and Jingyu He and Xin He},
  journal= {arXiv preprint arXiv:2501.16730},
  year   = {2025}
}
R2 v1 2026-06-28T21:21:26.804Z