Growing the efficient frontier on panel trees

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

*Corresponding author for this work

    Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

    3 Citations (Scopus)
    21 Downloads (CityUHK Scholars)

    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 languageEnglish
    Article number104024
    JournalJournal of Financial Economics
    Volume167
    Online published18 Feb 2025
    DOIs
    Publication statusPublished - 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/

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