Growing the Efficient Frontier on Panel Trees

Research output: Working PapersPreprint

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Original languageEnglish
PublisherSocial Science Research Network (SSRN)
Publication statusOnline published - 27 Oct 2021


We develop a new class of tree-based models (P-Trees) for analyzing (unbalanced) panel data using economically guided, global (instead of local) split criteria that guard against overfitting while preserving sparsity and interpretability. To generalize security sorting and better estimate the efficient frontier, we grow a P-Tree top-down to split the cross section of asset returns to construct test assets and recover the stochastic discount factor under the mean-variance efficient framework, visualizing (asymmetric) nonlinear interactions among firm characteristics. When applied to U.S. equities, boosted (multi-factor) P-Trees significantly advance the efficient frontier relative to those constructed with established factors and common test assets. P-Tree test assets are diversified and exhibit significant unexplained alphas against benchmark models. The unified P-Tree factors outperform most known observable and latent factor models in pricing cross-sectional returns, delivering transparent and effective trading strategies. Beyond asset pricing, our framework offers a more interpretable and computationally efficient alternative to recent machine learning and AI models for analyzing panel data through goal-oriented, high-dimensional clustering.

Research Area(s)

  • Characteristics, Decision Tree, Factors, Interpretable AI, Test Assets

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Research Unit(s) information for this publication is provided by the author(s) concerned.

Citation Format(s)

Growing the Efficient Frontier on Panel Trees. / Cong, Lin William; Feng, Guanhao; He, Jingyu et al.
Social Science Research Network (SSRN), 2021.

Research output: Working PapersPreprint