Growing the Efficient Frontier on Panel Trees

76 Pages Posted: 27 Oct 2021 Last revised: 30 Nov 2023

See all articles by Lin William Cong

Lin William Cong

Cornell University - Samuel Curtis Johnson Graduate School of Management; National Bureau of Economic Research (NBER)

Guanhao Feng

City University of Hong Kong (CityU)

Jingyu He

City University of Hong Kong (CityU)

Xin He

Hunan University; City University of Hong Kong (CityU)

Multiple version iconThere are 2 versions of this paper

Date Written: November 1, 2023

Abstract

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.

Keywords: Characteristics, Decision Tree, Factors, Interpretable AI, Test Assets.

JEL Classification: C1, G11, G12

Suggested Citation

Cong, Lin and Feng, Guanhao and He, Jingyu and He, Xin, Growing the Efficient Frontier on Panel Trees (November 1, 2023). Available at SSRN: https://ssrn.com/abstract=3949463 or http://dx.doi.org/10.2139/ssrn.3949463

Lin Cong

Cornell University - Samuel Curtis Johnson Graduate School of Management ( email )

Ithaca, NY 14853
United States

HOME PAGE: http://www.linwilliamcong.com/

National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Guanhao Feng (Contact Author)

City University of Hong Kong (CityU) ( email )

Hong Kong

Jingyu He

City University of Hong Kong (CityU) ( email )

83 Tat Chee Avenue
Hong Kong
Hong Kong

Xin He

Hunan University ( email )

Changsha, Hunan
China

HOME PAGE: http://www.xinhesean.com

City University of Hong Kong (CityU) ( email )

83 Tat Chee Avenue
Kowloon
Hong Kong

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