Asset Pricing with Panel Tree Under Global Split Criteria

77 Pages Posted: 27 Oct 2021 Last revised: 19 Dec 2022

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 - College of Finance and Statistics; City University of Hong Kong (CityU)

Multiple version iconThere are 2 versions of this paper

Date Written: Nov 15, 2022

Abstract

We develop a new class of tree-based models (P-Tree) for analyzing (unbalanced) panel data utilizing global (instead of local) split criteria that incorporate economic guidance to guard against overfitting while preserving interpretability. We grow a P-Tree top-down to split the cross section of asset returns to construct stochastic discount factor and test assets, generalizing sequential security sorting and visualizing (asymmetric) nonlinear interactions among firm characteristics and macroeconomic states. Data-driven P-Tree models reveal that idiosyncratic volatility and earnings-to-price ratio interact to drive cross-sectional return variations in U.S. equities; market volatility and inflation constitute the most critical regime-switching that asymmetrically interact with characteristics. P-Trees outperform most known observable and latent factor models in pricing individual stocks and test portfolios, while delivering transparent trading strategies and risk-adjusted investment outcomes (e.g., out-of-sample annualized Sharp ratios of about 3 and monthly alpha around 0.8%).

Keywords: Asset Pricing, Basis Asset, Explainable AI, Latent Factor, Machine Learning, SDF

JEL Classification: C1, G11, G12

Suggested Citation

Cong, Lin and Feng, Guanhao and He, Jingyu and He, Xin, Asset Pricing with Panel Tree Under Global Split Criteria (Nov 15, 2022). 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 )

83 Tat Chee Avenue
Hong Kong

Jingyu He

City University of Hong Kong (CityU) ( email )

83 Tat Chee Avenue
Hong Kong
Hong Kong

Xin He

Hunan University - College of Finance and Statistics ( email )

109th Shijiachong Road, Yuelu District
Changsha, Hunan 410006
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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