Asset Pricing with Panel Tree Under Global Split Criteria

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-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%).

Research Area(s)

  • Asset Pricing, Basis Asset, Explainable AI, Latent Factor, Machine Learning, SDF

Bibliographic Note

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

Citation Format(s)

Asset Pricing with Panel Tree Under Global Split Criteria. / Cong, Lin William; Feng, Guanhao; He, Jingyu et al.
Social Science Research Network (SSRN), 2021.

Research output: Working PapersPreprint