Growing the Efficient Frontier on Panel Trees

2025-01-28

AI Summary

Identifying investment strategies and recovering pricing kernels from high-dimensional, imbalanced panel data of asset returns remains challenging. Method: This paper proposes P-Trees—a class of interpretable tree-based models incorporating economic priors. Innovatively introducing tree structures into asset pricing frontiers, P-Trees employ adaptive panel partitioning, economically constrained node splitting, and efficient-frontier-directed objective optimization to generate sparse, tradable, economically interpretable factors with provable pricing-kernel recovery capability. Results: Empirically, P-Tree-derived tangency portfolios outperform both mainstream explicit and implicit factor models in cross-sectional pricing accuracy and portfolio performance; achieve out-of-sample Sharpe ratios comparable to overparameterized large-scale models; and generate statistically significant alpha—robustly unexplained by conventional benchmark asset pricing models.

Technology Category

• Machine Learning: Kernel Methods
• Game Theory and Economic Paradigms: Behavioral Game Theory
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

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.

Problem

• High-dimensional data analysis
• Imbalanced data challenge
• Investment strategy optimization

Innovation

• P-Tree Model
• Imbalanced Data Analysis
• High-Dimensional Data Handling