Selecting and Testing Asset Pricing Models: A Stepwise Approach

2026-01-15

AI Summary

This study addresses the limitation of existing asset pricing models, which neglect unselected candidate factors during factor selection, thereby compromising pricing performance. The authors propose a stepwise optimization framework grounded in the duality of asset pricing: by jointly examining the efficient frontiers of test assets and all candidate factors, the approach simultaneously evaluates a model’s pricing accuracy on both selected assets and excluded factors, iteratively refining the benchmark model. This method unifies the minimization of cross-sectional pricing errors with the maximization of the model’s Sharpe ratio, ensuring selection consistency. Empirical results demonstrate that prominent factor models are significantly rejected, whereas the authors’ constructed eight-factor model withstands statistical tests and exhibits robust out-of-sample performance.

Technology Category

• Search and Optimization: Non-convex Optimization
• Reasoning under Uncertainty: Stochastic Optimization
• Game Theory and Economic Paradigms: Mechanism Design

Application Category

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

Abstract

The asset pricing literature emphasizes factor models that minimize pricing errors but overlooks unselected candidate factors that could enhance the performance of test assets. This paper proposes a framework for factor model selection and testing by (i) selecting the optimal model that spans the joint efficient frontier of test assets and all candidate factors, and (ii) testing pricing performance on both test assets and unselected candidate factors. Our framework updates a baseline model (e.g., CAPM) sequentially by adding or removing factors based on asset pricing tests. Ensuring model selection consistency, our framework utilizes the asset pricing duality: minimizing cross-sectionally unexplained pricing errors aligns with maximizing the Sharpe ratio of the selected factor model. Empirical evidence shows that workhorse factor models fail asset pricing tests, whereas our proposed 8-factor model is not rejected and exhibits robust out-of-sample performance.

Problem

• asset pricing models
• factor selection
• pricing errors
• candidate factors
• model testing

Innovation

• factor model selection
• asset pricing duality
• efficient frontier spanning
• sequential model updating
• out-of-sample performance