Selecting and Testing Asset Pricing Models: A Stepwise Approach

TL;DR

This paper introduces a stepwise framework for selecting and testing asset pricing models that considers both selected and unselected factors, improving model performance and robustness.

Contribution

It proposes a sequential model selection method that updates baseline models by adding or removing factors, ensuring consistency and better asset pricing performance.

Findings

The 8-factor model passes asset pricing tests.

Workhorse models are often rejected in tests.

The proposed model shows robust out-of-sample performance.

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.