Selecting and Testing Asset-Pricing Models: A Stepwise Approach

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., capital asset-pricing model) 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 eight-factor model is not rejected and exhibits robust out-of-sample performance.

This paper was accepted by Lukas Schmid, finance.

Funding: G. Feng acknowledges financial support from the Hong Kong Research Grants Council [Grant GRF11502023] and the National Natural Science Foundation of China [Grant 72203190]. G. Feng is partially supported by the InnoHK Initiative of the Innovation and Technology Commission of the Hong Kong SAR and the Laboratory for AI-Powered Financial Technologies. The research of W. Lan was supported by the National Key R&D Program of China [Grant 2022YFA1003702]; the National Natural Science Foundation of China [Grants 72422020, 72333001, and 12531011]; and the Joint Lab of Data Science and Business Intelligence at Southwestern University of Finance and Economics. The research of H. Wang was partially supported by the National Natural Science Foundation of China [Grants 72495123 and 12271012].

Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.07804.