Diagnostics for Asset Pricing Models

75 Pages Posted: 22 Mar 2018 Last revised: 12 Aug 2023

See all articles by Ai He

Ai He

University of South Carolina - Darla Moore School of Business

Guofu Zhou

Washington University in St. Louis - John M. Olin Business School

Date Written: August 10, 2023

Abstract

The validity of asset pricing models implies white-noise pricing errors (PEs). However, we find that the PEs of six well-known factor models all exhibit a significant reversal pattern and are predictable by their lagged values up to 12 months. Moreover, the predictability of the PEs can produce substantial economic profits. Similar conclusions hold for recently developed machine learning models too. Additional analysis reveals that the significant PE profits cannot be explained by common behavioral biases. Our results imply that much remains to be done and there is much a need to develop new asset pricing models.

Keywords: Asset pricing tests, factor models, machine learning, pricing errors

JEL Classification: C53, G11, G12, G17

Suggested Citation

He, Ai and Zhou, Guofu, Diagnostics for Asset Pricing Models (August 10, 2023). Available at SSRN: https://ssrn.com/abstract= or http://dx.doi.org/10.2139/ssrn.3143752

Ai He

University of South Carolina - Darla Moore School of Business ( email )

1014 Greene Street
Columbia, SC 29208
United States

HOME PAGE: http://www.aihefinance.com/

Guofu Zhou (Contact Author)

Washington University in St. Louis - John M. Olin Business School ( email )

Washington University
Campus Box 1133
St. Louis, MO 63130-4899
United States
314-935-6384 (Phone)
314-658-6359 (Fax)

HOME PAGE: http://apps.olin.wustl.edu/faculty/zhou/

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