Further Tests of Asset Pricing Models and Anomaly Portfolio Returns

James W. Kolari (), Wei Liu, Jianhua Z. Huang () and Huiling Liao ()
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James W. Kolari: Texas A&M University, Mays Business School
Wei Liu: Texas A&M University, Mays Business School
Jianhua Z. Huang: The Chinese University of Hong Kong, Shenzhen, School of Artificial Intelligence and School of Data Science
Huiling Liao: Illinois Institute of Technology, Department of Applied Mathematics

Chapter Chapter 6 in Asset Pricing Models and Market Efficiency, 2026, pp 141-167 from Springer

Abstract: Abstract This chapter extends the cross-sectional tests of different asset pricing models in the previous chapter using different test asset portfolios. In Chapter 5, we conducted tests based on 133 U.S. anomaly portfolios constructed by Chen and Zimmerman (2020) and 153 anomaly portfolios by Jensen, Kelly, and Pedersen (Jensen and Kelly (2023)). Here, we report further evidence using other test asset portfolios. First, we employ commonly used portfolios in published asset pricing studies reviewed in Chapter 2. To do this, a variety of size, value, profitability, capital investment, and momentum stock portfolios are downloaded from Kenneth French’s database website. With the exception of momentum portfolios, these test assets are not long/short portfolios as in the case of anomaly portfolios; instead, they are portfolios that are used in the construction of anomaly portfolios. For example, Fama and French (1992, 1993) sorted individual stocks by market capitalization into size deciles. The top/bottom three size deciles are used to construct the long/short size factor in their multifactor asset pricing models. Thus, we take a more granular look at the returns of long and short portfolios that are used in forming anomaly portfolios. Second, we utilize industry portfolios available on French’s website. Industry portfolios’ returns are notorious for being difficult to explain with any asset pricing model and therefore can be considered to be anomalous. Finally, we add to the previous chapter by testing a sample of 86 anomaly portfolio returns in Japan. These long/short portfolios are available online at the website provided by Jensen et al. As in Chapter 5, our goal is to show that the ZCAPM does a good job of explaining stock portfolios’ returns. In turn, the efficient markets hypothesis holds in the sense that rational investors and systematic market risk explain stock returns. Behavioral explanations based on irrational investors subject to psychological biases are not needed to explain long/short portfolio stock returns for the most part.

Keywords: Anomalies; Artificial intelligence (AI); Industry portfolios; Japanese stock market; Market return dispersion; Machine learning (ML); Mispricing errors; Multifactor models; Out-of-sample cross-sectional regression test; ZCAPM; Zeta risk (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-3-031-92901-4_6

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