Wei Liu

The paper investigates the ability of asset pricing models to explain the cross section of average stock returns of anomaly portfolios. A large sample of 286 anomaly portfolios are employed. We perform out-of-sample cross-sectional regression tests of both prominent asset pricing models and a relatively new model dubbed the ZCAPM. Empirical tests strongly support the lesser known ZCAPM but not other multifactor models. Further analyses of out-of-sample mispricing errors of the models reveal that the ZCAPM provides much more accurate pricing of anomaly portfolios than other models. We conclude that anomalies are anomalous to popular multifactor models but not the ZCAPM. By implication, the efficient market hypothesis is supported.

This paper combines the CRSP market index with multiple factors to create a single multifactor market index. Empirical tests of different multifactor market indexes indicate that: (1) Sharpe ratios substantially increase and GRS test statistics decrease as multifactors are incrementally added to the CRSP index; and (2) the resultant multifactor market indexes are significantly priced in cross-sectional tests of associated beta loadings with t-values exceeding 3.0 in most cases.

In a recent book, Kolari et al. developed a new theoretical capital asset pricing model dubbed the ZCAPM. Based on out-of-sample cross-sectional tests using U.S. stocks, the ZCAPM consistently outperformed well-known multifactor models popular in the finance literature. This paper presents further evidence that expands their sample period from 1927 to 2020. Results are provided for the subperiods 1927 to 1964 and 1965 to 2020. Our results corroborate those of KLH. In cross-sectional tests, the ZCAPM outperforms the CAPM as well as the Fama and French three-factor model and Carhart four-factor model. Outperformance is found in terms of both higher goodness of fit and the statistical significance of factor loadings. Interestingly, the earlier subperiod results highlight problems with the endogeneity of test assets in cross-sectional tests of multifactor models.