James W. Kolari

This paper investigates the ability of local factors in asset pricing models to price anomaly portfolio returns in the stock markets of eight large industrialized countries. Unlike traditional marketwide factors, local factors are developed from sample test assets. We employ two-factor models based on the ZCAPM of Kolari, Liu, and Huang (2021) that are derived from Black's (1972) zero-beta CAPM. Sample anomaly portfolio returns in each country are used to compute local factors for these models. Standard out-of-sample tests using Fama and MacBeth (1973) cross-sectional regressions show that local factors can effectively price anomalies in different countries. Relatively high goodness-of-fit is achieved with R 2 values in the range of 63 percent to 99 percent across countries. Also, estimated market prices of risk associated with factor loadings in the models are highly significant. As robustness checks, we conduct split sample tests as well as comparative tests to prominent multifactor models in the Japanese and the US stock markets. We conclude that ZCAPM-based models with local factors do a good job of explaining the cross section of anomaly portfolio returns in different countries.

Multifactor models have been developed over time to price nonfinancial stocks. This paper investigates their ability to price widely-perceived anomalous bank stocks. Based on standard cross-sectional regression tests, we find that, as factors are added to multifactor models, their ability to price bank stocks substantially improves. Even so, a recently proposed two-factor model normally outperforms other multifactor models with relatively high goodness-of-fit and statistical significance. Further graphs of average mispricing errors confirm these findings. We conclude that traditional multifactor models well explain the cross section of average bank stock returns.

This paper investigates how fixed rate versus variable rate loans affect bankprofitability. Because U.S. Call Reports do not contain this loan rate information, we implement a simulation-based procedure to estimate the share of variable rate loans at individual banks. Using quarterly data for U.S. commercial banks from 2002 to 2024, we find a highly significant relationship between net interest margins and variable rate loan shares in response to changes in the prime rate. With the exception of the COVID-19 pandemic, this relationship holds in different interest rate periods. We conclude that variable rate lending tends to increase bank profitability.

This paper investigates how fxed rate versus variable rate loans affect bankproftability. Because U.S. Call Reports do not contain this loan rate information,we implement a simulation-based procedure to estimate the share ofvariable rate loans at individual banks. Using quarterly data for U.S. commercialbanks from 2002 to 2024, we find a highly significant relationshipbetween net interest margins and variable rate loan shares in response tochanges in the prime rate. With the exception of the COVID-19 pandemic,this relationship holds in different interest rate periods. We conclude thatvariable rate lending tends to increase bank profitability.

We empirically construct beta-sorted portfolios that are relatively efficient for testing the CAPM. Our novel approach applies the well-known Gibbons, Ross, and Shanken (1989) test for zero joint alphas to iteratively construct these portfolios. Unlike traditional tests using inefficient portfolios, crosssectional regression tests of market factor loadings produce significant market prices of beta risk. Consistent with CAPM theory, average portfolio returns are positively related to both beta risk and total risk. Further tests show that average mispricing errors are substantially reduced using efficient portfolios.