An augmented capital asset pricing model using new macroeconomic determinants

Abstract

Using the interview results of 26 experienced scholars, managers, and professional stock traders in conjunction with findings of recent studies in economics, we proposed an augmented asset pricing model using the macroeconomic determinants representing the macroeconomic state variables to explain the nexus between these risks and the U.S. stock returns. This non-traded factor model (MAPM) is inspired by and based on the macroeconomic theory and models and consists of the market return, U.S. prime rate, U.S. government long-term bond rate, and exchange rate of USD/EUR as in Eq. (1). Using the Bayesian approach (via two Bayes and t.Bayes estimators) and monthly returns of the S&P 500 stocks from 2007- 2019, our results showed the MAPM consistently yielded a statistically significant greater forecasting, explanatory power, and model adequacy compared to the most used capital asset pricing model (CAPM) in practice. Interestingly, our study found and confirmed (t-statistic > 3) that the last two macroeconomic determinants have a statistically significant positive effect on the stock returns, which also supports the MAPM. These findings suggest the MAPM is a more efficient and advantageous model compared to the CAPM. So, practitioners would be better off employing the MAPM over CAPM in practice and research.