Factorial asset pricing models using statistical anomalies

Abstract

Although up to seven factors market, size, earnings, profitability, investment, momentum, and quality are used to explain asset returns mainly due to anomalies, there is no consensus in the financial literature on the suitability of the factors to include in asset pricing models. Empirical research has found that investors’ responses to market movements up and down are not symmetric. We show a new type of anomaly, statistical anomalies, resulting from decomposing asset returns into three independent time series: positive outliers (the good), negative outliers (the bad), and the remainder or Gaussian returns (the usual). Using a sample consisting of 49 equal-weighted US industrial portfolios with daily and monthly frequencies from 1969 to 2020, we find evidence that the good-usual-bad factor model exhibits fewer anomalies, better explanatory power, and greater robustness than the “magnificent seven” factors model. Our results are relevant to investors trading at less than monthly frequencies.