Super Model?

Is there a clearly superior multi-factor model of next-month stock returns? In the November 2023 revision of their paper entitled “A Quantum Leap in Asset Pricing: Explaining Anomalous Returns”, James Kolari, Jianhua Huang, Wei Liu and Huiling Liao compare abilities of the following eight multi-factor models of stock returns to explain 133 stubbornly persistent stock return anomalies via out-of-sample (one-month-ahead) cross-sectional regression tests:

1. Capital Asset Pricing Model (CAPM, 1-factor)
2. Fama-French 3-factor model (FF3, adding size and book-to-market)
3. Carhart 4-factor model (adding momentum to FF3)
4. Fama-French 5-factor model (FF5, adding profitability and investment to FF3)
5. Fama-French 6-factor model (FF6, adding momentum to FF5)
6. Hou-Xue-Zhang 4-factor model (market, size, profitability, investment)
7. Stambaugh-Yuan 4-factor model (market, size, management, performance)
8. Kolari-Liu-Huang ZCAPM (market, return dispersion)

They specify return dispersion as the standard deviation of daily returns across all stocks aggregated over the past year. They call dependence of each stock’s return on this metric the stock’s zeta (analogous to beta for the interaction of a stock’s return with the market return). The goal of the study is to identify the model that best account for those pricing anomalies that are historically most difficult to explain. Using publicly available daily return data for 133 stock return anomalies and for the factors used in the above multi-factor models as available during July 1972 through December 2021, they find that: (more…)