Testing extensions of Fama & French models: A quantile regression approach

This research compares twelve different factor models in explaining variations in U.S. sector returns between Nov. 1989 and Feb. 2014 using the quantile regression approach. Specifically, these models rely on the Fama and French three- and five-factor models (1993 and 2015), adding other explanatory factors such as nominal interest rates, and its components: real interest and expected inflation rates, the Carhart (1997) risk factor for momentum and for momentum reversal and the Pastor and Stambaugh (2003) traded liquidity factor. Additionally, for robustness, this paper includes the alternative Amihud (2002) illiquidity measure, as well as analyses the validity of the proposed models, depending upon the stage of the economy, by distinguishing between recession and expansion sub-periods. This paper concludes that the most complete model shows the highest explanatory power. Furthermore, the extreme quantile 0.1 of the return distribution shows the best results in all the factor models, and some sectors, such as Industrials and Financials, consistently evidence more statistically significant coefficients and thus higher Adj. R2 values in the total sample as well as in recession and expansion sub-periods. In contrast, the Utilities sector consistently shows the lowest explanatory power for all the models, quantiles and periods.