Models for expected returns with statistical factors

Abstract

In this paper we propose factor-models assembled out of three new factors and evaluate them on European Equities. The new factors are built from statistical measurements on stock prices, in particular, coefficient of variation, skewness and kurtosis. Data come from Reuters, correspond to nearly 2000 EU companies and span from Jan-2008 to Feb-2018. Regarding methodology, we propose a non-parametric resampling procedure that accounts for time dependency in order to test the validity of the model and the significance of the parameters involved. We compare our bootstrap- based inferential results with classical proposals (based on F-statistics). Methods under assessment are Time-series regression, Cross-Sectional regression and the Fama-MacBeth procedure. The main findings indicate that the two factors that better improve the CAPM-model with regard to the adjusted R2 in the time-series regressions are the skewness and the cofficient of variation. For this reason, a model including those two factors together with the market is thoroughly studied.