Higher Moments Matter! Cross-Sectional (Higher) Moments and the Predictability of Stock Returns

Abstract

In this paper we investigate the predictive power of cross-sectional volatility, skewness and kurtosis for future stock returns. Adding to the work of Maio (2016), who finds cross-sectional volatility to forecast a decline in the equity premium with high predictive power in-sample as well as out-of-sample, we highlight the additional role of cross-sectional skewness and cross-sectional kurtosis. Applying a principal component approach, we show that cross-sectional higher moments add to the predictive quality of cross-sectional volatility by stabilizing the predictive performance and yielding a positive trend in in-sample and out-of-sample predictive quality since the burst of the dot-com bubble. In particular, we observe cross-sectional skewness to span the predictive quality of cross-sectional volatility over short-forecasting horizons, whereas cross-sectional kurtosis significantly contributes to long-horizon forecasting of 12 months and above. Results are both statistically and economically significant.

Keywords: cross-sectional volatility, cross-sectional skewness, cross-sectional kurtosis, principal components, return dispersion, predictability of stock returns, out-ofsample predictability, equity premium

JEL Classification: G12, G14, G17