In this paper, we investigate the predictive power of signals imputed from the cross-section of stock returns—namely cross-sectional volatility, skewness, and kurtosis—to forecast the time series. Adding to the existing literature, which documents cross-sectional volatility to forecast a decline in the equity premium with high in- and out-of-sample predictive power, 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 statistically and economically significant to the predictive quality of cross-sectional volatility by stabilizing its predictive performance and yielding a positive trend in in-sample and out-of-sample predictive quality for the equity premium. Additionally, we show that cross-sectional skewness and cross-sectional kurtosis span the predictive power of cross-sectional volatility for disaggregated returns with respect to size and value.

中文翻译：

---

更高的时刻很重要！横截面（较高）矩和股票收益的可预测性

在本文中，我们研究了从股票回报横截面（即横截面波动率、偏度和峰度）推算的信号的预测能力，以预测时间序列。除了记录横截面波动以预测股权溢价下降的现有文献之外，我们还强调了横截面偏度和横截面峰度的额外作用。应用主成分方法，我们表明，通过稳定其预测性能并在样本内和样本外预测质量中产生积极趋势，横截面较高矩在统计上和经济上显着增加了横截面波动率的预测质量为股权溢价。此外，