Stock return predictability using economic narrative: Evidence from energy sectors

Abstract

This paper applies the Narrative-based Energy General Index (NEG) to forecast stock returns in the energy industry. The index is constructed using natural language processing (NLP) techniques applied to news topics from The Wall Street Journal. The results indicate that NEG outperforms in predicting future returns of the energy industry in both in-sample and out-of-sample, and the predictive power surpasses that of other macroeconomic variables. The asset allocation exercise demonstrates the substantial economic value of NEG. Furthermore, we document that NEG not only exhibits superior predictive power for energy sector returns but also provides valuable insights for the whole stock market.