Narrative Asset Pricing: Interpretable Systematic Risk Factors from News Text

We seek fundamental risks from news text. Conceptually, news is closely related to the idea of systematic risk, in particular the "state variables" in the ICAPM. News captures investors' concerns about future investment opportunities, and hence drives the current pricing kernel. This paper demonstrates a way to extract a parsimonious set of risk factors and eventually a univariate pricing kernel from news text. The state variables are reduced and selected from the variations in attention allocated to different news narratives. As a result, the risk factors attain clear text-based interpretability as well as top-of-the-line asset pricing performance. The empirical method integrates topic modeling (LDA), latent factor analysis (IPCA), and variable selection (group lasso).