Forecasting the Equity Premium: Mind the News!

Abstract

We introduce a novel strategy to predict monthly equity premia that is based on extracted news from more than 700,000 newspaper articles, which were published in The New York Times and Washington Post between 1980 and 2018. We propose a flexible data-adaptive switching approach to map a large set of different news-topics into forecasts of aggregate stock returns. The information that is embedded in our extracted news is not captured by established economic predictors. Compared to the prevailing historical mean between 1999 and 2018, we find large out-of-sample (OOS) gains with an R²_OOS of 6.52% and sizeable utility gains for a mean-variance investor. The empirical results indicate that geopolitical news are at times more valuable than economic news to predict the equity premium and we also find that
forecasting gains arise in down markets.

Keywords: Topic Modeling, Big Data, Return Predictability, Text as Data

JEL Classification: G11, G12, G17, C53