Forecasting the Equity Premium: Mind the News!

This paper introduces a novel strategy for predicting the monthly equity premium based on extracted news from more than 700,000 newspaper articles, 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 embedded in our extracted news are not captured by established equity premium predictors. Compared to the historical mean between 1999 and 2018, we find large out-of-sample (OOS) gains with an R²oos of 6.52% and sizeable utility gains for a mean-variance investor. The empirical results imply that (geo-)political rather than economic news are more valuable to forecast the equity premium out of sample. Prediction gains arise in down markets.