New News is Bad News

TL;DR

This paper demonstrates that increased news novelty, measured via entropy from news text, predicts negative stock and macroeconomic outcomes, with entropy serving as a superior out-of-sample predictor and revealing a negative risk premium.

Contribution

It introduces a novel entropy-based measure of news novelty using neural networks and shows its predictive power for market returns beyond standard measures.

Findings

Entropy predicts negative market returns and macroeconomic outcomes.

Assets with positive entropy exposure hedge aggregate news risk.

Entropy risk is not explained by existing financial factors.

Abstract

An increase in the novelty of news predicts negative stock market returns and negative macroeconomic outcomes over the next year. We quantify news novelty - changes in the distribution of news text - through an entropy measure, calculated using a recurrent neural network applied to a large news corpus. Entropy is a better out-of-sample predictor of market returns than a collection of standard measures. Cross-sectional entropy exposure carries a negative risk premium, suggesting that assets that positively covary with entropy hedge the aggregate risk associated with shifting news language. Entropy risk cannot be explained by existing long-short factors.