  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.
