New News is Bad News

Paul Glasserman, Harry Mamaysky and Jimmy Qin

Papers from arXiv.org

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.

Date: 2023-09
New Economics Papers: this item is included in nep-big, nep-cmp and nep-rmg
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