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New News is Bad News

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  • Paul Glasserman
  • Harry Mamaysky
  • Jimmy Qin

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.

Suggested Citation

  • Paul Glasserman & Harry Mamaysky & Jimmy Qin, 2023. "New News is Bad News," Papers 2309.05560, arXiv.org.
    • Handle: RePEc:arx:papers:2309.05560
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    References listed on IDEAS

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