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Choosing News Topics to Explain Stock Market Returns

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  • Paul Glasserman
  • Kriste Krstovski
  • Paul Laliberte
  • Harry Mamaysky

Abstract

We analyze methods for selecting topics in news articles to explain stock returns. We find, through empirical and theoretical results, that supervised Latent Dirichlet Allocation (sLDA) implemented through Gibbs sampling in a stochastic EM algorithm will often overfit returns to the detriment of the topic model. We obtain better out-of-sample performance through a random search of plain LDA models. A branching procedure that reinforces effective topic assignments often performs best. We test methods on an archive of over 90,000 news articles about S&P 500 firms.

Suggested Citation

  • Paul Glasserman & Kriste Krstovski & Paul Laliberte & Harry Mamaysky, 2020. "Choosing News Topics to Explain Stock Market Returns," Papers 2010.07289, arXiv.org.
    • Handle: RePEc:arx:papers:2010.07289
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    References listed on IDEAS

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    1. Paul C. Tetlock & Maytal Saar‐Tsechansky & Sofus Macskassy, 2008. "More Than Words: Quantifying Language to Measure Firms' Fundamentals," Journal of Finance, American Finance Association, vol. 63(3), pages 1437-1467, June.
    2. Campbell, John Y, 1991. "A Variance Decomposition for Stock Returns," Economic Journal, Royal Economic Society, vol. 101(405), pages 157-179, March.
    3. Paul C. Tetlock, 2014. "Information Transmission in Finance," Annual Review of Financial Economics, Annual Reviews, vol. 6(1), pages 365-384, December.
    4. Tim Loughran & Bill Mcdonald, 2011. "When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks," Journal of Finance, American Finance Association, vol. 66(1), pages 35-65, February.
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    Cited by:

    1. Charles W. Calomiris & Nida Çakır Melek & Harry Mamaysky, 2021. "Predicting the Oil Market," NBER Working Papers 29379, National Bureau of Economic Research, Inc.
    2. Francisco Caio Lima Paiva & Leonardo Kanashiro Felizardo & Reinaldo Augusto da Costa Bianchi & Anna Helena Reali Costa, 2021. "Intelligent Trading Systems: A Sentiment-Aware Reinforcement Learning Approach," Papers 2112.02095, arXiv.org.
    3. Thierry Warin & Aleksandar Stojkov, 2021. "Machine Learning in Finance: A Metadata-Based Systematic Review of the Literature," JRFM, MDPI, vol. 14(7), pages 1-31, July.

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