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News vs. Sentiment: Predicting Stock Returns from News Stories

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  • Steven L. Heston
  • Nitish Ranjan Sinha

Abstract

The authors used a dataset of more than 900,000 news stories to test whether news can predict stock returns. They measured sentiment with a proprietary Thomson Reuters neural network and found that daily news predicts stock returns for only one to two days, confirming previous research. Weekly news, however, predicts stock returns for one quarter. Positive news stories increase stock returns quickly, but negative stories receive a long-delayed reaction. Much of the delayed response to news occurs around the subsequent earnings announcement. Disclosure: The authors report no conflicts of interest. Editor’s Note Submitted 10 November 2015 Accepted 28 December 2016 by Stephen J. Brown

Suggested Citation

  • Steven L. Heston & Nitish Ranjan Sinha, 2017. "News vs. Sentiment: Predicting Stock Returns from News Stories," Financial Analysts Journal, Taylor & Francis Journals, vol. 73(3), pages 67-83, July.
    • Handle: RePEc:taf:ufajxx:v:73:y:2017:i:3:p:67-83
    DOI: 10.2469/faj.v73.n3.3
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    Cited by:

    1. Mazzotta, Stefano, 2024. "Immigration Narrative and Home Prices," Journal of Behavioral and Experimental Finance, Elsevier, vol. 43(C).
    2. Pham, Linh & Kamal, Javed Bin, 2024. "Blessings or curse: How do media climate change concerns affect commodity tail risk spillovers?," Journal of Commodity Markets, Elsevier, vol. 34(C).
    3. Eryka Probierz & Adam Galuszka & Katarzyna Klimczak & Karol Jedrasiak & Tomasz Wisniewski & Tomasz Dzida, 2021. "Financial Sentiment on Twitter's Community and it's Connection to Polish Stock Market Movements in Context of Behavior Modelling," European Research Studies Journal, European Research Studies Journal, vol. 0(4 - Part ), pages 56-65.
    4. Muhammad Ateeq ur REHMAN & Masood AHMAD & Furman ALI & Habib AHMAD, 2024. "Expose the Hidden : Investor Sentiment and Anomaly Strategies in Emerging Market," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 63-81, December.
    5. du Plessis, Emile, 2024. "Reading between the lines: Quantitative text analysis of banking crises," Research in Economics, Elsevier, vol. 78(4).
    6. Peng, Yueqian & Shi, Li & Shi, Xiaojun & Tan, Songtao, 2024. "Tone or term: Machine-learning text analysis, featured vocabulary extraction, and evidence from bond pricing in China," Journal of Empirical Finance, Elsevier, vol. 78(C).

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