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The convergence and divergence of investors' opinions around earnings news: Evidence from a social network

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  • Giannini, Robert
  • Irvine, Paul
  • Shu, Tao

Abstract

We collect a unique dataset of Twitter posts to examine the change in investor disagreement around earnings announcements. We find that investors' opinions can either converge (reduced disagreement) or diverge (increased disagreement) around earnings announcements. The convergence and divergence of opinion has significant effects on trading volume and return. Consistent with theoretical predictions, both the convergence and divergence of opinion are associated with a greater volume reaction to earnings news. While the convergence of opinion is associated with lower earnings announcement returns, the divergence of opinion is associated with higher earnings announcement returns.

Suggested Citation

  • Giannini, Robert & Irvine, Paul & Shu, Tao, 2019. "The convergence and divergence of investors' opinions around earnings news: Evidence from a social network," Journal of Financial Markets, Elsevier, vol. 42(C), pages 94-120.
    • Handle: RePEc:eee:finmar:v:42:y:2019:i:c:p:94-120
    DOI: 10.1016/j.finmar.2018.12.003
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    8. Bouteska, Ahmed & Ha, Le Thanh & Bhuiyan, Faruk & Sharif, Taimur & Abedin, Mohammad Zoynul, 2024. "Contagion between investor sentiment and green bonds in China during the global uncertainties," International Review of Economics & Finance, Elsevier, vol. 93(PA), pages 469-484.
    9. Qing Liu & Hosung Son, 2024. "Data selection and collection for constructing investor sentiment from social media," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.
    10. Ding, Rong & Zhou, Hang & Li, Yifan, 2020. "Social media, financial reporting opacity, and return comovement: Evidence from Seeking Alpha," Journal of Financial Markets, Elsevier, vol. 50(C).
    11. Li, Xing & Hou, Keqiang, 2024. "Investors' opinion disagreement and abnormal trading around pre-earnings announcements," International Review of Financial Analysis, Elsevier, vol. 91(C).
    12. Juan Imbet & J. Anthony Cookson & Corbin Fox & Christoph Schiller & Javier Gil-Bazo, 2024. "Social Media as a Bank Run Catalyst," Post-Print hal-04660083, HAL.
    13. Laurent Bouton & Aniol Llorente-Saguer & Antonin Macé & Adam Meirowitz & Shaoting Pi & Dimitrios Xefteris, 2024. "Public Information as a Source of Disagreement," Working Papers halshs-04075483, HAL.
    14. Cookson, J. Anthony & Niessner, Marina & Schiller, Christoph M., 2022. "Can Social Media Inform Corporate Decisions? Evidence from Merger Withdrawals," SocArXiv 56yrj, Center for Open Science.
    15. Daniele Ballinari & Simon Behrendt, 2021. "How to gauge investor behavior? A comparison of online investor sentiment measures," Digital Finance, Springer, vol. 3(2), pages 169-204, June.
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    22. Liang, Chao & Tang, Linchun & Li, Yan & Wei, Yu, 2020. "Which sentiment index is more informative to forecast stock market volatility? Evidence from China," International Review of Financial Analysis, Elsevier, vol. 71(C).

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    More about this item

    Keywords

    Investor disagreement; Social media; Divergence of opinion; Convergence of opinion; Trading volume; Stock returns; Earnings announcement;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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