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Divergent opinions on social media

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  • Miwa, Kotaro

Abstract

This study analyzes the informational value of tweets in which opinions diverge from the consensus. I identify them using each firm's most positive and negative intraday social media sentiments. I find that these divergent opinions—specifically, negative ones—predict stock returns without subsequent reversals. Additionally, they contain incremental information on firm fundamentals identified by subsequent revisions to analysts' target prices and earnings forecasts. Finally, I find that return predictability is attributed to the fundamental information contained in the divergent opinions. My analysis sheds light on the role of divergent opinions on social media.

Suggested Citation

  • Miwa, Kotaro, 2023. "Divergent opinions on social media," International Review of Economics & Finance, Elsevier, vol. 86(C), pages 182-196.
    • Handle: RePEc:eee:reveco:v:86:y:2023:i:c:p:182-196
    DOI: 10.1016/j.iref.2023.03.004
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    Cited by:

    1. Cai, Yi & Tang, Zhenpeng & Chen, Ying, 2024. "Can real-time investor sentiment help predict the high-frequency stock returns? Evidence from a mixed-frequency-rolling decomposition forecasting method," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).

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    More about this item

    Keywords

    Social media; Divergent opinions; Linguistic tone; Firm fundamentals;
    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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