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Event studies on investor sentiment

Author

Listed:
  • Marc-Aurèle Divernois

    (EPFL; Swiss Finance Institute)

  • Damir Filipović

    (Ecole Polytechnique Fédérale de Lausanne; Swiss Finance Institute)

Abstract

60 million tweets are scraped from Stocktwits.com over 10 years and classified into bullish, bearish or neutral classes to create firm-individual polarity time-series. Changes in polarity are associated with changes of the same sign in contemporaneous stock returns. On average, polarity is not able to predict next day stock returns but when we focus on specific events (defined as sudden peak of tweet activity), polarity has predictive powers on abnormal returns. Finally, we show that bad events act more as surprises than good events.

Suggested Citation

  • Marc-Aurèle Divernois & Damir Filipović, 2021. "Event studies on investor sentiment," Swiss Finance Institute Research Paper Series 21-33, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp2133
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    File URL: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3657034
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    More about this item

    Keywords

    Investor sentiment; Event study; Polarity; Social Media; Microblogging; Natural Language Processing; Crowd Wisdom;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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