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StockTwits classified sentiment and stock returns

Author

Listed:
  • Marc-Aurèle Divernois

    (Ecole Polytechnique Fédérale de Lausanne and Swiss Finance Institute)

  • Damir Filipović

    (Ecole Polytechnique Fédérale de Lausanne and Swiss Finance Institute)

Abstract

We classify the sentiment of a large sample of StockTwits messages as bullish, bearish or neutral, and create a stock-aggregate daily sentiment polarity measure. Polarity is positively associated with contemporaneous stock returns. On average, polarity is not able to predict next-day stock returns. But when we condition on specific events, defined as sudden peaks of message volume, polarity has predictive power on abnormal returns. Polarity-sorted portfolios illustrate the economic relevance of our sentiment measure.

Suggested Citation

  • Marc-Aurèle Divernois & Damir Filipović, 2024. "StockTwits classified sentiment and stock returns," Digital Finance, Springer, vol. 6(2), pages 249-281, June.
    • Handle: RePEc:spr:digfin:v:6:y:2024:i:2:d:10.1007_s42521-023-00102-z
    DOI: 10.1007/s42521-023-00102-z
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Investor sentiment; Event study; Social media; Micro-blogs; Natural language processing;
    All these keywords.

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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