StockTwits classified sentiment and stock returns

Marc-Aurèle Divernois () and Damir Filipović ()
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Marc-Aurèle Divernois: Ecole Polytechnique Fédérale de Lausanne and Swiss Finance Institute
Damir Filipović: Ecole Polytechnique Fédérale de Lausanne and Swiss Finance Institute

Digital Finance, 2024, vol. 6, issue 2, No 3, 249-281

Abstract: 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.

Keywords: Investor sentiment; Event study; Social media; Micro-blogs; Natural language processing (search for similar items in EconPapers)
JEL-codes: C55 G14 G17 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s42521-023-00102-z

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