Event studies on investor sentiment

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

No 21-33, Swiss Finance Institute Research Paper Series from 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.

Keywords: Investor sentiment; Event study; Polarity; Social Media; Microblogging; Natural Language Processing; Crowd Wisdom (search for similar items in EconPapers)
JEL-codes: C32 G11 G14 (search for similar items in EconPapers)
Pages: 37 pages
Date: 2021-04
New Economics Papers: this item is included in nep-big, nep-cwa and nep-fmk
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp2133

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