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.
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