StockTwits classified sentiment and stock returns

被引：0
作者：
Marc-Aurèle Divernois [1 ]
Damir Filipović [1 ]
机构：
[1] Ecole Polytechnique Fédérale de Lausanne and Swiss Finance Institute, Lausanne
来源：
Digital Finance | 2024年 / 6卷 / 2期
关键词：
C55; Event study; G14; G17; Investor sentiment; Micro-blogs; Natural language processing; Social media;
D O I：
10.1007/s42521-023-00102-z
中图分类号：
学科分类号：
摘要：
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. © The Author(s) 2023.
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页码：249 / 281
页数：32
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