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,
来源
Digital Finance | 2024年 / 6卷 / 2期
关键词
Investor sentiment; Event study; Social media; Micro-blogs; Natural language processing; C55; G14; G17;
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.
引用
收藏
页码:249 / 281
页数:32
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