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

页码：249 / 281

页数：32

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