Social-Media Sentiment, Limited Attention, and Stock Returns

Abstract

Using tweets from StockTwits and machine-learning techniques in classifying them, we find that social-media sentiment predicts positively and significantly future stock returns, and, importantly, such positive predictability decreases when the number of stocks users tweeted about increases. Such return predictability likely stems from users’ ability in forecasting future earnings. Additional tests reveal that the reduced predictability due to stock coverage is significant only for firms that are complex, opaque, and thus hard-to-analyze. Together, our evidence suggests that stock analysis by users following many stocks is inferior due to attention and time constraints.

Keywords: Tweet sentiment, StockTwits, Behavioral finance, Limited Attention, Stock returns

JEL Classification: G11, G12, G41