Social-Media Sentiment, Limited Attention, and Stock Returns

73 Pages Posted: 13 Dec 2019 Last revised: 3 Jan 2020

See all articles by Woon Sau Leung

Woon Sau Leung

The University of Edinburgh Business School, The University of Edinburgh

Woon K. Wong

IMRU, Cardiff Business School

Gabriel Wong

Cardiff University - Cardiff Business School

Date Written: November 24, 2019

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

Suggested Citation

Leung, Woon Sau and Wong, Woon K. and Wong, Gabriel, Social-Media Sentiment, Limited Attention, and Stock Returns (November 24, 2019). Available at SSRN: https://ssrn.com/abstract=3492722 or http://dx.doi.org/10.2139/ssrn.3492722

Woon Sau Leung

The University of Edinburgh Business School, The University of Edinburgh ( email )

29 Buccleuch Pl
Edinburgh, Scotland EH8 9JS
United Kingdom

Woon K. Wong

IMRU, Cardiff Business School ( email )

Cardiff CF10 3EU
United Kingdom

Gabriel Wong (Contact Author)

Cardiff University - Cardiff Business School ( email )

Aberconway Building
Colum Drive
Cardiff, Wales CF10 3EU
United Kingdom

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