Social-Media Sentiment, Portfolio Complexity, and Stock Returns

73 Pages Posted: 13 Dec 2019 Last revised: 17 Jun 2020

See all articles by Woon Sau Leung

Woon Sau Leung

The University of Edinburgh Business School, The University of Edinburgh

Gabriel Wong

Cardiff University - Cardiff Business School

Woon K. Wong

IMRU, Cardiff Business School

Date Written: November 24, 2019

Abstract

Using tweets from StockTwits and machine-learning classification techniques, 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 follow increases. The return predictability appears to stem from users’ ability to forecast future earnings. Further tests reveal that reduced predictability due to stock coverage is significant only for firms that are complex, opaque, and thus hard to analyze. The evidence suggests that stock analysis by users with a more complex portfolio is inferior due to attention and time constraints.

Keywords: Social-media sentiment, StockTwits, Behavioral finance, Limited attention, Stock returns

JEL Classification: G11, G12, G41

Suggested Citation

Leung, Woon Sau and Wong, Gabriel and Wong, Woon K., Social-Media Sentiment, Portfolio Complexity, 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

Gabriel Wong (Contact Author)

Cardiff University - Cardiff Business School ( email )

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

Woon K. Wong

IMRU, Cardiff Business School ( email )

Cardiff CF10 3EU
United Kingdom

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