Journal
EUROPEAN FINANCIAL MANAGEMENT
Volume 26, Issue 3, Pages 753-777Publisher
WILEY
DOI: 10.1111/eufm.12245
Keywords
computational linguistics; investor sentiment; noise traders; social media bots; text classification
Categories
Ask authors/readers for more resources
This study examines the link between information spread by social media bots and stock trading. Based on a large sample of tweets mentioning 55 companies in the FTSE 100 composites, we find significant relations between bot tweets and stock returns, volatility, and trading volume at both daily and intraday levels. These results are also confirmed by an event study of stock response following abnormal increases in the volume of tweets. The findings are robust to various specifications, including controlling for traditional news channel, alternative measures of volatility, information flows in pretrading hours, and different measures of sentiment.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available