Social media bots and stock markets
Rui Fan (),
Oleksandr Talavera () and
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Rui Fan: School of Management, Swansea University
No 2018-30, Working Papers from Swansea University, School of Management
This study examines whether stock indicators are affected by information in social media such as Twitter. Using a daily sample of tweets with a FTSE 100 firm name over two years, we find insignificant associations between tweets/bot-tweets and stock returns whereas there is a strongly significant association with volatility and trading volume. Using a high-frequency sample, we detect a positive (negative) impact of tweets (bot-tweets) on stock returns. The impact of bot-tweets vanishes within 30 minutes. The results for volatility and trading volume are consistent with the daily data analysis. In addition, event study reveals a bounce-back pattern of price reactions in response to negative retweets. Abnormal increases in tweets/bottweets have significant effects on stock volatility, trading volume and liquidity.
Keywords: Social media bots; investor sentiments; noise traders; text classification; computational linguistics (search for similar items in EconPapers)
JEL-codes: G12 G14 L86 (search for similar items in EconPapers)
Pages: 48 pages
New Economics Papers: this item is included in nep-cfn, nep-cmp, nep-fmk, nep-ict and nep-mst
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https://rahwebdav.swan.ac.uk/repec/pdf/WP2018-30.pdf First version, 2018 (application/pdf)
Journal Article: Social media bots and stock markets (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:swn:wpaper:2018-30
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