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Social media bots and stock markets

Author

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  • Rui Fan

    () (School of Management, Swansea University)

  • Oleksandr Talavera

    () (School of Management, Swansea University)

  • Vu Tran

    () (School of Management, Swansea University)

Abstract

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.

Suggested Citation

  • Rui Fan & Oleksandr Talavera & Vu Tran, 2018. "Social media bots and stock markets," Working Papers 2018-30, Swansea University, School of Management.
  • Handle: RePEc:swn:wpaper:2018-30
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    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. What moves markets more, Twitter or traditional news?
      by ? in EUROPP European Politics and Policy on 2018-12-08 07:29:35

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    Cited by:

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    3. Costas Milas & Theodore Panagiotidis & Theologos Dergiades, 2018. "Twitter versus Traditional News Media: Evidence for the Sovereign Bond Markets," Working Paper series 18-42, Rimini Centre for Economic Analysis.
    4. Costas Milas & Theodore Panagiotidis & Theologos Dergiades, 2021. "Does it Matter where you Search? Twitter versus Traditional News Media," Discussion Paper Series 2021_04, Department of Economics, University of Macedonia, revised Feb 2021.
    5. Rui Fan & Oleksandr Talavera & Vu Tran, 2020. "Social media and price discovery: the case of cross-listed firms," Discussion Papers 20-05, Department of Economics, University of Birmingham.

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    More about this item

    Keywords

    Social media bots; investor sentiments; noise traders; text classification; computational linguistics;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software

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