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Social media bots and stock markets

Author

Listed:
  • 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:

    1. Fan, Rui & Talavera, Oleksandr & Tran, Vu, 2023. "Information flows and the law of one price," International Review of Financial Analysis, Elsevier, vol. 85(C).
    2. Bennett, Donyetta & Mekelburg, Erik & Williams, T.H., 2023. "BeFi meets DeFi: A behavioral finance approach to decentralized finance asset pricing," Research in International Business and Finance, Elsevier, vol. 65(C).
    3. Kai-Cheng Yang & Emilio Ferrara & Filippo Menczer, 2022. "Botometer 101: social bot practicum for computational social scientists," Journal of Computational Social Science, Springer, vol. 5(2), pages 1511-1528, November.
    4. Costas Milas & Theodore Panagiotidis & Theologos Dergiades, 2021. "Does It Matter Where You Search? Twitter versus Traditional News Media," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 53(7), pages 1757-1795, October.
    5. Dosumu, Oluwatoyin Esther & Sakariyahu, Rilwan & Oyekola, Olayinka & Lawal, Rodiat, 2023. "Panic bank runs, global market contagion and the financial consequences of social media," Economics Letters, Elsevier, vol. 228(C).
    6. Menghan Zhang & Xue Qi & Ze Chen & Jun Liu, 2022. "Social Bots’ Involvement in the COVID-19 Vaccine Discussions on Twitter," IJERPH, MDPI, vol. 19(3), pages 1-14, January.
    7. Rui Fan & Oleksandr Talavera & Vu Tran, 2023. "Social media and price discovery: The case of cross‐listed firms," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 46(1), pages 151-167, February.
    8. Liang, Qi & Sun, Wenjia & Li, Wenyu & Yu, Fengyan, 2021. "Media effects matter: Macroeconomic announcements in the gold futures market," Economic Modelling, Elsevier, vol. 96(C), pages 1-12.
    9. 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.

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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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