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A retail investor in a cobweb of social networks

Author

Listed:
  • Tamara Teplova
  • Aleksandr Tomtosov
  • Tatiana Sokolova

Abstract

In this study, using AI, we empirically examine the irrational behaviour, specifically attention-driven trading and emotion-driven trading such as consensus trading, of retail investors in an emerging stock market. We used a neural network to assess the tone of messages on social media platforms and proposed a novel Hype indicator that integrates metrics of investor attention and sentiment. The sample of messages, which are written in Russian with slang expressions, was retrieved from a unique dataset of social network communication of investors in the Russian stock market. Applying different portfolio designs, we evaluated the effectiveness of the new Hype indicator against the factors of momentum, volatility, and trading volume. We found the possibility of building a profitable trading strategy based on the Hype indicator over a 6-month time horizon. Over short periods, the Hype indicator allows investors to earn more by buying stocks of large companies, and over «longer» periods, this indicator tends to perform better for illiquid stocks of small companies. As consensus trading tends to produce negative returns, the investment strategy of ‘Go against the crowd’ proves rewarding in the medium term of 3 months.

Suggested Citation

  • Tamara Teplova & Aleksandr Tomtosov & Tatiana Sokolova, 2022. "A retail investor in a cobweb of social networks," PLOS ONE, Public Library of Science, vol. 17(12), pages 1-26, December.
    • Handle: RePEc:plo:pone00:0276924
    DOI: 10.1371/journal.pone.0276924
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    References listed on IDEAS

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    1. Fumiko Takeda & Hiroaki Yamazaki, 2006. "Stock Price Reactions to Public TV Programs on Listed Japanese Companies," Economics Bulletin, AccessEcon, vol. 13(7), pages 1-7.
    2. repec:ebl:ecbull:v:13:y:2006:i:7:p:1-7 is not listed on IDEAS
    3. Djalilov, Abdulaziz & Ülkü, Numan, 2021. "Individual investors’ trading behavior in Moscow Exchange and the COVID-19 crisis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 31(C).
    4. Xu, Yongan & Liang, Chao & Li, Yan & Huynh, Toan L.D., 2022. "News sentiment and stock return: Evidence from managers’ news coverages," Finance Research Letters, Elsevier, vol. 48(C).
    5. Umar, Zaghum & Gubareva, Mariya & Yousaf, Imran & Ali, Shoaib, 2021. "A tale of company fundamentals vs sentiment driven pricing: The case of GameStop," Journal of Behavioral and Experimental Finance, Elsevier, vol. 30(C).
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    Cited by:

    1. Tamara Teplova & Maksim Fayzulin, 2025. "Decoding Russian stock market trends through ensemble methods and sentiment analysis of social media," Annals of Operations Research, Springer, vol. 353(3), pages 1123-1172, October.

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