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Analyzing stock market trends using social media user moods and social influence

Author

Listed:
  • Daifeng Li
  • Yintian Wang
  • Andrew Madden
  • Ying Ding
  • Jie Tang
  • Gordon Guozheng Sun
  • Ning Zhang
  • Enguo Zhou

Abstract

Information from microblogs is gaining increasing attention from researchers interested in analyzing fluctuations in stock markets. Behavioral financial theory draws on social psychology to explain some of the irrational behaviors associated with financial decisions to help explain some of the fluctuations. In this study we argue that social media users who demonstrate an interest in finance can offer insights into ways in which irrational behaviors may affect a stock market. To test this, we analyzed all the data collected over a 3‐month period in 2011 from Tencent Weibo (one of the largest microblogging websites in China). We designed a social influence (SI)‐based Tencent finance‐related moods model to simulate investors' irrational behaviors, and designed a Tencent Moods‐based Stock Trend Analysis (TM_STA) model to detect correlations between Tencent moods and the Hushen‐300 index (one of the most important financial indexes in China). Experimental results show that the proposed method can help explain the data fluctuation. The findings support the existing behavioral financial theory, and can help to understand short‐term rises and falls in a stock market. We use behavioral financial theory to further explain our findings, and to propose a trading model to verify the proposed model.

Suggested Citation

  • Daifeng Li & Yintian Wang & Andrew Madden & Ying Ding & Jie Tang & Gordon Guozheng Sun & Ning Zhang & Enguo Zhou, 2019. "Analyzing stock market trends using social media user moods and social influence," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 70(9), pages 1000-1013, September.
    • Handle: RePEc:bla:jinfst:v:70:y:2019:i:9:p:1000-1013
    DOI: 10.1002/asi.24173
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    Cited by:

    1. Na, Haejung & Kim, Soonho, 2021. "Predicting stock prices based on informed traders’ activities using deep neural networks," Economics Letters, Elsevier, vol. 204(C).
    2. Li, Jiaqi & Ahn, Hee-Joon, 2024. "Sensitivity of Chinese stock markets to individual investor sentiment: An analysis of Sina Weibo mood related to COVID-19," Journal of Behavioral and Experimental Finance, Elsevier, vol. 41(C).
    3. Halil D Kaya & Abhinav Maramraju & Anish Nallapu, 2023. "Social Media, Trading Volume, Volatility And Stock Prices," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 6, pages 40-50, December.
    4. Humaira Asad & Iqra Toqeer & Khalid Mahmood, 2021. "A qualitative phenomenological exploration of social mood and investors’ risk tolerance in an emerging economy," Qualitative Research in Financial Markets, Emerald Group Publishing Limited, vol. 14(1), pages 189-211, August.
    5. Haque, Md Ziaul & Qian, Aimin & Hoque, Md Rakibul & Lucky, Suraiea Akter, 2022. "A unified framework for exploring the determinants of online social networks (OSNs) on institutional investors’ capital market investment decision," Technology in Society, Elsevier, vol. 70(C).

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