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Twitter financial community sentiment and its predictive relationship to stock market movement

Author

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  • Steve Y. Yang
  • Sheung Yin Kevin Mo
  • Anqi Liu

Abstract

Twitter, one of the several major social media platforms, has been identified as an influential factor for financial markets by multiple academic and professional publications in recent years. The motivation of this study hinges on the growing popularity of the use of Twitter and the increasing prevalence of its influence among the financial investment community. This paper presents empirical evidence of the existence of a financial community on Twitter in which users' interests align with financial market-related topics. We establish a methodology to identify relevant Twitter users who form the financial community, and we also present the empirical findings of network characteristics of the financial community. We observe that this financial community behaves similarly to a small-world network, and we further identify groups of critical nodes and analyse their influence within the financial community based on several network centrality measures. Using a novel sentiment analysis algorithm, we construct a weighted sentiment measure using tweet messages from these critical nodes, and we discover that it is significantly correlated with the returns of the major financial market indices. By forming a financial community within the Twitter universe, we argue that the influential Twitter users within the financial community provide a proxy for the relationship between social sentiment and financial market movement. Hence, we conclude that the weighted sentiment constructed from these critical nodes within the financial community provides a more robust predictor of financial markets than the general social sentiment.

Suggested Citation

  • Steve Y. Yang & Sheung Yin Kevin Mo & Anqi Liu, 2015. "Twitter financial community sentiment and its predictive relationship to stock market movement," Quantitative Finance, Taylor & Francis Journals, vol. 15(10), pages 1637-1656, October.
    • Handle: RePEc:taf:quantf:v:15:y:2015:i:10:p:1637-1656
    DOI: 10.1080/14697688.2015.1071078
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    Cited by:

    1. Polyzos, Efstathios & Wang, Fang, 2022. "Twitter and market efficiency in energy markets: Evidence using LDA clustered topic extraction," Energy Economics, Elsevier, vol. 114(C).
    2. Ozdamar, Melisa & Sensoy, Ahmet & Akdeniz, Levent, 2022. "Retail vs institutional investor attention in the cryptocurrency market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 81(C).
    3. Saurabh, Samant & Dey, Kushankur, 2020. "Unraveling the relationship between social moods and the stock market: Evidence from the United Kingdom," Journal of Behavioral and Experimental Finance, Elsevier, vol. 26(C).
    4. Wang, Xinjie & Xiang, Zhiqiang & Xu, Weike & Yuan, Peixuan, 2022. "The causal relationship between social media sentiment and stock return: Experimental evidence from an online message forum," Economics Letters, Elsevier, vol. 216(C).
    5. Baoqing Gan, 2020. "Does Social Media Sentiment Trump News?," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 5-2020.
    6. Zeitun, Rami & Rehman, Mobeen Ur & Ahmad, Nasir & Vo, Xuan Vinh, 2023. "The impact of Twitter-based sentiment on US sectoral returns," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
    7. Uddin, Ajim & Yu, Dantong, 2020. "Latent factor model for asset pricing," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
    8. Zachary McGurk & Adam Nowak & Joshua C. Hall, 2020. "Stock returns and investor sentiment: textual analysis and social media," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 44(3), pages 458-485, July.
    9. Wentao Xu & Weiqing Liu & Lewen Wang & Yingce Xia & Jiang Bian & Jian Yin & Tie-Yan Liu, 2021. "HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information," Papers 2110.13716, arXiv.org, revised Jan 2022.
    10. Umar, Zaghum & Jareño, Francisco & González, María de la O, 2021. "The impact of COVID-19-related media coverage on the return and volatility connectedness of cryptocurrencies and fiat currencies," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    11. Behrendt, Simon & Schmidt, Alexander, 2018. "The Twitter myth revisited: Intraday investor sentiment, Twitter activity and individual-level stock return volatility," Journal of Banking & Finance, Elsevier, vol. 96(C), pages 355-367.
    12. Hadi NekoeiQachkanloo & Benyamin Ghojogh & Ali Saheb Pasand & Mark Crowley, 2019. "Artificial Counselor System for Stock Investment," Papers 1903.00955, arXiv.org.
    13. Ganggang Guo & Yulei Rao & Feida Zhu & Fang Xu, 2020. "Innovative deep matching algorithm for stock portfolio selection using deep stock profiles," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-31, November.
    14. Jia‐Yen Huang & Jin‐Hao Liu, 2020. "Using social media mining technology to improve stock price forecast accuracy," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 104-116, January.
    15. Rick Steinert & Saskia Altmann, 2023. "Linking microblogging sentiments to stock price movement: An application of GPT-4," Papers 2308.16771, arXiv.org.
    16. Kazım Berk Küçüklerli & Veysel Ulusoy, 2024. "Sentiment-Driven Exchange Rate Forecasting: Integrating Twitter Analysis with Economic Indicators," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 14(3), pages 1-4.
    17. Afanasyev, Dmitriy O. & Fedorova, Elena & Ledyaeva, Svetlana, 2021. "Strength of words: Donald Trump's tweets, sanctions and Russia's ruble," Journal of Economic Behavior & Organization, Elsevier, vol. 184(C), pages 253-277.
    18. Wentao Xu & Weiqing Liu & Chang Xu & Jiang Bian & Jian Yin & Tie-Yan Liu, 2021. "REST: Relational Event-driven Stock Trend Forecasting," Papers 2102.07372, arXiv.org, revised Feb 2021.
    19. Alomari, Mohammad & Al Rababa’a, Abdel Razzaq & El-Nader, Ghaith & Alkhataybeh, Ahmad & Ur Rehman, Mobeen, 2021. "Examining the effects of news and media sentiments on volatility and correlation: Evidence from the UK," The Quarterly Review of Economics and Finance, Elsevier, vol. 82(C), pages 280-297.
    20. Alzahrani, Ahmed Ibrahim & Sarsam, Samer Muthana & Al-Samarraie, Hosam & Alblehai, Fahad, 2023. "Exploring the sentimental features of rumor messages and investors' intentions to invest," International Review of Economics & Finance, Elsevier, vol. 87(C), pages 433-444.
    21. Daniele Ballinari & Simon Behrendt, 2021. "How to gauge investor behavior? A comparison of online investor sentiment measures," Digital Finance, Springer, vol. 3(2), pages 169-204, June.
    22. 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).
    23. Machus, Tobias & Mestel, Roland & Theissen, Erik, 2022. "Heroes, just for one day: The impact of Donald Trump’s tweets on stock prices," Journal of Behavioral and Experimental Finance, Elsevier, vol. 33(C).

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