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Social Media Sentiment in International Stock Returns and Trading Activity

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  • Selin Duz Tan
  • Oktay Tas

Abstract

The authors investigate the impact of social media on S&P index constituents for U.S., European, and emerging markets with the international investor perspective using firm-specific Twitter sentiment and activity. The findings indicate that Twitter activity and sentiment are associated with trading volume and returns, and predicts subsequent-day trading volume. The authors find that firm-specific Twitter sentiment contains information for predicting stock returns and this predictive power remains significant after controlling news sentiment. The positive tone of Twitter sentiment is more pronounced in small and emerging market firms, which is consistent with the literature stating that small firms are hard to value and emerging market firms contain high information asymmetry. From a practical perspective, investors could potentially use social media sentiment in trading strategies.

Suggested Citation

  • Selin Duz Tan & Oktay Tas, 2021. "Social Media Sentiment in International Stock Returns and Trading Activity," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 22(2), pages 221-234, April.
    • Handle: RePEc:taf:hbhfxx:v:22:y:2021:i:2:p:221-234
    DOI: 10.1080/15427560.2020.1772261
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    Citations

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    Cited by:

    1. 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).
    2. Blasco-Arcas, Lorena & Lee, Hsin-Hsuan Meg & Kastanakis, Minas N. & Alcañiz, Mariano & Reyes-Menendez, Ana, 2022. "The role of consumer data in marketing: A research agenda," Journal of Business Research, Elsevier, vol. 146(C), pages 436-452.
    3. 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).
    4. Xiaohong Shen & Gaoshan Wang & Yue Wang & Alfred Peris, 2021. "The Influence of Research Reports on Stock Returns: The Mediating Effect of Machine-Learning-Based Investor Sentiment," Discrete Dynamics in Nature and Society, Hindawi, vol. 2021, pages 1-14, December.
    5. Ben Chamberlain & Zhangxin (Frank) Liu & Lee A. Smales, 2023. "Short interest and the stock market relation with news sentiment from traditional and social media sources," Australian Economic Papers, Wiley Blackwell, vol. 62(2), pages 321-334, June.
    6. Tolga Buz & Gerard de Melo, 2021. "Should You Take Investment Advice From WallStreetBets? A Data-Driven Approach," Papers 2105.02728, arXiv.org.
    7. 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).
    8. Alex Frino & Caihong Xu & Z. Ivy Zhou, 2022. "Are option traders more informed than Twitter users? A PVAR analysis," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(9), pages 1755-1771, September.
    9. Shahid Raza & Sun Baiqing & Imtiaz Hussain & Pwint Kay-Khine, 2023. "Do good and bad news affect the day of the week effect? An analysis of the KSE-100 Index," SN Business & Economics, Springer, vol. 3(7), pages 1-22, July.
    10. Tolga Buz & Gerard de Melo, 2022. "Democratization of Retail Trading: Can Reddit's WallStreetBets Outperform Investment Bank Analysts?," Papers 2301.00170, arXiv.org.

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