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The Interplay Between Investor Activity on Virtual Investment Community and the Trading Dynamics: Evidence From the Bitcoin Market

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  • Peng Xie

    (California State University, East Bay)

Abstract

Virtual investment community has become an important information source for investors. This study contributes to the related literature by investigating the endogenous interplay between investor activity on the virtual investment community and the market trading dynamics using a vector autoregressive framework to analyze an hourly dataset collected from the Bitcoin market. The main results suggest that the sentiment and the posting frequency of virtual investment community messages are largely driven by the past market outcomes, but they provide limited value-relevant information for future price prediction. It is also demonstrated that when investors express conflicting opinions, or when their discussions exhibit a lack of diversity, their incentive to trade decreases, resulting in low trading volume. Theoretical contributions and practical implications are discussed.

Suggested Citation

  • Peng Xie, 2022. "The Interplay Between Investor Activity on Virtual Investment Community and the Trading Dynamics: Evidence From the Bitcoin Market," Information Systems Frontiers, Springer, vol. 24(4), pages 1287-1303, August.
    • Handle: RePEc:spr:infosf:v:24:y:2022:i:4:d:10.1007_s10796-021-10130-y
    DOI: 10.1007/s10796-021-10130-y
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    1. John Y. Campbell & Sanford J. Grossman & Jiang Wang, 1993. "Trading Volume and Serial Correlation in Stock Returns," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 108(4), pages 905-939.
    2. A. Geethapriya & S. Valli, 2021. "An Enhanced Approach to Map Domain-Specific Words in Cross-Domain Sentiment Analysis," Information Systems Frontiers, Springer, vol. 23(3), pages 791-805, June.
    3. Sanjiv R. Das & Mike Y. Chen, 2007. "Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web," Management Science, INFORMS, vol. 53(9), pages 1375-1388, September.
    4. Bin Gu & Prabhudev Konana & Rajagopal Raghunathan & Hsuanwei Michelle Chen, 2014. "Research Note —The Allure of Homophily in Social Media: Evidence from Investor Responses on Virtual Communities," Information Systems Research, INFORMS, vol. 25(3), pages 604-617, September.
    5. Wei-Lun Chang & Yi-Pei Chen, 2019. "Way too sentimental? a credible model for online reviews," Information Systems Frontiers, Springer, vol. 21(2), pages 453-468, April.
    6. Brock, William A. & Kleidon, Allan W., 1992. "Periodic market closure and trading volume : A model of intraday bids and asks," Journal of Economic Dynamics and Control, Elsevier, vol. 16(3-4), pages 451-489.
    7. Gediminas Adomavicius & Jesse Bockstedt & Alok Gupta, 2012. "Modeling Supply-Side Dynamics of IT Components, Products, and Infrastructure: An Empirical Analysis Using Vector Autoregression," Information Systems Research, INFORMS, vol. 23(2), pages 397-417, June.
    8. Kawaljeet Kaur Kapoor & Kuttimani Tamilmani & Nripendra P. Rana & Pushp Patil & Yogesh K. Dwivedi & Sridhar Nerur, 2018. "Advances in Social Media Research: Past, Present and Future," Information Systems Frontiers, Springer, vol. 20(3), pages 531-558, June.
    9. Angela K. Davis & Jeremy M. Piger & Lisa M. Sedor, 2012. "Beyond the Numbers: Measuring the Information Content of Earnings Press Release Language," Contemporary Accounting Research, John Wiley & Sons, vol. 29(3), pages 845-868, September.
    10. Juheng Zhang & Selwyn Piramuthu, 2018. "Product recommendation with latent review topics," Information Systems Frontiers, Springer, vol. 20(3), pages 617-625, June.
    11. Cheah, Eng-Tuck & Fry, John, 2015. "Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin," Economics Letters, Elsevier, vol. 130(C), pages 32-36.
    12. Suominen, Matti, 2001. "Trading Volume and Information Revelation in Stock Market," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 36(4), pages 545-565, December.
    13. Paul C. Tetlock & Maytal Saar‐Tsechansky & Sofus Macskassy, 2008. "More Than Words: Quantifying Language to Measure Firms' Fundamentals," Journal of Finance, American Finance Association, vol. 63(3), pages 1437-1467, June.
    14. Qianwen Xu & Victor Chang & Ching-Hsien Hsu, 2020. "Event Study and Principal Component Analysis Based on Sentiment Analysis – A Combined Methodology to Study the Stock Market with an Empirical Study," Information Systems Frontiers, Springer, vol. 22(5), pages 1021-1037, October.
    15. Xueming Luo & Jie Zhang & Wenjing Duan, 2013. "Social Media and Firm Equity Value," Information Systems Research, INFORMS, vol. 24(1), pages 146-163, March.
    16. Paul C. Tetlock, 2007. "Giving Content to Investor Sentiment: The Role of Media in the Stock Market," Journal of Finance, American Finance Association, vol. 62(3), pages 1139-1168, June.
    17. Werner Antweiler & Murray Z. Frank, 2004. "Is All That Talk Just Noise? The Information Content of Internet Stock Message Boards," Journal of Finance, American Finance Association, vol. 59(3), pages 1259-1294, June.
    18. C. Baek & M. Elbeck, 2015. "Bitcoins as an investment or speculative vehicle? A first look," Applied Economics Letters, Taylor & Francis Journals, vol. 22(1), pages 30-34, January.
    19. James H. Stock & Mark W. Watson, 2001. "Vector Autoregressions," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 101-115, Fall.
    20. Wang, Jiang, 1994. "A Model of Competitive Stock Trading Volume," Journal of Political Economy, University of Chicago Press, vol. 102(1), pages 127-168, February.
    21. Xueming Luo, 2009. "Quantifying the Long-Term Impact of Negative Word of Mouth on Cash Flows and Stock Prices," Marketing Science, INFORMS, vol. 28(1), pages 148-165, 01-02.
    22. Tim Loughran & Bill Mcdonald, 2011. "When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks," Journal of Finance, American Finance Association, vol. 66(1), pages 35-65, February.
    23. Hailiang Chen & Prabuddha De & Yu (Jeffrey) Hu & Byoung-Hyoun Hwang, 2014. "Wisdom of Crowds: The Value of Stock Opinions Transmitted Through Social Media," The Review of Financial Studies, Society for Financial Studies, vol. 27(5), pages 1367-1403.
    24. David H. Solomon, 2012. "Selective Publicity and Stock Prices," Journal of Finance, American Finance Association, vol. 67(2), pages 599-638, April.
    25. Shalak Mendon & Pankaj Dutta & Abhishek Behl & Stefan Lessmann, 2021. "A Hybrid Approach of Machine Learning and Lexicons to Sentiment Analysis: Enhanced Insights from Twitter Data of Natural Disasters," Information Systems Frontiers, Springer, vol. 23(5), pages 1145-1168, September.
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