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.
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Notes
For clarity, trading volume refers to the logarithm of the USD equivalence of all Bitcoin transactions hereinafter.
Many of the child boards on Bitcointalk.org are designed for Altcoin discussions (Altcoin generally refers to other cryptocurrencies besides Bitcoin) or not directly related to the Bitcoin market. There is also a general discussion board, but it covers a wide range of topics, sometimes not even related to the Bitcoin community.
For more details, please refer to the Stanford Topic Modeling Toolbox, accessed Apr 09, 2020, https://nlp.stanford.edu/software/tmt/tmt-0.4/.
References
Adomavicius, G., Bockstedt, J., & Gupta, A. (2012). Modeling supply-side dynamics of it components, products, and infrastructure: An empirical analysis using vector autoregression. Information Systems Research. https://doi.org/10.1287/isre.1120.0418.
Antweiler, W., & Frank, M. Z. (2004). Is all that talk just noise? The information content of internet stock message boards. Journal of Finance. https://doi.org/10.1111/j.1540-6261.2004.00662.x.
Aral, S., & van Alstyne, M. (2011). The diversity-bandwidth trade-off. American Journal of Sociology. https://doi.org/10.1086/661238.
Baek, C., & Elbeck, M. (2015). Bitcoins as an investment or speculative vehicle? A first look. Applied Economics Letters. https://doi.org/10.1080/13504851.2014.916379.
Baumeister, R. F., Bratslavsky, E., Finkenauer, C., & Vohs, K. D. (2001). Bad is stronger than good. Review of General Psychology. https://doi.org/10.1037//1089-2680.5.4.323.
Bhattacharya, D., & Ram, S. (2012). Sharing news articles using 140 characters: A diffusion analysis on twitter. In Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012. https://doi.org/10.1109/ASONAM.2012.170.
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research. https://doi.org/10.1016/b978-0-12-411519-4.00006-9.
Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science. https://doi.org/10.1016/j.jocs.2010.12.007.
Brock, W. A., & Kleidon, A. W. (1992). Periodic market closure and trading volume. A model of intraday bids and asks. Journal of Economic Dynamics and Control. https://doi.org/10.1016/0165-1889(92)90045-G.
Campbell, J. Y., Grossman, S. J., & Wang, J. (1993). Trading volume and serial correlation in stock returns. The Quarterly Journal of Economics. https://doi.org/10.2307/2118454.
Chang, W. L., & Chen, Y. P. (2019). Way too sentimental? A credible model for online reviews. Information Systems Frontiers. https://doi.org/10.1007/s10796-017-9757-z.
Cheah, E. T., & Fry, J. (2015). Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin. Economics Letters. https://doi.org/10.1016/j.econlet.2015.02.029.
Chen, H., De, P., Hu, Y., & Hwang, B. H. (2014). Wisdom of crowds: The value of stock opinions transmitted through social media. Review of Financial Studies. https://doi.org/10.1093/rfs/hhu001.
Clapham, B., Siering, M., & Gomber, P. (2019). Popular news are relevant news! How investor attention affects algorithmic decision-making and decision support in financial markets. Information Systems Frontiers. https://doi.org/10.1007/s10796-019-09950-w.
Cooper, A. A., & Humphreys, K. R. (2008). The uncertainty is killing me: Self-triage decision making and information availability. E-Journal of Applied Psychology. https://doi.org/10.7790/ejap.v4i1.124.
Das, S. R., & Chen, M. Y. (2007). Yahoo! for amazon: Sentiment extraction from small talk on the Web. Management Science. https://doi.org/10.1287/mnsc.1070.0704.
Davis, A. K., Piger, J. M., & Sedor, L. M. (2012). Beyond the numbers: measuring the information content of earnings press release language. Contemporary Accounting Research. https://doi.org/10.1111/j.1911-3846.2011.01130.x.
Dekimpe, M. G., & Hanssens, D. M. (1999). Sustained spending and persistent response: A new look at long-term marketing profitability. Journal of Marketing Research. https://doi.org/10.2307/3151996.
Dewally, M. (2003). Internet investment advice: Investing with a rock of salt. Financial Analysts Journal. https://doi.org/10.2469/faj.v59.n4.2546.
Dewan, S., & Ramaprasad, J. (2014). Social media, traditional media, and music sales. MIS Quarterly: Management Information Systems. https://doi.org/10.25300/MISQ/2014/38.1.05.
Geethapriya, A., & Valli, S. (2021). An enhanced approach to map domain-specific words in cross-domain sentiment analysis. Information Systems Frontiers, 1–15. https://doi.org/10.1007/s10796-020-10094-5.
Gray, W. R., & Kern, A. E. (2012). Talking your book: social networks and price discovery. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1767452.
Gu, B., Konana, P., Raghunathan, R., & Chen, H. M. (2014). The allure of homophily in social media: Evidence from investor responses on virtual communities. Information Systems Research. https://doi.org/10.1287/isre.2014.0531.
Ho, T. H., Lim, N., & Camerer, C. (2006). Modeling the Psychology of Consumer and Firm Behavior. Journal of Marketing Research. https://doi.org/10.1509/jmkr.43.3.307.
Horwitz, S. K., & Horwitz, I. B. (2007). The effects of team diversity on team outcomes: A meta-analytic review of team demography. Journal of Management. https://doi.org/10.1177/0149206307308587.
Kapoor, K. K., Tamilmani, K., Rana, N. P., Patil, P., Dwivedi, Y. K., & Nerur, S. (2018). Advances in social media research: past, present and future. Information Systems Frontiers. https://doi.org/10.1007/s10796-017-9810-y.
Li, X., & Whinston, A. B. (2020). Analyzing cryptocurrencies. Information Systems Frontiers. https://doi.org/10.1007/s10796-019-09966-2.
Loughran, T., & Mcdonald, B. (2011). When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. Journal of Finance. https://doi.org/10.1111/j.1540-6261.2010.01625.x.
Love, I., & Zicchino, L. (2006). Financial development and dynamic investment behavior: Evidence from panel VAR. Quarterly Review of Economics and Finance. https://doi.org/10.1016/j.qref.2005.11.007.
Luo, X. (2009). Quantifying the long-term impact of negative word of mouth on cash flows and stock prices. Marketing Science. https://doi.org/10.1287/mksc.1080.0389.
Luo, X., & Homburg, C. (2007). Neglected outcomes of customer satisfaction. Journal of Marketing. https://doi.org/10.1509/jmkg.71.2.133.
Luo, X., Zhang, J., & Duan, W. (2013). Social media and firm equity value. Information Systems Research. https://doi.org/10.1287/isre.1120.0462.
Lütkepohl, H. (2005). New introduction to multiple time series analysis. https://doi.org/10.1007/978-3-540-27752-1.
Mai, F., Shan, Z., Bai, Q., Wang, X., Shane, & Chiang, R. H. L. (2018). How does social media impact Bitcoin value? A test of the silent majority hypothesis. Journal of Management Information Systems. https://doi.org/10.1080/07421222.2018.1440774.
Mendon, S., Dutta, P., Behl, A., & Lessmann, S. (2021). A Hybrid approach of machine learning and lexicons to sentiment analysis: enhanced insights from twitter data of natural disasters. Information Systems Frontiers, 1–24. https://doi.org/10.1007/s10796-021-10107-x.
Nann, S., Krauss, J., & Schoder, D. (2013). Predictive analytics on public data-The case of stock markets. In ECIS 2013 - Proceedings of the 21st European Conference on Information Systems.
Oh, C., & Sheng, O. R. L. (2011). Investigating predictive power of stock micro blog sentiment in forecasting future stock price directional movement. In International Conference on Information Systems 2011, ICIS 2011.
Paletz, S. B. F., & Schunn, C. D. (2010). A social-cognitive framework of multidisciplinary team innovation. Topics in Cognitive Science. https://doi.org/10.1111/j.1756-8765.2009.01029.x.
Said, S. E., & Dickey, D. A. (1984). Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika. https://doi.org/10.1093/biomet/71.3.599.
Solomon, D. H. (2012). Selective publicity and stock prices. Journal of Finance. https://doi.org/10.1111/j.1540-6261.2012.01726.x.
Stock, J. H., & Watson, M. W. (2001). Vector autoregressions. Journal of Economic Perspectives. https://doi.org/10.1257/jep.15.4.101.
Suominen, M. (2001). Trading volume and information revelation in stock markets. The Journal of Financial and Quantitative Analysis. https://doi.org/10.2307/2676224.
Swann, W. B. (1987). Identity negotiation: where two roads meet. Journal of Personality and Social Psychology. https://doi.org/10.1037/0022-3514.53.6.1038.
Tetlock, P. C. (2007). Giving content to investor sentiment: The role of media in the stock market. Journal of Finance. https://doi.org/10.1111/j.1540-6261.2007.01232.x.
Tetlock, P. C., Saar-Tsechansky, M., & Macskassy, S. (2008). More than words: quantifying language to measure firms’ fundamentals. Journal of Finance. https://doi.org/10.1111/j.1540-6261.2008.01362.x.
Wang, J. (1994). A model of competitive stock trading volume. Journal of Political Economy. https://doi.org/10.1086/261924.
Wasko, M. M. L., & Faraj, S. (2005). Why should I share? Examining social capital and knowledge contribution in electronic networks of practice. MIS Quarterly: Management Information Systems. https://doi.org/10.2307/25148667.
Xu, Q., Chang, V., & Hsu, C. H. (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. https://doi.org/10.1007/s10796-020-10024-5.
Xue, G., Xu, J., Wu, H., Lu, W., & Xu, L. (2020). Incentive mechanism for rational miners in Bitcoin Mining Pool. Information Systems Frontiers. https://doi.org/10.1007/s10796-020-10019-2.
Zhang, J., & Piramuthu, S. (2018). Product recommendation with latent review topics. Information Systems Frontiers. https://doi.org/10.1007/s10796-016-9697-z.
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Xie, P. The Interplay Between Investor Activity on Virtual Investment Community and the Trading Dynamics: Evidence From the Bitcoin Market. Inf Syst Front 24, 1287–1303 (2022). https://doi.org/10.1007/s10796-021-10130-y
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DOI: https://doi.org/10.1007/s10796-021-10130-y