Social media disagreement and financial markets: A comparison of stocks and Bitcoin
DOI:
https://doi.org/10.18559/ebr.2024.4.1683Keywords:
disagreement, trading volume, volatility, Bitcoin, RedditAbstract
We examine whether disagreement in social media discussions related to financial markets affects subsequent volatility and abnormal trading volume. We also compare how traditional and digital asset markets differ by comparing stocks and Bitcoin. We show that social media disagreement is positively associated with future market volatility and abnormal trading volume in the stock market. The effect of disagreement is more pronounced at the individual stock level than at the index level. A higher level of social media disagreement also increases the probability of extremely negative stock market returns. In contrast, disagreement in Bitcoin-related social media weakly affects subsequent volatility but does not affect trading volume or extremely negative returns. Our findings also reveal that market activity impacts the disagreement in the stock market and Bitcoin communities differently.
JEL Classification
Search • Learning • Information and Knowledge • Communication • Belief • Unawareness (D83)
General (G10)
Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets (G41)
Downloads
References
Ahn, Y., & Kim, D. (2020). Sentiment disagreement and bitcoin price fluctuations: A psycholinguistic approach. Applied Economics Letters, 27(5), 412–416. https://doi.org/10.1080/13504851.2019.1619013
View in Google Scholar
DOI: https://doi.org/10.1080/13504851.2019.1619013
Ajinkya, B. B., Atiase, R. K., & Gift, M. J. (1991). Volume of trading and the dispersion in financial analysts’ earnings forecasts. The Accounting Review, 66(2), 389–401.
View in Google Scholar
Al-Nasseri, A., & Menla Ali, F. (2018). What does investors’ online divergence of opinion tell us about stock returns and trading volume? Journal of Business Research, 86, 166–178. https://doi.org/10.1016/j.jbusres.2018.01.006
View in Google Scholar
DOI: https://doi.org/10.1016/j.jbusres.2018.01.006
Andrei, D., & Hasler, M. (2015). Investor attention and stock market volatility. Review of Financial Studies, 28(1), 33–72. https://doi.org/10.1093/rfs/hhu059
View in Google Scholar
DOI: https://doi.org/10.1093/rfs/hhu059
Antweiler, W., & Frank, M. Z. (2004). Is all that talk just noise? The information content of Internet Stock Message Boards. The Journal of Finance, 59(3), 1259–1294. https://doi.org/10.1111/j.1540-6261.2004.00662.x
View in Google Scholar
DOI: https://doi.org/10.1111/j.1540-6261.2004.00662.x
Aouadi, A., Arouri, M., & Teulon, F. (2013). Investor attention and stock market activity: Evidence from France. Economic Modelling, 35, 674–681. https://doi.org/10.1016/j.econmod.2013.08.034
View in Google Scholar
DOI: https://doi.org/10.1016/j.econmod.2013.08.034
Atiase, R. K., & Bamber, L. S. (1994). Trading volume reactions to annual account- ing earnings announcements: The incremental role of predisclosure information asymmetry. Journal of Accounting and Economics, 17(3), 309–329. https://doi.org/10.1016/01654101(94)90031-0
View in Google Scholar
DOI: https://doi.org/10.1016/0165-4101(94)90031-0
Atmaz, A., & Basak, S. (2018). Belief dispersion in the stock market. The Journal of Finance, 73(3), 1225–1279. https://doi.org/10.1111/jofi.12618
View in Google Scholar
DOI: https://doi.org/10.1111/jofi.12618
Bamber, L. S., Barron, O. E., & Stober, T. L. (1997). Trading volume and different aspects of disagreement coincident with earnings announcements. The Accounting Review, 72(4), 575–597.
View in Google Scholar
Banerjee, S. (2011). Learning from prices and the dispersion in beliefs. The Review of Financial Studies, 24(9), 3025–3068. https://doi.org/10.1093/rfs/hhr050
View in Google Scholar
DOI: https://doi.org/10.1093/rfs/hhr050
Banerjee, S., & Kremer, I. (2010). Disagreement and learning: Dynamic patterns of trade. The Journal of Finance, 65(4), 1269–1302. https://doi.org/10.1111/j.1540-6261.2010.01570.x
View in Google Scholar
DOI: https://doi.org/10.1111/j.1540-6261.2010.01570.x
Barron, O. E. (1995). Trading volume and belief revisions that differ among individual analysts. The Accounting Review, 70(4), 581–597.
View in Google Scholar
Blau, B. M., & Whitby, R. J. (2017). Range-based volatility, expected stock returns, and the low volatility anomaly. Plos One, 12(11), 0188517. https://doi.org/10.1371/journal.pone.0188517
View in Google Scholar
DOI: https://doi.org/10.1371/journal.pone.0188517
Carlin, B. I., Longstaff, F. A., & Matoba, K. (2014). Disagreement and asset prices. Journal of Financial Economics, 114(2), 226–238. https://doi.org/10.1016/j.jfine-co.2014.06.007
View in Google Scholar
DOI: https://doi.org/10.1016/j.jfineco.2014.06.007
Chen, H., De, P., Hu, Y., & Hwang, B. H. (2014). Wisdom of crowds: The value of stock opinions transmitted through social media. The Review of Financial Studies, 27(5), 1367–1403. https://doi.org/10.1093/rfs/hhu001
View in Google Scholar
DOI: https://doi.org/10.1093/rfs/hhu001
Cookson, J. A., & Niessner, M. (2020). Why don’t we agree? Evidence from a social network of investors. The Journal of Finance, 75(1), 173–228. https://doi.org/10.1111/jofi.12852
View in Google Scholar
DOI: https://doi.org/10.1111/jofi.12852
Ding, R., & Hou, W. (2015). Retail investor attention and stock liquidity. Journal of International Financial Markets, Institutions and Money, 37, 12–26. https://doi.org/10.1016/j.intfin.2015.04.001
View in Google Scholar
DOI: https://doi.org/10.1016/j.intfin.2015.04.001
Giannini, R., Irvine, P., & Shu, T. (2019). The convergence and divergence of investors’ opinions around earnings news: Evidence from a social network. Journal of Financial Markets, 42, 94–120. https://doi.org/10.1016/j.finmar.2018.12.003
View in Google Scholar
DOI: https://doi.org/10.1016/j.finmar.2018.12.003
Graham, J. R., & Harvey, C. R. (1996). Market timing ability and volatility implied in investment newsletters’ asset allocation recommendations. Journal of Financial Economics, 42(3), 397–421. https://doi.org/10.1016/0304-405X(96)00878-1
View in Google Scholar
DOI: https://doi.org/10.1016/0304-405X(96)00878-1
Hong, H., & Stein, J. C. (2007). Disagreement and the stock market. Journal of Economic Perspectives, 21(2), 109–128. https://doi.org/10.1257/jep.21.2.109
View in Google Scholar
DOI: https://doi.org/10.1257/jep.21.2.109
Hutto, C., & Gilbert, E. (2014). VADER: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media, 8(1), 216–225. https://doi.org/10.1609/icwsm.v8i1.14550
View in Google Scholar
DOI: https://doi.org/10.1609/icwsm.v8i1.14550
Kantorovitch, I., & Heineken, J. (2021, September 10). Does dispersed sentiment drive returns, turnover, and volatility for Bitcoin? https://doi.org/10.2139/ssrn.3920987
View in Google Scholar
DOI: https://doi.org/10.2139/ssrn.3920987
Kim, D. Y., & Ahn, Y. (2023). Emotional reactions, sentiment disagreement, and Bitcoin trading. Asia-Pacific Journal of Business, 14(4), 37–48. https://doi.org/10.32599/apjb.14.4.202312.37
View in Google Scholar
DOI: https://doi.org/10.32599/apjb.14.4.202312.37
Knittel, M., Pitts, S., & Wash, R. (2019). “The most trustworthy coin”: How ideological tensions drive trust in Bitcoin. Proceedings of the ACM on Human–Computer Interaction, 3(CSCW), 1–23. https://doi.org/10.1145/3359138
View in Google Scholar
DOI: https://doi.org/10.1145/3359138
Li, D., & Li, G. (2021). Whose disagreement matters? Household belief dispersion and stock trading volume. Review of Finance, 25(6), 1859–1900. https://doi.org/10.1093/rof/rfab005
View in Google Scholar
DOI: https://doi.org/10.1093/rof/rfab005
Li, T., van Dalen, J., & van Rees, P. J. (2018). More than just noise? Examining the information content of stock microblogs on financial markets. Journal of Information Technology, 33(1), 50–69. https://doi.org/10.1057/s41265-016-0034-2
View in Google Scholar
DOI: https://doi.org/10.1057/s41265-016-0034-2
Loria, S. (2020, April 26). Textblob documentation. release 0.16.0. https://buildmedia.readthedocs.org/media/pdf/textblob/latest/textblob.pdf
View in Google Scholar
Siganos, A., Vagenas-Nanos, E., & Verwijmeren, P. (2017). Divergence of sentiment and stock market trading. Journal of Banking & Finance, 78, 130–141. https://doi.org/10.1016/j.jbankfin.2017.02.005
View in Google Scholar
DOI: https://doi.org/10.1016/j.jbankfin.2017.02.005
Sprenger, T. O., Tumasjan, A., Sandner, P. G., & Welpe, I. M. (2014). Tweets and trades: The information content of stock microblogs. European Financial Management, 20(5), 926–957. https://doi.org/10.1111/j.1468-036X.2013.12007.x
View in Google Scholar
DOI: https://doi.org/10.1111/j.1468-036X.2013.12007.x
Tan, S. D., & Tas, O. (2021). Social media sentiment in international stock returns and trading activity. Journal of Behavioral Finance, 22(2), 221–234. https://doi.org/10.1080/15427560.2020.1772261
View in Google Scholar
DOI: https://doi.org/10.1080/15427560.2020.1772261
Vlahavas, G., & Vakali, A. (2024). Dynamics between Bitcoin market trends and social media activity. FinTech, 3(3), 349–378. https://doi.org/10.3390/fintech3030020
View in Google Scholar
DOI: https://doi.org/10.3390/fintech3030020
Wang, J., Wu, K., Pan, J., & Jiang, Y. (2023). Disagreement, speculation, and the idiosyncratic volatility. Journal of Empirical Finance, 72, 232–250. https://doi.org/10.1016/j.jempfin.2023.03.011
View in Google Scholar
DOI: https://doi.org/10.1016/j.jempfin.2023.03.011
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Sergen Akarsu, Neslihan Yilmaz

This work is licensed under a Creative Commons Attribution 4.0 International License.
