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
- 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.
- 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
- 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
- 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
- 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
- Atiase, R. K., & Bamber, L. S. (1994). Trading volume reactions to annual accounting earnings announcements: The incremental role of predisclosure information asymmetry. Journal of Accounting and Economics, 17(3), 309–329. 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
- 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.
- 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
- 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
- Barron, O. E. (1995). Trading volume and belief revisions that differ among individual analysts. The Accounting Review, 70(4), 581–597.
- 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
- 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.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
- 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
- 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
- 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
- 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
- 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
- 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, .(1), 216–225. 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
- 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
- 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, .(CSCW), 1–23. 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
- 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
- Loria, S. (2020, April 26). Textblob documentation. release 0.16.0. https://buildmedia.readthedocs.org/media/pdf/textblob/latest/textblob.pdf
- 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
- 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
- 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
- Vlahavas, G., & Vakali, A. (2024). Dynamics between Bitcoin market trends and social media activity. FinTech, .(3), 349–378. 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