ANALYSIS OF THE RELATIONSHIP BETWEEN MOOD EXPRESSED ON TWITTER, RETURN, VOLATILITY AND TRADING VOLUME IN THE BRAZILIAN STOCK MARKET
DOI:
https://doi.org/10.4270/ruc.2023114Keywords:
Mood, Stock market, TwitterAbstract
The aim of the study is to investigate the relationship between changes in mood expressed on Twitter, stock returns, volatility and trading volume in the Brazilian stock market. The sample consisted of daily data on the mood expressed on Twitter and the Ibovespa. These data were analyzed using quantile regression, in which the impact that the variation in mood expressed on the platform has on the stock market was investigated, due to the rapid and wide reach, the network effect and the emotional contagion that the media generates. . The theme is original, with a growing interest in research involving social media, expressed sentiment and its relationship with decision-making in the stock market. The study showed that the variation in mood has a negative relationship with the trading volume and a positive relationship with the volatility of the Ibovespa, that is, investors tend to be less willing to trade when the mood is oscillating and that its variation contributes to the increase stock volatility. There is an inverse change in the movement of stock returns as the mood of Twitter changes. The relationship is negative when mood variation is low and positive when it is high. These results contribute to those involved in the stock market by showing that humor is an element that affects asset prices, such as investors, financial analysts and, in particular, regulators who have shown interest in monitoring the dissemination of financial information on social media, such as the performance of digital influencers. The study also brought theoretical contributions to the literature and academia by discussing, in an innovative way, a subject in increasing development.
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References
Alter, A. Irresistível. Objetiva. ISBN: 978-8547000585.
Alter, A. L., Oppenheimer, D. M., Eppley, N., & Eyre, R. N. (2007). Overcoming Institution: Metacognitive Difficulty Activates Analytic Reasoning. Journal of Experimental Psychology General, 136 (4), 569-576. https://doi.org/10.1037/0096-3445.136.4.569
Baddeley, M. (2018). Behavioural Economics and Finance (English Edition). 2nd, Routledge. ISBN: 978-041-579-218-9 (Ebook).
Baker, M., Wurgler, J. (2007). Investor sentiment in the stock market. J. Econ. Perspect. 21 (2), 129–151.
Bessa, H. A. (2016). A hierarquia de preferência do consumidor em decisões de investimento financeiro. Tese de Doutorado, Universidade de São Paulo, São Paulo, SP, Brasil.
Bollen, J., Mao, H., Zeng, X. (2011). Twitter mood predicts the stock market. J. Comput. Sci. 2 (1), 1–8.
Bolte, A., Goschke, T., Kuhl, J. (2003). Emotion and Intuition: Effects of positive and Negative Mood on Implicit Judgments of Semantic Choerence. Psychological Science, 14 (5), 416-421. https://doi.org/10.1111/1467-9280.01456
Bond, R. M., Farris, C. J., Jones, J. J., Kramer, A. D. I., Marlow, C., Settle, J. E., & Fowler, J. H. (2012). A 61-million-person experiment in social influence and political mobilization. Nature, 489 (7415), 295–298. https://doi.org/10.1038/nature11421
Brown, G., & Cliff, M. (2004). Investor sentiment and the near-term stock market. Journal of Empirical Finance, 11 (1), 1–27. https://doi.org/10.1016/j.jempfin.2002.12.001
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, 27(5), 1367–1403. https://doi.org/10.1093/rfs/hhu001
Christoffersen, P. F. (2001) Elements of financial risk management. 1a. ed. Amsterdam: Academic Press.
Chua, A.Y., & Banerjee, S. (2015). Marketing via social networking sites: a study of brand-post popularity for brands in Singapore. Proceedings of the International Multi-conference of Engineers and Computer Scientists, 1 (18-20), 1-6.
Da, Z., Engelberg, J., Gao, P. (2015). The sum of all FEARS investor sentiment and asset prices. Rev. Financ. Stud. 28 (1), 1–32. https://doi.org/10.1093/rfs/hhu072
Dalgalarrondo, P. (2000). A afetividade e suas alterações. Psicopatologia e semiologia dos transtornos mentais. Porto Alegre: Editora Artes Médicas Sul.
Deng, S., Huang, Z., Sinha, A. P., & Zhao, H. (2018). The interaction between microblog sentiment and stock returns: An empirical examination. MIS Quarterly: Management Information Systems, 42(3), 895–918.
Fang, L., & Peress, J. (2009). Media Coverage and the Cross-section of Stock Returns. The Journal of Finance, LXIV (5), 2023-20-52. https://doi.org/10.1111/j.1540-6261.2009.01493.x
Fávero, L. P, & Belfiore, P. (2020). Manual de Análise de Dados. 1. Ed. Rio de Janeiro: Gen/LTC.
Fischer, A. H., & Manstead, A. S. (2008). Social Functions of Emotion. In: Handbook of Emotions, 3, 456–468.
Fowler, J. H, & Christakis, N. A. (2008). Dynamic spread of happiness in a large social network: Longitudinal analysis over 20 years in the Framingham Heart Study. BMJ, 337, 23-37. https://doi.org/10.1136/bmj.a2338
Jia, W., Redigolo, G., Shu, S. & Zhao, J. (2020) Can Social Media Distort Price Discovery? Evidence from merger rumors. Journal of Accounting and Economics. https://doi.org/10.1016/j.jacceco.2020.101334
Jordan. J. & Kaas, K. P. (2002). Advertising in the mutual fund business: The role of judgmental heuristics in private investors’ evaluation of risk and return. Journal of Financial Services Marketing, 7 (2), 129-140. https://doi.org/10.1057/palgrave.fsm.4770079
JP Morgan (1994), “RiskMetrics Technical Documents”, 1st edition, New York.
Jung, M. J., Naughton, J. P., Tahoun, A., & Wang, C. (2018). Do firms strategically disseminate? Evidence from corporate use of social media. The Accounting Review. https://doi.org/10.2308/accr-51906
Kahneman, D., & Tversky, A. (1979). Prospect Theory: an Analysis of Decision under Risk. Econometrica, 47, 263-291. https://doi.org/10.2307/1914185
Kang, D., & Park, Y. (2013). Review-based measurement of customer satisfaction in mobile service: sentiment analysis and VIKOR approach. Expert Systems with Applications, 41, 1041-1050. https://doi.org/10.1016/j.eswa.2013.07.101
Kim, S.-H., Kim, D. (2014). Investor sentiment from internet message postings and the predictability of stock returns. J. Econ. Behav. Org. 107, 708–729. https://doi.org/10.1016/j.jebo.2014.04.015
Kraaijeveld, O., & Smedt, J. D. (2020). The predictive power of public Twitter sentiment for forecasting cryptocurrency prices. Journal of International Financial Markets, Institutions and Money, 65, 101188. https://doi.org/10.1016/j.intfin.2020.101188
Kramer, A. D. I., Guillory, J. E., & Hancock, J. T. (2014). Experimental evidence of massive-scale emotional contagion through social networks. PNAS, 111 (24). https://doi.org/10.1073/pnas.1320040111
Kujur, F., & Singh, S. (2018). Emotions as predictor for consumer engagement in YouTube advertisement. Journal of Advances in Management Research, 15 (2), 184-197. https://doi.org/10.1108/JAMR-05-2017-0065
Lee, C. C., Chen, M. P., & Peng, Y. T. (2020). Tourism development and happiness: International evidence. Tourism Economics. https://doi.org/10.1177/1354816620921574
Lee, W., Jiang, C., & Indro, D. (2002). Stock market volatility, excess returns, and the role of investor sentiment. Journal of Banking & Finance, 26 (12), 2277-2299. https://doi.org/10.1016/S0378-4266(01)00202-3
Li, Y. M., & Li, T. Y. (2013). Deriving market intelligence from microblogs, Decision Support Systems, 55, 206–217. http://dx.doi.org/10.1016/j.dss.2013.01.023
Li, X., Shen, D., Xue, M. & Zhang, W. (2017). Daily happiness and stock returns: The case of Chinese Company listed in the United State. Economic Modelling, 64, 496-501. http://dx.doi.org/10.1016/j.econmod.2017.03.002
Loewenstein, G., & Lerner, J.S. (2003). The Role of Affect in Decision Making. In: Davidson, R., Scherer, K., Goldsmith, H. (Eds.), Handbook of Affective Science. New York: Oxford University Press.
Loewenstein, G., & Rick, S. (2008). The Role of Emotion in Economic Behavior. In: Lewis, M., Haviland-Jones, J.M., L., F.B. (Eds.), Handbook of Emotions. The Guildford Press, 138–156.
Luo, X., Zhang, J., & Duan, W. (2013). Social Media and Firm Equity Value. Information Systems Research, 24 (1), 146–163. http://dx.doi.org/10.1287/isre.1120.0462
Mogilner, C., Aaker, J., & Kamvar, S. D. (2012). How Happiness Affects Choice. Journal of Consumer Research, 39, 429-443. https://doi.org/10.1086/663774
Naeem, M. A., Farid, S., Balli, F., & Shahzad, S. J. H. (2020). Can hapiness predict future volatility in stock Market? Research in International Business and Finance, 54, 101298. https://doi.org/10.1016/j.ribaf.2020.101298
Naeem, M. A., Mbarki, I., & Shahzad, S. J. H. (2021). Predictive role of online investor sentiment for cryptocurrency market: Evidence from happiness and fears. International Review of Economics and Finance, 73, 496-514. https://doi.org/10.1016/j.iref.2021.01.008
Owens, H., & Maxmen, J. S. (1979). Mood and affect: a semantic confusion. The American Journal of Psychiatry, 136 (1), 97-9. https://doi.org/10.1176/ajp.136.1.97
Ralph, A., & Damasio, A. R. (2000). The Interaction of Affect and Cognition: A Neurobiological Perspective. Handbook of Affect and Social Cognition. Routledge.
Ruan, Y., Durresi, A., & Alfantoukh, L. (2018). Using twitter trust network for stock market analysis. Knowledge-Based Systems, 1–12. https://doi.org/10.1016/j.knosys.2018.01.016
Rui, H., Liu, Y., & Whinston, A. (2013). Whose and what chatter matters? The effect of tweets on movie sales. Decision Support Systems, 55 (2013) 863–870. http://dx.doi.org/10.1016/j.dss.2012.12.022
Shen, D., Liu, L., & Zhang, Y. (2018). Quantifying the cross-sectional relationship between online sentiment and the skewness of stock returns. Physica A, 490, 928-934. http://dx.doi.org/10.1016/j.physa.2017.08.036
Siganos, A., Vagenas-Nanos, E., & Verwijmeren, P. (2014). Facebook’s daily sentiment and international stock markets. Journal of Economics Behavior & Organization, 107, 730-743. http://dx.doi.org/10.1016/j.jebo.2014.06.004
Slovic, P., Finucane, M., Peters, E., & MacGregor, D. G. (2002). The Affect Heuristic. In: Heuristic and Biases: the psychology of intuitive judgment. 397-420.
Slovic, P., Finucane, M., Peters, E., & MacGregor, D. G. (2004). Risk as Analysis and Risk as Feeling: Some Thoughts about Affect, Reason, Risk, and Rationality: Risk Analysis, 24 (2), 311-322. https://doi.org/10.1111/j.0272-4332.2004.00433.x
Stambaugh, R.F., Yu, J., Yuan, Y. (2012). The short of it: investor sentiment and anomalies. J. financ. Econ, 104 (2), 288–302. https://doi.org/10.1016/j.jfineco.2011.12.001
Topolinski, S., & Strack, F. (2009). The analysis of intuition: processind fluency and affect in judgements of semantic coherence. Psychology Press, 23 (8), 1465-1503. https://doi.org/10.1080/02699930802420745
Xiaomei, Z., Jing. Y., Jianpei, Z., & Hongyu, H. (2018). Microblog sentiment analysis with weak dependency connections. Knowledge-Based Systems, 142, 170-180. https://doi.org/10.1016/j.knosys.2017.11.035
You, W., Guo, Y., & Peng, C. (2017). Twitter’s daily happiness sentiment and the predictability of stock returns. Finance Research Letters, 23, 58-64. http://dx.doi.org/10.1016/j.frl.2017.07.018
Zhang, W., Wang, P., Li, X., & Shen, D. (2018). Twitter’s daily happiness sentiment and international stock returns: Evidence from linear and nonlinear causality tests. Journal of Behavioral and Experimental Finance, 18, 50-53. https://doi.org/10.1016/j.jbef.2018.01.005
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