SOCIAL MEDIA INFLUENCE ON PETROBRAS’ VALUE
DOI:
https://doi.org/10.51320/rmc.v23i3.1371Keywords:
Textual Sentiment, Social Media, Investor sentimentAbstract
This research seeks to contribute to the discussion on investor sentiment. The objective of the research was to analyze the relationship between social media sentiment on Twitter and the return of Petrobras. Thus, tweets about Petrobras, from 2010 to 2020, were analyzed to verify whether the textual sentiment of these messages impacts company return. The method to classify the words was the perceptual mapping together with the mean and standard deviation of the frequency of the terms in positive or negative days. The results showed that the variation of the total sentiment and the negative sentiment are related to the return. The variation in sentiment is significant when the market is bearish, demonstrating that the negative variation in sentiment intensifies the bearish movement in the market, smoothing the market’s decline when the variation in sentiment is positive. The findings corroborate the behavioural finance theory that sentiment is related to return, textual feeling in the case of the study. Finally, different samples were analyzed using the same methodology. With this, it was possible to verify that individuals talk more about the state-owned company than about the stock tickers or about the company itself on its official page.
Downloads
References
Alkubaisi, G. A. A. J., Kamaruddin, S. S., & Husni, H. (2018). Stock Market Classification Model Using Sentiment Analysis on Twitter Based on Hybrid Naive Bayes Classifiers. Computer and Information Science, 11 (1).
Almeida, M. D., Maia, V. M., Tommasetti, R., & de Oliveira Leite, R. (2021). Sentiment analysis based on a social media customised dictionary. MethodsX, 8, 101449.
Baker, M., & Wurgler, J. (2006) Investor sentiment and the cross-section of stock returns. Journal of Finance, 61 (4), 1645–1680.
Bannier, C., Pauls, T., & Walter, A. (2019) Content analysis of business communication: introducing a German dictionary. Journal of Business Economics, 89 (1), 79–123.
Bartov, E., Faurel, L., & Mohanram, P. S. (2018) Can twitter help predict firm-level earnings and stock returns? The Accounting Review, 93 (3), 25–57.
Beattie, V. (2014). Accounting narratives and the narrative turn in accounting research: Issues, theory, methodology, methods and a research framework. British Accounting Review, 46 (2), 111–134.
Bukovina, J. (2016). Social media big data and capital markets-An overview. Journal of Behavioral and Experimental Finance, 11, 18–26.
Buzz Rankings de 2019: Brasil | YouGov - BrandIndex. (2020, June 13). Retrieved from: <https://www.brandindex.com/ranking/brazil/2019-buzz>.
Carosia, A. E. O., Coelho, G. P., & Silva, A. E. A. (2019). Analyzing the Brazilian Financial Market through Portuguese Sentiment Analysis in Social Media. Applied Artificial Intelligence, 34 (1), 1–19.
Clement, J. (2020, March 15). Countries with most Twitter users 2020. Statista. Retrieved from: <https://www.statista.com/statistics/242606/number-of-active-twitter-users-in-selected-countries/>.
Cruz, L., Ochoa, J., Roche, M., Poncelet, P. (2017) Dictionary – Based Sentiment Analysis Aplied to Specific Domain. In: Lossio-Ventura J., Alatrista-Salas H. (eds) Information Management and Big Data. SIMBig 2015, SIMBig 2016. Communications in Computer and Information Science, vol 656. Springer, Cham. https://doi.org/10.1007/978-3-319-55209-5_5.
Silva, S. O., Maia, V. M., Haveroth, J., & Tommasetti, R. (2021). Papel da Auditoria: uma visão a partir do Twitter. Revista Catarinense da Ciência Contábil, 20, e3183-e3183.
Dařena, F., Petrovský, J., Přichystal, J., & Žižka, J. (2018). Machine learning-based analysis of the association between online texts and stock price movements. Inteligencia Artificial, 21 (61), 95–110.
Fang, B., Liang, S., Zou, Q., & Huang, W. (2017). Research on sentiment analysis of financial texts. 7th IEEE International Conference on Electronics Information and Emergency Communication, (ICEIEC), 248-252.
Feuerriegel, S., Wolff, G., & Neumann, D. (2015). Information processing of foreign exchange news: Extending the overshooting model to include qualitative information from news sentiment. SSRN Eletronic Journal.
Gandhi, P., Loughran, T., & Mcdonald, B. (2019). Using Annual Report Sentiment as a Proxy for Financial Distress in U.S. Banks. Journal of Behavioral Finance, 20 (4), 424–436.
Ge, Q., Kurov, A., & Wolfe, M. H. (2018). Stock Market Reactions to Presidential Statements: Evidence from Company-Specific Tweets. Economics Faculty Scholarship, 29.
Godsay, M. (2015) The Process of Sentiment Analysis: A Study. International Journal of Computer Applications, 126 (7), 26–30.
Gower, J.C, & Legendre, P. (1986). Metric and Euclidean properties of dissimilarity coefficients. Journal of Classification, 3, 5-18.
Gupta, A., Simaan, M., & Zaki, M. J. (2017). Investigating bank failures using text mining. 2016 IEEE Symposium Series on Computational Intelligence, SSCI.
Ito, T., Sakaji, H., Izumi, K., Tsubouchi, K., & Yamashita, T. (2017). Development of an interpretable neural network model for creation of polarity concept dictionaries. IEEE International Conference on Data Mining Workshops, ICDMW.
Jiang, F., Lee, J., Martin, X., & Zhou, G. (2019). Manager Sentiment and Stock Returns. Journal of Financial Economics, 132 (1), 126-149.
Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Jornal of the Econometrica society, 47 (2), 263–292.
Kearney, C., & Liu, S. (2014). Textual sentiment in finance: A survey of methods and models. International Review of Financial Analysis, 33, 171–185.
Li, X., Wu, P., & Wang, W. (2020). Incorporating stock prices and news sentiments for stock market prediction: A case of Hong Kong. Information Processing and Management, 57 (5).
Loughran, T., & Mcdonald, B. (2015). The Use of Word Lists in Textual Analysis. Journal of Behavioral Finance, 16 (1), 1–11.
Mai, F., Shan, Z., Bai, Q., Wang, X. S., & Chiang, R. H. L. (2018). How Does Social Media Impact Bitcoin Value? A Test of the Silent Majority Hypothesis. Journal of Management Information Systems, 35 (1), 19–52.
Mangee, N. (2018). Stock Returns and the Tone of Marketplace Information: Does Context Matter? Journal of Behavioral Finance, 19 (4), 396–406.
Marschner, P. F., & Ceretta, P. S. (2021). Sentimento do investidor, incerteza econômica e política monetária no Brasil. Revista Contabilidade & Finanças, 32, 528-540.
Martins, R., Pereira, A., & Benevenuto, F. (2015) An approach to sentiment analysis of web applications in Portuguese. WebMedia 2015 - Proceedings of the 21st Brazilian Symposium on Multimedia and the Web, 105–112.
McGurk, Z., Nowak, A., & Hall, J. C. (2020). Stock returns and investor sentiment: textual analysis and social media. Journal of Economics and Finance, 44 (3), 458–485.
Naylor, R. W., Lamberton, C. P., & West, P. M. (2012). Beyond the “like” button: The impact of mere virtual presence on brand evaluations and purchase intentions in social media settings. Journal of Marketing, 76 (6), 105–120.
Nofsinger, J. (2010). The Psychology of Investing. 4. ed. New Jersey: Prentice Hall.
Otoboni, J., & Freire, D. (2020). Seis anos da Lava Jato: relembre todas as fases da operação. Retrieved from: <https://www.cnnbrasil.com.br/politica/2020/03/07/seis-anos-da-lava-jato-relembre-todas-as-fases-da-operacao>.
Pengnate, S. F., & Riggins, F. J. (2020). The role of emotion in P2P microfinance funding: A sentiment analysis approach. International Journal of Information Management, 54.
Piccoli, P., da Costa Jr, N. C., da Silva, W. V., & Cruz, J. A. (2018). Investor sentiment and the risk–return tradeoff in the Brazilian market. Accounting & Finance, 58, 599-618.
Pinto, A. E. De S. (2020. Octuber 11). Pelo 4o ano seguido, Petrobras é a marca que representa o Brasil. Folha de S. Paulo. Retrieved from: <https://top-of-mind.folha.uol.com.br/2019/10/pelo-4o-ano-seguido-petrobras-e-a-marca-que-representa-o-brasil.shtml>.
Prates, W. R., da Costa, N. C., & Santos, A. A. (2019). Efeito disposição: Propensão à venda de investidores individuais e institucionais. Revista Brasileira de Economia, 73, 97-119.
Ranco, G., Aleksovski, D., Caldarelli, G., Grčar, M., & Mozetič, I. (2015). The effects of twitter sentiment on stock price returns. PLoS ONE, 10 (9), 1–21.
Santana, C. V. S., Santos, L. P. G. D., Carvalho, C. V. D. O., & Martinez, A. L. (2019). Sentimento do investidor e gerenciamento de resultados no Brasil. Revista Contabilidade & Finanças, 31, 283-301.
Santos, H. S., Laender, A., & Pereira, A. C. (2015). Uma Visão do Mercado Brasileiro de Ações a partir de Dados do Twitter. IV Brazilian Workshop on Social Network Analysis and Mining, 10 (3), 369–394.
Sharpe, W. F. A. (1963). Simplified Model For Portfolio Analysis. Magazine Science, 9 (2), 277–293.
Souza, D. M. S. de, Lucena, W. G. L., & Queiroz, D. B. de. (2019). O Efeito do Sentimento do Investidor Expresso via Twitter sobre o Comportamento do Mercado Acionário Brasileiro Durante o Período Eleitoral. XIX Congresso USP, 1–19.
Sprenger, T. O., Sandner, P. G., Tumasjan, A., & Welpe, I. M. (2014). News or noise? Using twitter to identify and understand company-specific news flow. Journal of Business Finance and Accounting, 41 (7–8), 791–830.
Tarde, G. (1902). Psychologie Économique. Paris: Bibliothèque de philosophie contemporaine.
Tommasetti, R., Maia, V. M., & Macedo, M. A. da S. (2021). Twitter as ‘bully pulpit’: Brazilian banking sector case. Applied Economics Letters, 28 (1), 61–64.
Yadav, A., Jha, C. K., Sharan, A., & Vaish, V. (2020). Sentiment analysis of financial news using unsupervised approach. Procedia Computer Science, 167, 589-598.
Downloads
Published
Versions
- 2023-11-30 (2)
- 2022-12-23 (1)
How to Cite
Issue
Section
License
Copyright (c) 2022 Milene Dias Almeida, Vinicius Mothé Maia, Roberto Tommasetti
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Autores que publicam nesta revista concordam com os seguintes termos:
a) Os autores mantêm os direitos autorais e concedem à revista o direito de primeira publicação, com o trabalho simultaneamente licenciado sob a Licença Attribution-NonCommercial-ShareAlike 4.0 International, que permite o compartilhamento do trabalho com reconhecimento da autoria e publicação inicial nesta revista. Essa licença permite que outros remixem, adaptem e criem a partir do seu trabalho para fins não comerciais, desde que atribuam a você o devido crédito e que licenciem as novas criações sob termos idênticos.
b) Não cabe aos autores compensação financeira a qualquer título, por artigos ou resenhas publicados na Revista Mineira de Contabilidade.
c) Os conceitos expressos nos artigos publicados pela RMC são de inteira responsabilidade de seus autores.
Creative Commons Atribuição 4.0 Internacional
