Emoções e investimentos: a influência do sentimento do investidor nas criptomoedas durante a pandemia de Covid-19

Rayane Farias dos  Santos; César Augusto Tibúrcio Silva

doi:10.21680/2176-9036.2025v17n2ID37600

Autores

Rayane Farias dos Santos Universidade de Brasília (UnB) https://orcid.org/0000-0001-6500-1096
César Augusto Tibúrcio Silva Universidade de Brasília (UnB) https://orcid.org/0000-0002-5717-9502

DOI:

https://doi.org/10.21680/2176-9036.2025v17n2ID37600

Palavras-chave:

Sentimento do Investidor, Criptomoedas, Retorno, Volume de Negociação

Resumo

Objetivo: O estudo analisou a relação entre o sentimento do investidor e o retorno e volume de negociação das principais criptomoedas no Brasil, durante o período da pandemia de Covid-19.

Metodologia: Foram utilizadas duas métricas para capturar o sentimento do investidor, os índices de Felicidade (IFH) e Medo (FEARS), captados por meio das ferramentas Twitter e Google respectivamente. Os dados referentes as criptomoedas foram coletados no site Cryptocompare. Por meio de regressões quantílicas foram analisadas as variações no impacto do sentimento dos investidores nos diferentes tipos de moedas.

Resultados: Os resultados indicaram que a felicidade e o medo afetam de forma heterogênea as criptomoedas, com o IFH causando tanto impactos negativos quanto positivos no retorno de ativos como o BTC, USDC e USDT. E o FEARS um impacto predominantemente negativo no retorno de criptomoedas como o BTC e BRZ, mas positivo no ETH. Em termos de volume de negociação, o IFH influenciou de maneira ambígua o BRZ, enquanto o FEARS reduziu o volume das moedas BTC, USDT e USDC. Os padrões distintos de impacto identificados sugerem que o sentimento do investidor pode ser um indicador chave para a formulação de estratégias em um mercado altamente volátil e emocionalmente reativo.

Contribuições do Estudo: Contribui de maneira significativa para a literatura, ao focar no mercado brasileiro de criptomoedas, que tem sido pouco explorado em pesquisas internacionais. Utiliza uma abordagem quantílica para examinar como o sentimento dos investidores impacta múltiplas criptomoedas, oferecendo uma análise mais detalhada e não linear, algo raro na literatura. Além disso, ao investigar o comportamento das criptomoedas no Brasil durante o Covid-19, fornece insights críticos sobre como emoções coletivas, como medo e alegria, afetam os movimentos de mercado, especialmente em um ambiente dominado por investidores individuais, tornando o estudo relevante para mercados emergentes.

Downloads

Não há dados estatísticos.

Biografia do Autor

Rayane Farias dos Santos, Universidade de Brasília (UnB)

Doutoranda em Ciências Contábeis na Universidade de Brasília. Mestra em Ciências Contábeis pela Universidade Federal da Paraíba.

César Augusto Tibúrcio Silva, Universidade de Brasília (UnB)

Doutor em Controladoria e Contabilidade pela Universidade de São Paulo. Professor titular na Universidade de Brasília. Departamento de Ciências Contábeis e Atuariais.

Referências

Baker, M., & Wurgler, J. (2007). Investor sentiment in the stock market. Journal of Economic Perspectives, 21(2), 129-152. https://doi.org/10.1257/jep.21.2.129

Baur, D. G., & Dimpfl, T. (2018). Asymmetric volatility in cryptocurrencies. Economics Letters, 173, 148-151. https://doi.org/10.1016/j.econlet.2018.10.008

Biais, B., Bisière, C., Bouvard, M., Casamatta, C., & Menkveld, A. J. (2020). Equilibrium Bitcoin Pricing. Journal of Finance, 75(2), 753-791.

Böhme, R., Christin, N., Edelman, B., & Moore, T. (2015). Bitcoin: Economics, technology, and governance. Journal of economic Perspectives, 29(2), 213-238. https://doi.org/10.1257/jep.29.2.213

Bouri, E., Gupta, R., & Roubaud, D. (2019). Herding behaviour in cryptocurrencies. Finance Research Letters, 29, 216-221. https://doi.org/10.1016/j.frl.2018.07.008

Bouteska, A., Hajek, P., Abedin, M. Z., & Dong, Y. (2023). Effect of twitter investor engagement on cryptocurrencies during the COVID-19 pandemic. Research in International Business and Finance, 64, 101850. https://doi.org/10.1016/j.ribaf.2022.101850

Brooks, C. (2019).Introductory econometrics for finance. Cambridge university press.

Burggraf, T., Huynh, T. L. D., Rudolf, M., & Wang, M. (2021). Do FEARS drive bitcoin?. Review of Behavioral Finance, 13(3), 229-258.

Cevik, E., Kirci Altinkeski, B., Cevik, E. I., & Dibooglu, S. (2022). Investor sentiments and stock markets during the COVID-19 pandemic. Financial Innovation, 8(1), 69.

Cheah, E. T., & Fry, J. (2015). Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin. Economics letters, 130, 32-36. https://doi.org/10.1016/j.econlet.2015.02.029

Chen, H., De, P., Hu, Y. J., & 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

Chen, Z., Li, C., & Sun, W. (2020). Bitcoin price prediction using machine learning: An approach to sample dimension engineering. Journal of Computational and Applied Mathematics, 365, 112395. https://doi.org/10.1016/j.cam.2019.112395

Chen, W., Xu, D., Cheng, H. K., & Zhou, L. (2020). When blockchain meets social media: The effects of sentiment and social influence on cryptocurrency markets. Decision Support Systems, 130, 113227.

Chuen, K., Lee, D., Guo, L., & Wang, Y. (2017). Cryptocurrency: A New Investment Opportunity?(October 24, 2017). Available at SSRN 2994097.

Corbet, S., Larkin, C., & Lucey, B. (2020). The contagion effects of the COVID-19 pandemic: Evidence from gold and cryptocurrencies. Finance Research Letters, 35, 101554. https://doi.org/10.1016/j.frl.2020.101554

Da, Z., Engelberg, J., & Gao, P. (2015). The sum of all FEARS investor sentiment and asset prices. The Review of Financial Studies, 28(1), 1-32.

Eenmaa-Dimitrieva, H., & Schmidt-Kessen, M. J. (2019). Smart contracts: reducing risks in economic exchange with no-party trust?. European Journal of Risk Regulation, 10(2), 245-262.

Eom, C., Kaizoji, T., Kang, S. H., & Pichl, L. (2019). Bitcoin and investor sentiment: statistical characteristics and predictability. Physica A: Statistical Mechanics and its Applications, 514, 511-521. https://doi.org/10.1016/j.physa.2018.09.063

Galvão Jr, A. F. (2011). Quantile regression for dynamic panel data with fixed effects. Journal of Econometrics, 164(1), 142-157.

Garcia, D., Tessone, C. J., Mavrodiev, P., & Perony, N. (2014). The digital traces of bubbles: Feedback cycles between socio-economic signals in the Bitcoin economy. Journal of the Royal Society Interface, 11(99), 20140623. https://doi.org/10.1098/rsif.2014.0623

Güler, D. (2023). The Impact of investor sentiment on bitcoin returns and conditional volatilities during the era of Covid-19. Journal of Behavioral Finance, 24(3), 276-289.

Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263–291. https://doi.org/10.2307/1914185

Katsiampa, P., Corbet, S., & Lucey, B. (2019). Volatility spillover effects in leading cryptocurrencies: A BEKK-MGARCH analysis. Finance Research Letters, 29, 68-74. https://doi.org/10.1016/j.frl.2019.03.009

Kim, Y. B., Kim, J. G., Kim, W., Im, J. H., Kim, T. H., Kang, S. J., & Kim, C. H. (2016). Predicting fluctuations in cryptocurrency transactions based on user comments and replies. PloS one, 11(8), e0161197.

Kraaijeveld, O., & Smedt, J. (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

Kyriazis, N., Papadamou, S., Tzeremes, P., & Corbet, S. (2023). The differential influence of social media sentiment on cryptocurrency returns and volatility during COVID-19. The Quarterly Review of Economics and Finance, 89, 307-317. https://doi.org/10.1016/j.qref.2022.09.004

Liu, Y., Tsyvinski, A., & Wu, X. (2022). Common risk factors in cryptocurrency. Journal of Finance, 77(2), 577-622. https://doi.org/10.1111/jofi.13119

Mai, F., Bai, Q., Shan, J., Wang, X. S., & Chiang, R. H. (2015). The impacts of social media on Bitcoin performance. Decision Support Systems, 93, 138-149.

Mancini-Griffoli, T., Peria, M. S. M., Agur, I., Ari, A., Kiff, J., Popescu, A., & Rochon, C. (2018). Casting light on central bank digital currency. IMF Staff Discussion Notes, 18(08), 1.

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 & Finance, 73, 496-514.

Nakamoto, S., 2008. Bitcoin: A Peer-to-Peer Electronic Cash System.

Ojo, A. (2023, May 15). Crypto regulation and adoption in Brazil: What the experts say. Business Today. https://www.businesstoday.in/latest/world/story/crypto-regulation-and-adoption-in-brazil-what-the-experts-say-380221-2023-05-15

Piñeiro-Chousa, J., López-Cabarcos, M. Á., Pérez-Pico, A. M., & Ribeiro-Navarrete, B. (2018). Does social network sentiment influence the relationship between the S&P 500 and gold returns?. International Review of Financial Analysis, 57, 57-64. https://doi.org/10.1016/j.irfa.2018.02.005

Prechter Jr, R. R. (2001). Unconscious herding behavior as the psychological basis of financial market trends and patterns. The Journal of Psychology and Financial Markets, 2(3), 120-125. https://doi.org/10.1207/S15327760JPFM0203_1

Receita Federal do Brasil. (2023). Criptoativos: Receita Federal detecta crescimento vertiginoso na movimentação de stablecoins. https://www.gov.br/receitafederal/pt-br/assuntos/noticias/2023/outubro/criptoativos-receita-federal-detecta-crescimento-vertiginoso-na-movimentacao-de-stablecoins

Rupande, L., Muguto, H. T., & Muzindutsi, P. F. (2019). Investor sentiment and stock return volatility: Evidence from the Johannesburg Stock Exchange. Cogent Economics & Finance, 7: 1600233. https://doi.org/10.1080/23322039.2019.1600233

Salisu, A. A., & Akanni, L. O. (2020). Constructing a global fear index for the COVID-19 pandemic. Emerging Markets Finance and Trade, 56(10), 2310-2331. https://doi.org/10.1080/1540496X.2020.1785424

Schär, F. (2021). Decentralized finance: On blockchain-and smart contract-based financial markets. FRB of St. Louis Review, 103(2), 153-74. https://doi.org/10.20955/r.103.153-74

Shen, D., Urquhart, A., & Wang, P. (2019). Does twitter predict Bitcoin?. Economics letters, 174, 118-122. https://doi.org/10.1016/j.econlet.2018.11.007

Shiller, R. J. (2003). From efficient markets theory to behavioral finance. Journal of economic perspectives, 17(1), 83-104.

Smales, L. A. (2019). Bitcoin as a safe haven: Is it even worth considering? Finance Research Letters, 30, 240-245. https://doi.org/10.1016/j.frl.2018.11.002

Tantaopas, P., Padungsaksawasdi, C., & Treepongkaruna, S. (2016). Attention effect via internet search intensity in Asia-Pacific stock markets. Pacific-Basin Finance Journal, 38, 107-124. https://doi.org/10.1016/j.pacfin.2016.03.008

Tetlock, P. C. (2007). Giving content to investor sentiment: The role of media in the stock market. The Journal of finance, 62(3), 1139-1168. https://doi.org/10.1111/j.1540-6261.2007.01232.x

Vasileiou, E., & Koutrakos, P. (2023). Performance of Cryptocurrencies Under a Sentiment Analysis Approach in the Time of COVID-19. In Data Analytics for Management, Banking and Finance: Theories and Application (pp. 255-265). Cham: Springer Nature Switzerland.

Yağlı, İ., & Haykır, Ö. (2023). Investor Happiness and Cryptocurrency Returns: Fresh Evidence from Top Five Cryptocurrencies. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, (59), 293-305. https://doi.org/10.30794/pausbed.1183813

Emoções e investimentos: a influência do sentimento do investidor nas criptomoedas durante a pandemia de Covid-19

Autores

DOI:

Palavras-chave:

Resumo

Downloads

Biografia do Autor

Rayane Farias dos Santos, Universidade de Brasília (UnB)

César Augusto Tibúrcio Silva, Universidade de Brasília (UnB)

Referências

Downloads

Publicado

Como Citar

Edição

Seção

Licença

dados

Edição Atual