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Performance of Cryptocurrencies Under a Sentiment Analysis Approach in the Time of COVID-19

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Data Analytics for Management, Banking and Finance

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Abstract

This chapter presents a sentiment analysis model that examines the performance of the most popular and highly capitalized cryptocurrencies during the COVID-19 era. Specifically, we look at the performance of Bitcoin (BTC), Ethereum (ETH), Binance (BNB), and Cardano (ADA) during the period 1/1/2020–30/9/2021. We do not use unique and expensive data and we do not apply complex models for our analysis. Instead, we construct behavioral indices based on Google trends data and we test the impact of these indices on the performance of BTC, ETH, BNB, and ADA. We used open source software, such as Python, so similar analysis can be carried out by anyone who is interested in financial markets. The results of this study support the following conclusions: (i) when the health risk increases, the BTC, BNB, and ADA prices fall (and vice versa), (ii) the Google trends indices, which reveal the users’ intention to buy a cryptocurrency, could be useful indicators of crypto performance, but (iii) the sell sentiment, at least in our case, does not have any statistically significant effect. Thus, sentiment analysis based on Google searches could be useful for practitioners, investors, analysts, and scholars, and further study on this direction should be done.

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Notes

  1. 1.

    https://www.who.int/emergencies/diseases/novel-coronavirus-2019/interactive-timeline#!

  2. 2.

    https://www.worldometers.info/coronavirus/ (13/2/2023).

  3. 3.

    Heightened health risk increases risk aversion and leads to a decline in asset prices (Smith et al., 2005).

  4. 4.

    WHO announced “COVID-19” as the name of this new disease on 11 February, 2020 – short for COronaVIrus Disease 2019.

References

  • Ammous, S. (2018). Can cryptocurrencies fulfil the functions of money? The Quarterly Review of Economics and Finance, 70, 38–51.

    Article  Google Scholar 

  • Barberis, N., & Thaler, R. (2003). A survey of behavioral finance. In Handbook of the economics of finance (Vol. 1, pp. 1053–1128).

    Google Scholar 

  • Betsch, C., Korn, L., Sprengholz, P., Felgendreff, L., Eitze, S., Schmid, P., & Böhm, R. (2020). Social and behavioral consequences of mask policies during the COVID-19 pandemic. Proceedings of the National Academy of Sciences, 117(36), 21851–21853.

    Article  Google Scholar 

  • Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327.

    Article  MathSciNet  MATH  Google Scholar 

  • Choi, H., & Varian, H. (2012). Predicting the present with Google Trends. Economic Record, 88, 2–9.

    Article  Google Scholar 

  • Conlon, T., Corbet, S., & McGee, R. J. (2020). Are cryptocurrencies a safe haven for equity markets? An international perspective from the COVID-19 pandemic. Research in International Business and Finance, 54, 101248.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Corbet, S., Lucey, B., Urquhart, A., & Yarovaya, L. (2019). Cryptocurrencies as a financial asset: A systematic analysis. International Review of Financial Analysis, 62, 182–199.

    Article  Google Scholar 

  • Corbet, S., Meegan, A., Larkin, C., Lucey, B., & Yarovaya, L. (2018). Exploring the dynamic relationships between cryptocurrencies and other financial assets. Economics Letters, 165, 28–34.

    Article  Google Scholar 

  • Costola, M., Iacopini, M., & Santagiustina, C. R. (2021). Google search volumes and the financial markets during the COVID-19 outbreak. Finance Research Letters, 42, 101884.

    Article  Google Scholar 

  • Dergiades, T., Milas, C., & Panagiotidis, T. (2015). Tweets, Google trends, and sovereign spreads in the GIIPS. Oxford Economic Papers, 67(2), 406–432.

    Article  Google Scholar 

  • Dyhrberg, A. H. (2016). Bitcoin, gold and the dollar–a GARCH volatility analysis. Finance Research Letters, 16, 85–92.

    Article  Google Scholar 

  • Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383–417.

    Article  Google Scholar 

  • Feng, W., Wang, Y., & Zhang, Z. (2018). Can cryptocurrencies be a safe haven: A tail risk perspective analysis. Applied Economics, 50(44), 4745–4762.

    Article  Google Scholar 

  • Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The Journal of Finance, 48(5), 1779–1801.

    Article  Google Scholar 

  • Goodell, J. W., & Goutte, S. (2021). Diversifying equity with cryptocurrencies during COVID-19. International Review of Financial Analysis, 76, 101781.

    Article  Google Scholar 

  • Jun, S. P., Yoo, H. S., & Choi, S. (2018). Ten years of research change using Google Trends: From the perspective of big data utilizations and applications. Technological Forecasting and Social Change, 130, 69–87.

    Article  Google Scholar 

  • Han, C., Yang, M., & Piterou, A. (2021). Do news media and citizens have the same agenda on COVID-19? An empirical comparison of twitter posts. Technological Forecasting and Social Change, 169, 120849.

    Article  Google Scholar 

  • McKibbin, W., & Fernando, R. (2020). The economic impact of COVID-19. Economics in the Time of COVID-19, 45(10.1162).

    Google Scholar 

  • Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093–1113.

    Article  Google Scholar 

  • Nelson, D. B. (1991). Conditional Heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.

    Article  MathSciNet  MATH  Google Scholar 

  • O’Leary, D. E., & Storey, V. C. (2020). A Google–Wikipedia–Twitter model as a leading indicator of the numbers of coronavirus deaths. Intelligent Systems in Accounting, Finance and Management, 27(3), 151–158.

    Article  Google Scholar 

  • Preis, T., Moat, H. S., & Stanley, H. E. (2013). Quantifying trading behavior in financial markets using Google Trends. Scientific Reports, 3(1), 1–6.

    Article  Google Scholar 

  • Smith, R. D., Yago, M., Millar, M., & Coast, J. (2005). Assessing the macroeconomic impact of a healthcare problem: The application of computable general equilibrium analysis to antimicrobial resistance. Journal of Health Economics, 24(6), 1055–1075.

    Article  Google Scholar 

  • Van Aelst, P., Toth, F., Castro, L., Štětka, V., Vreese, C. D., Aalberg, T., et al. (2021). Does a crisis change news habits? A comparative study of the effects of COVID-19 on news media use in 17 European countries. Digital Journalism, 9, 1–31.

    Google Scholar 

  • Vasileiou, E. (2021a). Efficient markets hypothesis in the time of COVID-19. Review of Economic Analysis, 13(1), 45–63.

    Article  Google Scholar 

  • Vasileiou, E. (2021b). Explaining stock markets’ performance during the COVID‐19 crisis: Could Google searches be a significant behavioral indicator? Intelligent Systems in Accounting, Finance and Management, 28(3), 173–181.

    Article  Google Scholar 

  • Vasileiou, E. (2021c). Behavioral finance and market efficiency in the time of the COVID-19 pandemic: Does fear drive the market? International Review of Applied Economics, 35(2), 224–241.

    Article  Google Scholar 

  • Vindegaard, N., & Benros, M. E. (2020). COVID-19 pandemic and mental health consequences: Systematic review of the current evidence. Brain, Behavior, and Immunity, 89, 531–542.

    Article  Google Scholar 

  • Xue, J., Chen, J., Hu, R., Chen, C., Zheng, C., Su, Y., & Zhu, T. (2020). Twitter discussions and emotions about the COVID-19 pandemic: Machine learning approach. Journal of Medical Internet Research, 22(11), e20550.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Financial and Management Engineering, University of the Aegean, Chios, Greece

    Evangelos Vasileiou & Petros Koutrakos

Authors
  1. Evangelos Vasileiou
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  2. Petros Koutrakos
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Correspondence to Evangelos Vasileiou .

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Editors and Affiliations

  1. Faculty of Sciences of Monastir, University of Monastir, Monastir, Tunisia

    Foued Saâdaoui

  2. Department of Mathematics and Statistics, Georgia State University, Atlanta, GA, USA

    Yichuan Zhao

  3. Department of Business Computing, University of Sousse, Sousse, Tunisia

    Hana Rabbouch

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Vasileiou, E., Koutrakos, P. (2023). Performance of Cryptocurrencies Under a Sentiment Analysis Approach in the Time of COVID-19. In: Saâdaoui, F., Zhao, Y., Rabbouch, H. (eds) Data Analytics for Management, Banking and Finance. Springer, Cham. https://doi.org/10.1007/978-3-031-36570-6_11

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