ISSN: 2056-3736 (Online Version) | 2056-3728 (Print Version)

Impacts of Stock Indices, Oil, and Twitter Sentiment on Major Cryptocurrencies during the COVID-19 First Wave

Νikolaos A. Kyriazis

Correspondence: Νikolaos A. Kyriazis,

Department of Economics, University of Thessaly, Greece

pdf (766.75 Kb) | doi:


This paper sets under scrutiny whether the S&P500, oil, and Twitter-based uncertainty about financial markets affect the returns and volatility of three major cryptocurrencies. Estimations are conducted concerning Bitcoin, Bitcoin Cash, and Dogecoin during the first wave of the COVID-19 pandemic. Findings document that Twitter uncertainty exhibits a weaker impact on cryptocurrencies than the S&P500 and crude oil. S&P500 constitutes a positive and significant determinant while impacts of oil are weaker and mixed. The volatility of cryptocurrencies is found to display a non-linear character. Moreover, it is revealed that Dogecoin could be more useful to investors as a speculative tool than Bitcoin and Bitcoin Cash. These outcomes inform the interested reader that traditional investments are influential in a much larger degree towards modern financial assets than investor sentiment when economic conditions are stressed.


  Twitter Sentiment, Stock, Oil, Cryptocurrency, COVID-19 pandemic.


Adekoya, O. B., & Oliyide, J. A. (2021). How COVID-19 drives connectedness among commodity and financial markets: Evidence from TVP-VAR and causality-in-quantiles techniques. Resources Policy, 70, 101898.Akaike, H. (1974). A new look at the statistical model identification. IEEE transactions on automatic control, 19(6), 716-723.Albulescu, C. T. (2021). COVID-19 and the United States financial markets’ volatility. Finance Research Letters, 38, 101699.Ammous, S. (2018). Can cryptocurrencies fulfil the functions of money? The Quarterly Review of Economics and Finance, 70, 38-51.Baig, A. S., Butt, H. A., Haroon, O., & Rizvi, S. A. R. (2021). Deaths, panic, lockdowns and US equity markets: The case of COVID-19 pandemic. Finance research letters, 38, 101701.Baig, A., Blau, B. M., & Sabah, N. (2019). Price clustering and sentiment in bitcoin. Finance Research Letters, 29, 111-116.Balcilar, M., Ozdemir, Z. A., Ozdemir, H., & Wohar, M. E. (2020). Fed’s unconventional monetary policy and risk spillover in the US financial markets. The Quarterly Review of Economics and Finance, 78, 42-52.Beneki, C., Koulis, A., Kyriazis, N. A., & Papadamou, S. (2019). Investigating volatility transmission and hedging properties between Bitcoin and Ethereum. Research in International Business and Finance, 48, 219-227.Blau, B. M. (2018). Price dynamics and speculative trading in Bitcoin. Research in International Business and Finance, 43, 15-21.Böhme, R., Christin, N., Edelman, B., & Moore, T. (2015). Bitcoin: Economics, technology, and governance. Journal of economic Perspectives, 29(2), 213-38.Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307-327.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.Ciner, C. (2021). Stock Return Predictability in the time of COVID-19. Finance Research Letters, 38, 101705.Conlon, T., & McGee, R. (2020). Safe haven or risky hazard? Bitcoin during the COVID-19 bear market. Finance Research Letters, 35, 101607.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.Diks, C., & Panchenko, V. (2006). A new statistic and practical guidelines for nonparametric Granger causality testing. Journal of Economic Dynamics and Control, 30(9-10), 1647-1669.Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 987-1007.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.Fassas, A. P., Papadamou, S., & Koulis, A. (2020). Price discovery in bitcoin futures. Research in International Business and Finance, 52, 101116.Foley, S., Karlsen, J. R., & Putniņš, T. J. (2019). Sex, drugs, and bitcoin: How much illegal activity is financed through cryptocurrencies?. The Review of Financial Studies, 32(5), 1798-1853.Goodell, J. W., & Goutte, S. (2021). Co-movement of COVID-19 and Bitcoin: Evidence from wavelet coherence analysis. Finance Research Letters, 38, 101625.Granger, C. W. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica: Journal of the Econometric Society, 424-438.Guégan, D., & Renault, T. (2020). Does investor sentiment on social media provide robust information for Bitcoin returns predictability?. Finance Research Letters, 101494.Higgins, M. L., & Bera, A. K. (1992). A class of nonlinear ARCH models. International Economic Review, 137-158.Huang, D., Jiang, F., Tu, J., & Zhou, G. (2015). Investor sentiment aligned: A powerful predictor of stock returns. The Review of Financial Studies, 28(3), 791-837.Ibikunle, G., McGroarty, F., & Rzayev, K. (2020). More heat than light: Investor attention and bitcoin price discovery. International Review of Financial Analysis, 69, 101459.Karalevicius, V., Degrande, N., & De Weerdt, J. (2018). Using sentiment analysis to predict interday Bitcoin price movements. The Journal of Risk Finance, Vol. 19 No. 1, pp. 56-75.Kyriazis, N., Papadamou, S., & Corbet, S. (2020). A systematic review of the bubble dynamics of cryptocurrency prices. Research in International Business and Finance, 101254.Lahiani, A., & Jlassi, N. B. (2021). Nonlinear tail dependence in cryptocurrency-stock market returns: The role of Bitcoin futures. Research in International Business and Finance, 56, 101351.Mariana, C. D., Ekaputra, I. A., & Husodo, Z. A. (2021). Are Bitcoin and Ethereum safe-havens for stocks during the COVID-19 pandemic?. Finance research letters, 38, 101798.Mensi, W., Sensoy, A., Vo, X. V., & Kang, S. H. (2020). Impact of COVID-19 outbreak on asymmetric multifractality of gold and oil prices. Resources Policy, 69, 101829.Omane-Adjepong, M., & Alagidede, I. P. (2021). Exploration of safe havens for Africa's stock markets: A test case under COVID-19 crisis. Finance Research Letters, 38, 101877.Papadamou, S., Kyriazis, N. A., & Tzeremes, P. G. (2021). Non-linear causal linkages of EPU and gold with major cryptocurrencies during bull and bear markets. The North American Journal of Economics and Finance, 56, 101343.Philippas, D., Rjiba, H., Guesmi, K., & Goutte, S. (2019). Media attention and Bitcoin prices. Finance Research Letters, 30, 37-43.Rahman, M. L., Amin, A., & Al Mamun, M. A. (2021). The COVID-19 outbreak and stock market reactions: Evidence from Australia. Finance Research Letters, 38, 101832.Renault, T. (2020). Sentiment analysis and machine learning in finance: a comparison of methods and models on one million messages. Digital Finance, 2(1), 1-13.Salisu, A. A., & Vo, X. V. (2020). Predicting stock returns in the presence of COVID-19 pandemic: The role of health news. International Review of Financial Analysis, 71, 101546.Salisu, A. A., Raheem, I. D., & Vo, X. V. (2021). Assessing the safe haven property of the gold market during COVID-19 pandemic. International Review of Financial Analysis, 74, 101666.Schwarz, G. (1978). Estimating the dimension of a model. Annals of statistics, 6(2), 461-464.Shen, D., Urquhart, A., & Wang, P. (2019). Does twitter predict Bitcoin?. Economics Letters, 174, 118-122.Topcu, M., & Gulal, O. S. (2020). The impact of COVID-19 on emerging stock markets. Finance Research Letters, 36, 101691.Yermack, D. (2015). Is Bitcoin a real currency? An economic appraisal. In Handbook of digital currency (pp. 31-43). Academic Press.