Rui Dias – School of Business and Administration, Polytechnic Institute of SetΓΊbal, Portugal; CEFAGE-UE, IIFA, University of Γvora, Portugal
Nicole HortaΒ – School of Business and Administration, Polytechnic Institute of SetΓΊbal, Portugal
Catarina RevezΒ – School of Business and Administration, Polytechnic Institute of SetΓΊbal, Portugal
Paula HeliodoroΒ – School of Business and Administration, Polytechnic Institute of SetΓΊbal, Portugal
Paulo Alexandre – School of Business and Administration, Polytechnic Institute of SetΓΊbal, Portugal
Keywords:Β Β Β Β Β Β Β Β Β Β Cryptocurrency markets;
Efficient market hypothesis;
Market efficiency
DOI: https://doi.org/10.31410/ERAZ.2022.87
Abstract: When compared to traditional financial markets, cryptocurrenΒcies were seen as assets with minimal correlations. However, because this continually expanding financial market is marked by substantial volatiliΒty and strong price movements over a short period, developing an accurate and reliable forecasting model is deemed crucial for portfolio management and optimization. Given the relevance of cryptocurrencies in the global econΒomy, it is important to determine if Bitcoin (BTC) becomes more predictable as investors adopt more aggressive trading positions. We examine BTC over the period from May 15th, 2021, to April 14th, 2022 (8676-time data), using inΒtraday (hourly) time scales. The results reveal that the random walk hypothΒesis is rejected at lags of 3 to 16 days, while we see that the BTC market tends toward efficiency (see the evolution between lags of 16 and 2). These findings reveal that, given the uncertainty in the global economy in 2022, namely the Russian invasion of Ukraine, the BTC market shows values of the variance raΒtios close to unity, implying that it is, apparently, not predictable and that the residuals are not autocorrelated in time. In addition, the results of the DeΒtrended Fluctuation Analysis (DFA) exponent show that this market does not exhibit characteristics of (in) efficiency in its weak form. In other words, this market does not have persistent and mean-reverting properties, thus valiΒdating the results of Wrightβs Rankings and Signs variance test.


8th International Scientific ERAZ Conference β ERAZ 2022 β Conference Proceedings: KNOWLEDGE BASED SUSTAINABLE DEVELOPMENT, Online-Virtual (Prague, Czech Republic), May 26, 2022
ERAZ Conference Proceedings published by: Association of Economists and Managers of the Balkans – Belgrade, Serbia
ERAZ conference partners: Faculty of Economics and Business, Mediterranean University, Montenegro; University of National and World Economy – Sofia, Bulgaria; Faculty of Commercial and Business Studies – Celje, Slovenia; AMBIS University, Prague – Czech Republic; Faculty of Applied Management, Economics and Finance β Belgrade, Serbia
ERAZ Conference 2022 Conference Proceedings: ISBN 978-86-80194-60-8, ISSN 2683-5568, DOI: https://doi.org/10.31410/ERAZ.2022
Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-Non-Commercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission.Β
Sugested citation
Dias, R.,Β Horta, N.,Β Revez, C., Heliodoro, P., & Alexandre, P.Β (2022). The Evolution of the Cryptocurrency Market Is Trending toward Efficiency? In V. Bevanda (Ed.), ERAZ Conference – Knowlegde Based Sustainable Development: Vol 8. Conference Proceedings (pp. 87-94). Association of Economists and Managers of the Balkans. https://doi.org/10.31410/ERAZ.2022.87
References
Chibane, M., & Janson, N. (2020). Do Bitcoin Stylized Facts Depend on Geopolitical Risk? SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3530020Β
Dias, R., Heliodoro, P., & Alexandre, P. (2020). Efficiency of Asean-5 Markets: An Detrended Fluctuation Analysis. Mednarodno Inovativno Poslovanje = Journal of Innovative BusiΒness and Management, 12(2), 13β19. https://doi.org/10.32015/jibm.2020.12.2.13-19Β
Dias, R., Heliodoro, P., Alexandre, P., Santos, H., & Farinha, A. (2021). Long memory in stock returns: Evidence from the Eastern European markets. SHS Web of Conferences, 91. https://doi.org/10.1051/shsconf/20219101029Β Β
Dias, R., Pereira, J. M., & Carvalho, L. C. (2022). Are African Stock Markets Efficient? A ComΒparative Analysis Between Six African Markets, the UK, Japan and the USA in the Period of the Pandemic. NaΕ‘e Gospodarstvo/Our Economy, 68(1), 35β51. https://doi.org/10.2478/ngoe-2022-0004Β Β
Dias, R. T., Pardal, P., Santos, H., & Vasco, C. (2021). Testing the Random Walk Hypothesis for Real Exchange Rates. June, 304β322. https://doi.org/10.4018/978-1-7998-6926-9.ch017Β
Dickey, D., & Fuller, W. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49(4), 1057β1072. https://doi.org/10.2307/1912517Β
Dimitrova, V., FernΓ‘ndez-MartΓnez, M., SΓ‘nchez-Granero, M. A., & Segovia, J. E. T. (2019). Some comments on Bitcoin market (in)efficiency. PLoS ONE, 14(7). https://doi.org/10.1371/journal.pone.0219243Β Β
Fang, F., Ventre, C., Basios, M., Kanthan, L., Martinez-Rego, D., Wu, F., & Li, L. (2022). CrypΒtocurrency trading: a comprehensive survey. In Financial Innovation (Vol. 8, Issue 1). https://doi.org/10.1186/s40854-021-00321-6Β
Guedes, E. F., Santos, R. P. C., Figueredo, L. H. R., Da Silva, P. A., Dias, R. M. T. S., & Zebende, G. F. (2022). Efficiency and Long-Range Correlation in G-20 Stock Indexes: A Sliding Windows Approach. Fluctuation and Noise Letters. https://doi.org/10.1142/S021947752250033XΒ Β
Hamayel, M. J., & Owda, A. Y. (2021). A Novel Cryptocurrency Price Prediction Model Using GRU, LSTM and bi-LSTM Machine Learning Algorithms. AI, 2(4). https://doi.org/10.3390/ai2040030Β
Jarque, C. M., & Bera, A. K. (1980). Efficient tests for normality, homoscedasticity and seΒrial independence of regression residuals. Economics Letters, 6(3), 255β259. https://doi.org/10.1016/0165-1765(80)90024-5Β
Kakinaka, S., & Umeno, K. (2021). Exploring asymmetric multifractal cross-correlations of priceβvolatility and asymmetric volatility dynamics in cryptocurrency markets. Physica A: Statistical Mechanics and Its Applications, 581. https://doi.org/10.1016/j.physa.2021.126237Β
Kristoufek, L. (2018). On Bitcoin markets (in)efficiency and its evolution. Physica A: Statistical Mechanics and Its Applications, 503, 257β262. https://doi.org/10.1016/j.physa.2018.02.161Β
KrΓΌckeberg, S., & Scholz, P. (2020). Decentralized Efficiency? Arbitrage in Bitcoin Markets. FiΒnancial Analysts Journal, 76(3), 135β152. https://doi.org/10.1080/0015198X.2020.1733902Β
Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., & Shinb, Y. (1992). Testing the null hypotheΒsis of stationary against the alternative of a unit root. Journal of Econometrics, 54(1), 159β 178. https://doi.org/http://dx.doi.org/10.1016/0304-4076(92)90104-YΒ
Perron, P., & Phillips, P. C. B. (1988). Testing for a Unit Root in a Time Series Regression. BiΒometrika, 2(75), 335β346. https://doi.org/10.1080/07350015.1992.10509923Β
Shrestha, K. (2021). Multifractal Detrended Fluctuation Analysis of Return on Bitcoin*. In InΒternational Review of Finance (Vol. 21, Issue 1). https://doi.org/10.1111/irfi.12256Β
Wright, J. H. (2000). Alternative variance-ratio tests using ranks and signs. Journal of Business and Economic Statistics. https://doi.org/10.1080/07350015.2000.10524842Β
Wu, X., Wu, L., & Chen, S. (2022). Long memory and efficiency of Bitcoin during COVID-19. Applied Economics, 54(4). https://doi.org/10.1080/00036846.2021.1962513Β
Zebende, G. F., Santos Dias, R. M. T., & de Aguiar, L. C. (2022). Stock market efficiency: An inΒtraday case of study about the G-20 group. Heliyon, 8(1), e08808. https://doi.org/10.1016/j.heliyon.2022.e08808Β Β