The weak-form efficiency of cryptocurrencies
DOI:
https://doi.org/10.18559/ref.2023.1.198Keywords:
efficient market hypothesis, adaptive market hypothesis, weak-form efficiency of cryptocurrencies, martingale difference hypothesis, cryptocurrency marketsAbstract
This study aimed to examine the weak-form efficiency of some of the most capitalised cryptocurrencies. The sample consisted of 24 cryptocurrencies selected out of 30 cryptocurrencies with the highest market capitalisation as of October 19, 2022. Stablecoins were not considered. The study covered the period from January 1, 2018 to August 31, 2022. The results of robust martingale difference hypothesis tests suggest that the examined cryptocurrencies were efficient most of the time. However, their efficiency turned out to be time-varying, which validates the adaptive market hypothesis. No evidence was found for the impact of the coronavirus outbreak and the Russian invasion of Ukraine on the weak-form efficiency of the examined cryptocurrencies. The differences in efficiency between the most efficient cryptocurrencies and the least efficient ones were noticeable, but not large. The results also allowed to observe some slight differences in efficiency between the cryptocurrencies with the largest market cap and cryptocurrencies with the lowest market cap. However, the differences between the two groups were too small to draw any far-reaching conclusions about a positive relationship between the market cap and efficiency. The obtained results also did not allow us to detect any trends in efficiency.
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References
Alvarez-Ramirez, J., & Rodriguez, E. (2021). A singular value decomposition approach for testing the efficiency of Bitcoin and Ethereum markets. Economics Letters, 206, 1–5. DOI: https://doi.org/10.1016/j.econlet.2021.109997
View in Google Scholar
Apopo, N., & Phiri, A. (2021). On the (in)efficiency of cryptocurrencies: have they taken daily or weekly random walks? Heliyon, 7(4), 1–10. DOI: https://doi.org/10.1016/j.heliyon.2021.e06685
View in Google Scholar
Arouxet, M. B., Bariviera, A. F., Pastor, V. E., & Vampa, V. (2022). COVID-19 impact on cryp¬tocurrencies: Evidence from a wavelet-based Hurst exponent. Physica A, 596, 1–12. DOI: https://doi.org/10.1016/j.physa.2022.127170
View in Google Scholar
Assaf, A., Bhandari, A., Charif, H., & Demir, E. (2022a). Multivariate long memory struc¬ture in the cryptocurrency market: The impact of COVID-19. International Review of Financial Analysis, 82, 1–17. DOI: https://doi.org/10.1016/j.irfa.2022.102132
View in Google Scholar
Assaf, A., Mokni, K., Yousaf, I., & Bhandari, A. (2022b). Long memory in the high frequency cryptocurrency markets using fractal connectivity analysis: The impact of COVID-19. Research in International Business and Finance, 64, 1–19. DOI: https://doi.org/10.1016/j.ribaf.2022.101821
View in Google Scholar
Aslam, F., Slim, S., Osman, M., & Tabche, I. (2022). The footprints of Russia–Ukraine war on the intraday (in)efficiency of energy markets: a multifractal analysis. Journal of Risk Finance, 24(1), 89–104. DOI: https://doi.org/10.1108/JRF-06-2022-0152
View in Google Scholar
Bundi, N., & Wildi, M. (2019). Bitcoin and market (in)efficiency: a systematic time series approach. Digital Finance, 1, 47–65. DOI: https://doi.org/10.1007/s42521-019-00004-z
View in Google Scholar
Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). The Econometrics of Financial Markets. Princeton. Princeton University Press. DOI: https://doi.org/10.1515/9781400830213
View in Google Scholar
Charles, A., Darné, O., & Kim, J. H. (2011). Small sample properties of alternative tests for martingale difference hypothesis. Economics Letters, 110(2), 151–154. DOI: https://doi.org/10.1016/j.econlet.2010.11.018
View in Google Scholar
Chu, J., Zhang, Y., & Chan, S. (2019). The adaptive market hypothesis in the high frequen¬cy cryptocurrency market. International Review of Financial Analysis, 64(C), 221–231. DOI: https://doi.org/10.1016/j.irfa.2019.05.008
View in Google Scholar
Escanciano, J. C., & Lobato, I. N. (2009). An automatic Portmanteau test for serial correlation. Journal of Econometrics, 151(2), 140–149. DOI: https://doi.org/10.1016/j.jeconom.2009.03.001
View in Google Scholar
Fama, E. F. (1965). The behaviour of stock market prices. Journal of Business, 38(1), 34–105. DOI: https://doi.org/10.1086/294743
View in Google Scholar
Fama, E. F. (1970). Efficient capital markets: a review of theory and empirical work. Journal of Finance, 25(2), 383–417. DOI: https://doi.org/10.1111/j.1540-6261.1970.tb00518.x
View in Google Scholar
Gaio, L. E., Stefanelli, N. O., Pimenta Júnior, T., Bonacim, C. A. G., & Gatsios, R. C. (2022). The impact of the Russia–Ukraine conflict on market efficiency: Evidence for the de¬veloped stock market. Finance Research Letters, 50, 1–7. DOI: https://doi.org/10.1016/j.frl.2022.103302
View in Google Scholar
Hawaldar, I. T., Mathukutti, R., & Dsouza, L. J. (2019). Testing the weak form of efficiency of cryptocurrencies: A case study of Bitcoin and Litecoin. International Journal of Scientific & Technology Research, 8(9), 2301–2305.
View in Google Scholar
Hu, Y., Valera, H. G. A., & Oxley, L. (2019). Market efficiency of the top market-cap crypto¬currencies: Further evidence from a panel framework. Finance Research Letters, 31(C), 138–145. DOI: https://doi.org/10.1016/j.frl.2019.04.012
View in Google Scholar
Kakinaka, S., & Umeno, K. (2022). Cryptocurrency market efficiency in short- and long-term horizons during COVID-19: An asymmetric multifractal analysis approach. Finance Research Letters, 46, 1–10. DOI: https://doi.org/10.1016/j.frl.2021.102319
View in Google Scholar
Khuntia, S., & Pattanayak, J. K. (2018). Adaptive market hypothesis and evolving pre¬dictability of Bitcoin. Economics Letters, 167, 26–28. DOI: https://doi.org/10.1016/j.econlet.2018.03.005
View in Google Scholar
Khursheed, A., Naeem, M., Ahmed, S., & Mustafa, F. (2020). Adaptive market hypothesis: An empirical analysis of time-varying market efficiency of cryptocurrencies. Cogent Economics and Finance, 8(1), 1–15. DOI: https://doi.org/10.1080/23322039.2020.1719574
View in Google Scholar
Kim, J. H. (2009). Automatic variance ratio test under conditional heteroskedasticity. Finance Research Letters, 6(3), 179–185. DOI: https://doi.org/10.1016/j.frl.2009.04.003
View in Google Scholar
Linton, O. (2019). Financial econometrics. Models and methods. Cambridge University Press. DOI: https://doi.org/10.1017/9781316819302
View in Google Scholar
Lo, A. W. (2004). The adaptive markets hypothesis: Market efficiency from an evolutionary perspective. Journal of Portfolio Management, 30(5), 15–29. DOI: https://doi.org/10.3905/jpm.2004.442611
View in Google Scholar
Lo, A. W. (2005). Reconciling efficient markets with behavioral finance: The adaptive mar¬kets hypothesis. Journal of Investment Consulting, 7(2), 21–44.
View in Google Scholar
López Martín, C., Muela, S. B., & Arguedas, R. (2021). Efficiency in cryptocurrency markets: New evidence. Eurasian Economic Review, 11(3), 403–431. DOI: https://doi.org/10.1007/s40822-021-00182-5
View in Google Scholar
Mandaci, P. E., & Cagli, E. C. (2022). Herding intensity and volatility in cryptocurrency mar¬kets during the COVID-19. Finance Research Letters, 46, 1–7. DOI: https://doi.org/10.1016/j.frl.2021.102382
View in Google Scholar
Mensi, W., Tiwari, A. K., & Al-Yahyaee, K. H. (2019). An analysis of the weak form efficiency, multifractality and long memory of global, regional and European stock markets. The Quarterly Review of Economics and Finance, 72, 168–177. DOI: https://doi.org/10.1016/j.qref.2018.12.001
View in Google Scholar
Nadarajah, S., & Chu, J. (2017). On the inefficiency of Bitcoin. Economics Letters, 150(C), 6–9. DOI: https://doi.org/10.1016/j.econlet.2016.10.033
View in Google Scholar
Naeem, M. A., Bouri, E., Peng, Z., Shahzad, S. J. H., & Vo, X. V. (2021). Asymmetric efficiency of cryptocurrencies during COVID19. Physica A, 565, 1–12. DOI: https://doi.org/10.1016/j.physa.2020.125562
View in Google Scholar
Noda, A. (2021). On the evolution of cryptocurrency market efficiency. Applied Economic Letters, 28(6), 433–439. DOI: https://doi.org/10.1080/13504851.2020.1758617
View in Google Scholar
Palamalai, S., Kumar, K. K., & Maity, B. (2021). Testing the random walk hypothesis for lead¬ing cryptocurrencies. Borsa Istanbul Review, 21(3), 256–268. DOI: https://doi.org/10.1016/j.bir.2020.10.006
View in Google Scholar
Samuelson, P. A. (1965). Proof that properly anticipated prices fluctuate randomly. Industrial Management Review, 6, 41–49.
View in Google Scholar
Sensoy, A. (2019). The inefficiency of Bitcoin revisited: A high-frequency analysis with al¬ternative currencies. Finance Research Letters, 28(C), 68–73. DOI: https://doi.org/10.1016/j.frl.2018.04.002
View in Google Scholar
Tran, V. L., & Leirvik, T. (2019). A simple but powerful measure of market efficiency. Finance Research Letters, 29(C), 141–151. DOI: https://doi.org/10.1016/j.frl.2019.03.004
View in Google Scholar
Tran, V. L., & Leirvik, T. (2020). Efficiency in the markets of crypto-currencies. Finance Research Letters, 35(C). DOI: https://doi.org/10.1016/j.frl.2019.101382
View in Google Scholar
Urquhart, A. (2016). The inefficiency of Bitcoin. Economics Letters, 148(C), 80–82. https://doi.org/10.1016/j.econlet.2016.09.019 DOI: https://doi.org/10.1016/j.econlet.2016.09.019
View in Google Scholar
Usman, N., & Nduka, K. N. (2022). Announcement effect of COVID-19 on cryptocurrencies. Asian Economics Letters, 3(3). DOI: https://doi.org/10.46557/001c.29953
View in Google Scholar
Yonghong, J., He, N., & Weihua, R. (2018). Time-varying long-term memory in Bitcoin mar¬ket. Finance Research Letters, 25(C), 280–284. DOI: https://doi.org/10.1016/j.frl.2017.12.009
View in Google Scholar
Zhang, W., Wang, P., Li, X., & Shen, D. (2018). The inefficiency of cryptocurrency and its cross-correlation with Dow Jones Industrial Average. Physica A: Statistical Mechanics and Its Applications, 510, 658–670. DOI: https://doi.org/10.1016/j.physa.2018.07.032
View in Google Scholar
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