The adaptive market hypothesis and the return predictability in the cryptocurrency markets

• Authors: Jacek Karasiński

Abstracts

EN

This study employs robust martingale diefrence hypothe sis tests to examine return predictability in a broad sample of the 40 most capitalized cryptocurrency markets in the context of the adaptive market hypothesis. The tests were applied to daily returns using the rolling window method in the research period from May 1, 2013 to September 30, 2022. The results of this study suggest that the returns of the majority of the examined cryptocurrencies were unpredictable most of the time. However, a great part of them also suefred some short periods of weak-form ineficien cy. The results obtained validate the adaptive market hy pothesis. Additionally, this study allowed the observation of some diefrences in return predictability between the examined cryptocurrencies. Also some historical trends in weak-form eficiency were identifed. The results suggest that the predictability of cryptocurrency returns might have decreased in recent years also no significant relation ship between market cap and predictability was observed. JEL codes: G14

Keywords

EN

cryptocurrency markets; adaptive market hypothesis; efficient market hypothesis; cryptocurrency return predictability; weak-form efficiency of cryptocurrency markets; martingale difference hypothesis

• Publisher: Uniwersytet Ekonomiczny w Poznaniu
• Journal: Economics and Business Review
• Year: 2022
• Volume: 9
• Issue: 1
• Pages: 94-118

Dates

published

2023

Contributors

author

Jacek Karasiński

• Faculty of Management, University of Warsaw

• https://orcid.org/0000000343265044

References

• Anagnostidis, P., Varsakelis, C., & Emmanouilides, C. J. (2016). Has the 2008 financial crisis aefcted stock market eficiency? The case of Eurozone. Physica A: Statistical Mechanics and Its Applications , 447, 116-128. htps://doi.org/10.1016/j.physa. 2015.12.017
• Apopo, N., & Phiri, A. (2021). On the (in)eficiency of cryptocurrencies: Have they taken daily or weekly random walks? Heliyon, 7(4). htps://doi.org/10.1016/j. heliyon.2021.e06685
• Aslam, F., Aziz, S., Nguyen, D. K., Mughal, K. S., & Khan, M. (2020). On the eficiency of foreign exchange markets in times of the COVID-19 pandemic. Technological Forecasting & Social Change , 161, 120261. htps://doi.org/10.1016/j.techfore. 2020.120261
• Bariviera, A. F. (2017). The ineficiency of Bitcoin revisited: A dynamic approach. Economics Leetrs , 161, 1-4. htps://doi.org/10.1016/j.econlet.2017.09.013
• Bundi, N., & Wildi, M. (2019). Bitcoin and market-(in)eficiency: A systematic time series approach. Digital Finance, 1, 47-65. htps://doi.org/10.1007/s42521-019- 00004-z
• Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). The econometrics of financial markets. Princeton University Press.
• Caporale, G. M., Gil-Alana, L., Plastun, A. (2018). Persistence in the cryptocurrency market. Research in International Business and Finance , 46(C), 141-148. htps:// doi.org/10.1016/j.ribaf.2018.01.002
• Charles, A., Darné, O., & Kim, J. H. (2011). Small sample properties of alternative tests for martingale diefrence hypothesis. Economics Leetrs , 110(2), 151-154. htps://doi.org/10.1016/j.econlet.2010.11.018
• Charles, A., Darné, O., & Kim, J. H. (2012). Exchange-rate return predictability and the adaptive markets hypothesis: Evidence from major foreign exchange rates. Journal of International Money and Finance , 31(6), 1607-1626. htps://doi.org/ 10.1016/j.jimonfin.2012.03.003
• Cheong, C. W., Nor, A. H. S. M., & Isa, Z. (2007). Asymmetry and long-memory volati - lity: Some empirical evidence using GARCH. Physica A: Statistical and Theoretical Physics, 373, 651−664. htps://doi.org/10.1016/j.physa.2006.05.050
• Chu, J., Zhang, Y., & Chan, S. (2019). The adaptive market hypothesis in the high fre - quency cryptocurrency market. International Review of Financial Analysis , 64(C), 221-231. htps://doi.org/10.1016/j.irfa.2019.05.008
• Domínguez, M. A., & Lobato, I. N. (2003). Tesntig the marntigale diefrence hypothesis. Econometric Reviews, 22(4), 351-377. htps://doi.org/10.1081/ETC-120025895
• Escanciano, J. C., & Lobato, I. N. (2009). An automatic Portmanteau test for serial correlation. Journal of Econometrics, 151(2), 140-149. htps://doi.org/10.1016/j. jeconom.2009.03.001
• Escanciano, J.C., & Velasco, C. (2006). Generalized spectral tests for the martingale diefrence hypothesis. Journal of Econometrics, 134(1), 151-185.
• Fama, E. F. (1965). The behaviour of stock market prices. Journal of Business, 38(1), 34-105. htps://doi.org/10.1086/294743
• Fama, E. F. (1970). Eficient capital markets: A review of theory and empirical work. Journal of Finance, 25(2), 383-417. htps://doi.org/10.2307/2325486
• Hawaldar, I. T., Mathukuti, R., & Dsouza, L. J. (2019). Testing the weak form of ef - ifciency of cryptocurrencies: A case study of Bitcoin and Litecoin. International Journal of Scientifc & Technology Research , 8(9), 2301-2305.
• Horta, P., Lagoa, S., & Martins, L. (2014). The impact of the 2008 and 2010 financial crises on the Hurst exponents of international stock markets: Implications for ef - ifciency and contagion. International Review of Financial Analysis , 35, 140-153. htps://doi.org/10.1016/j.irfa.2014.08.002
• Hu, Y., Valera, H. G. A., & Oxley, L. (2019). Market eficiency of the top market-cap cryptocurrencies: Further evidence from a panel framework. Finance Research Leetrs , 31(C), 138-145. htps://doi.org/10.1016/j.frl.2019.04.012
• Khuntia, S., & Paatnayak, J. K. (2018). Adaptive market hypothesis and evolving pre - dictability of Bitcoin. Economics Leetrs , 167, 26-28. htps://doi.org/10.1016/j. econlet.2018.03.005
• Khursheed, A., Naeem, M., Ahmed, S., & Mustafa, F. (2020). Adaptive market hy - pothesis: An empirical analysis of time-varying market eficiency of cryptocur - rencies. Cogent Economics and Finance, 8(1). htps://doi.org/10.1080/23322039. 2020.1719574
• Kian-Ping, L., Brooks, R. D., & Kim, J. H. (2007). Financial crisis and stock market efi - ciency: Empirical evidence from Asian countries. International Review of Financial Analysis, 2008(17), 571-591. htps://doi.org/10.1016/j.irfa.2007.03.001
• Kim, J. H. (2009). Automatic variance ratio test under conditional heteroskedasticity. Finance Research Leetrs , 6(3), 179-185. hpts://doi.org/10.1016/j.frl.2009.04.003
• Kim, J. H. (2022). vrtest: Variance ratio tests and other tests for martingale diefr - ence hypothesis (R package version 1.1). htps://cran.r-project.org/web/pack - ages/vrtest/index.html
• Kristoufek, L. (2018). On Bitcoin markets (in)eficiency and its evolution. Physica A: Stastictial Mechanics and Its Applicaotins , 503, 257-262. hpts://doi.org/10.1016/j. physa.2018.02.161
• Kristoufek, L., & Vosvrda, M. (2013). Measuring capital market eficiency: Global and local correlations structure. Physica A: Statistical Mechanics and its Applications , 392(1), 184-193. htps://doi.org/10.1016/j.physa.2012.08.003
• Kristoufek, L., & Vosvrda, M. (2019). Cryptocurrencies market eficiency ranking: Not so straighotfrward. Physica A: Statistical Mechanics and Its Applications , 531, 2-8. htps://doi.org/10.1016/j.physa.2019.04.089
• Łęt, B., Sobański, K., Świder, W., & Włosik, K. (2022). Is the cryptocurrency market eficient? Evidence from an analysis of fundamental factors for Bitcoin and Ethereum. International Journal of Management and Economics , 58(4), 351-370. htps://doi.org/10.2478/ijme-2022-0030
• Linton, O. (2019). Financial econometrics. Models and methods. Cambridge University Press. htps://doi.org/10.1017/9781316819302
• Lo, A. W. (2004). The adaptive markets hypothesis: Market eficiency from an evo - lutionary perspective. Journal of Porotflio Management , 30(5), 15-29. htps:// doi.org/10.3905/jpm.2004.442611
• Lo, A. W. (2005). Reconciling eficient markets with behavioral finance: The adaptive markets hypothesis. Journal of Investment Consulting , 7(2), 21-44.
• López-Martín, C., Muela, S. B., & Arguedas, R. (2021). Eficiency in cryptocurrency markets: New evidence. Eurasian Economic Review, 11(3), 403-431. htps://doi. org/10.1007/s40822-021-00182-5
• Mensi, W., Lee, Y. J., Al-Yahyaee, K. H., Sensoy, A., & Yoon, S. M. (2019). Intraday downward/upward mulftiractality and long memory in Bitcoin and Ethereum mar - kets: An asymmetric mulftiractal detrended uflctuaotin analysis. Finance Research Leetrs , 31, 19-25. htps://doi.org/10.1016/j.frl.2019.03.029
• 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. htps://doi.org/10.1016/j.resourpol.2020.101829
• Mensi, W., Tiwari, A. K., & Yoon, S. M. (2017). Global nfiancial crisis and weak-form ef - cifiency of Islamic sectoral stock markets: An MF-DFA analysis. Physica A: Stastictial Mechanics and its Applications , 471, 135-146. htps://doi.org/10.1016/j.physa. 2016.12.034
• Nadarajah, S., & Chu, J. (2017). On the ineficiency of Bitcoin. Economics Leetrs , 150(C), 6-9. htps://doi.org/10.1016/j.econlet.2016.10.033
• Noda, A. (2020). On the evolution of cryptocurrency market eficiency. Applied Economic Leters , 28(6), 433-439. htps://doi.org/10.1080/13504851.2020. 1758617
• Palamalai, S., Kumar, K. K., & Maity, B. (2021). Testing the random walk hypothesis for leading cryptocurrencies. Borsa Istanbul Review, 21(3), 256-268.
• Samuelson, P. A. (1965). Proof that properly anticipated prices fluctuate randomly. Industrial Management Review, 6, 41-49.
• Sensoy, A. (2019). The ineficiency of Bitcoin revisited: A high-frequency analysis with alternative currencies. Finance Research Leetrs , 28(C), 68-73. htps://doi. org/10.1016/j.frl.2018.04.002
• Sensoy, A., & Tabak, B. M. (2015). Time-varying long term memory in the European Union stock markets. Physica A: Statistical Mechanics and Its Applications , 436, 147-158. htps://doi.org/10.1016/j.physa.2015.05.034
• Tiwari, A. K., Jana, R. K., Das, D., & Roubaud, D. (2018). Informational eficien - cy of Bitcoin-An extension. Economics Leters , 163, 106-109. htps://doi. org/10.1016/j.econlet.2017.12.006
• Tran, V. L., & Leirvik, T. (2019). A simple but powerful measure of market efi - ciency. Finance Research Leters , 29(C), 141-151. htps://doi.org/10.1016/j. frl.2019.03.004
• Tran, V. L., & Leirvik, T. (2020). Eficiency in the markets of crypto-currencies. Finance Research Leetrs , 35(C). htps://doi.org/10.1016/j.frl.2019.101382
• Urquhart, A. (2016). The ineficiency of Bitcoin. Economics Leetrs , 148(C), 80-82. htps://doi.org/10.1016/j.econlet.2016.09.019
• Wei, W. C. (2018). Liquidity and market eficiency in cryptocurrencies. Economics Leetrs , 168, 21-24. htps://doi.org/10.1016/j.econlet.2018.04.003
• Yonghong, J., He, N., & Weihua, R. (2018). Time-varying long-term memory in Bitcoin market. Finance Research Leetrs , 25(C), 280-284. htps://doi.org/10.1016/j. frl.2017.12.009
• Zargar, F. N., & Kumar, D. (2019). Informational ineficiency of Bitcoin: A study based on high-frequency data. Research in International Business and Finance , 47, 344- 353. htps://doi.org/10.1016/j.ribaf.2018.08.008
• Zhang, W., Wang, P., Li, X., & Shen, D. (2018). The ineficiency of cryptocurrency and its cross-correlation with Dow Jones Industrial Average. Physica A: Statistical Mechanics and Its Applications , 510, 658-670. htps://doi.org/10.1016/j.physa. 2018.07.032

Identifiers

DOI

10.18559/ebr.2023.1.4

Biblioteka Nauki

2192168