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

Authors

DOI:

https://doi.org/10.18559/ebr.2023.1.4

Keywords:

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

Abstract

This study employs robust martingale difference hypothesis 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 suffered some short periods of weak-form inefficiency. The results obtained validate the adaptive market hypothesis. Additionally, this study allowed the observation of some differences in return predictability between the examined cryptocurrencies. Also some historical trends in weak-form efficiency were identified. The results suggest that the predictability of cryptocurrency returns might have decreased in recent years also no significant relationship between market cap and predictability was observed.

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References

Anagnostidis, P., Varsakelis, C., & Emmanouilides, C. J. (2016). Has the 2008 financial crisis affected stock market efficiency? The case of Eurozone. Physica A: Statistical Mechanics and Its Applications, 447, 116–128. https://doi.org/10.1016/j.physa.2015.12.017
View in Google Scholar DOI: https://doi.org/10.1016/j.physa.2015.12.017

Apopo, N., & Phiri, A. (2021). On the (in)efficiency of cryptocurrencies: Have they taken daily or weekly random walks? Heliyon, 7(4). https://doi.org/10.1016/j.heliyon.2021.e06685
View in Google Scholar DOI: https://doi.org/10.1016/j.heliyon.2021.e06685

Aslam, F., Aziz, S., Nguyen, D. K., Mughal, K. S., & Khan, M. (2020). On the efficiency of foreign exchange markets in times of the COVID-19 pandemic. Technological Forecasting & Social Change, 161, 120261. https://doi.org/10.1016/j.techfore.2020.120261
View in Google Scholar DOI: https://doi.org/10.1016/j.techfore.2020.120261

Bariviera, A. F. (2017). The inefficiency of Bitcoin revisited: A dynamic approach. Economics Letters, 161, 1–4. https://doi.org/10.1016/j.econlet.2017.09.013
View in Google Scholar DOI: https://doi.org/10.1016/j.econlet.2017.09.013

Bundi, N., & Wildi, M. (2019). Bitcoin and market-(in)efficiency: A systematic time series approach. Digital Finance, 1, 47–65. https://doi.org/10.1007/s42521-019-00004-z
View in Google Scholar DOI: https://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.
View in Google Scholar DOI: https://doi.org/10.1515/9781400830213

Caporale, G. M., Gil-Alana, L., Plastun, A. (2018). Persistence in the cryptocurrency market. Research in International Business and Finance, 46(C), 141–148. https://doi.org/10.1016/j.ribaf.2018.01.002
View in Google Scholar DOI: https://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 difference hypothesis. Economics Letters, 110(2), 151–154. https://doi.org/10.1016/j.econlet.2010.11.018
View in Google Scholar DOI: https://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. https://doi.org/10.1016/j.jimonfin.2012.03.003
View in Google Scholar DOI: https://doi.org/10.1016/j.jimonfin.2012.03.003

Cheong, C. W., Nor, A. H. S. M., & Isa, Z. (2007). Asymmetry and long-memory volatility: Some empirical evidence using GARCH. Physica A: Statistical and Theoretical Physics, 373, 651−664. https://doi.org/10.1016/j.physa.2006.05.050
View in Google Scholar DOI: https://doi.org/10.1016/j.physa.2006.05.050

Chu, J., Zhang, Y., & Chan, S. (2019). The adaptive market hypothesis in the high frequency cryptocurrency market. International Review of Financial Analysis, 64(C), 221–231. https://doi.org/10.1016/j.irfa.2019.05.008
View in Google Scholar DOI: https://doi.org/10.1016/j.irfa.2019.05.008

Domínguez, M. A., & Lobato, I. N. (2003). Testing the martingale difference hypothesis. Econometric Reviews, 22(4), 351–377. https://doi.org/10.1081/ETC-120025895
View in Google Scholar DOI: https://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. https://doi.org/10.1016/j.jeconom.2009.03.001
View in Google Scholar DOI: https://doi.org/10.1016/j.jeconom.2009.03.001

Escanciano, J.C., & Velasco, C. (2006). Generalized spectral tests for the martingale difference hypothesis. Journal of Econometrics, 134(1), 151–185.
View in Google Scholar DOI: https://doi.org/10.1016/j.jeconom.2005.06.019

Fama, E. F. (1965). The behaviour of stock market prices. Journal of Business, 38((1), 34–105. https://doi.org/10.1086/294743
View in Google Scholar DOI: https://doi.org/10.1086/294743

Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. Journal of Finance, 25(2), 383–417. https://doi.org/10.2307/2325486
View in Google Scholar DOI: https://doi.org/10.1111/j.1540-6261.1970.tb00518.x

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

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 efficiency and contagion. International Review of Financial Analysis, 35, 140–153. https://doi.org/10.1016/j.irfa.2014.08.002
View in Google Scholar DOI: https://doi.org/10.1016/j.irfa.2014.08.002

Hu, Y., Valera, H. G. A., & Oxley, L. (2019). Market efficiency of the top market-cap cryptocurrencies: Further evidence from a panel framework. Finance Research Letters, 31(C), 138–145. https://doi.org/10.1016/j.frl.2019.04.012
View in Google Scholar DOI: https://doi.org/10.1016/j.frl.2019.04.012

Khuntia, S., & Pattanayak, J. K. (2018). Adaptive market hypothesis and evolving predictability of Bitcoin. Economics Letters, 167, 26–28. https://doi.org/10.1016/j.econlet.2018.03.005
View in Google Scholar DOI: https://doi.org/10.1016/j.econlet.2018.03.005

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). https://doi.org/10.1080/23322039.2020.1719574
View in Google Scholar DOI: https://doi.org/10.1080/23322039.2020.1719574

Kian-Ping, L., Brooks, R. D., & Kim, J. H. (2007). Financial crisis and stock market efficiency: Empirical evidence from Asian countries. International Review of Financial Analysis, 2008(17), 571–591. https://doi.org/10.1016/j.irfa.2007.03.001
View in Google Scholar DOI: https://doi.org/10.1016/j.irfa.2007.03.001

Kim, J. H. (2009). Automatic variance ratio test under conditional heteroskedasticity. Finance Research Letters, 6(3), 179–185. https://doi.org/10.1016/j.frl.2009.04.003
View in Google Scholar DOI: https://doi.org/10.1016/j.frl.2009.04.003

Kim, J. H. (2022). vrtest: Variance ratio tests and other tests for martingale difference hypothesis (R package version 1.1). https://cran.r-project.org/web/packages/vrtest/index.html
View in Google Scholar

Kristoufek, L. (2018). On Bitcoin markets (in)efficiency and its evolution. Physica A: Statistical Mechanics and Its Applications, 503, 257–262. https://doi.org/10.1016/j.physa.2018.02.161
View in Google Scholar DOI: https://doi.org/10.1016/j.physa.2018.02.161

Kristoufek, L., & Vosvrda, M. (2013). Measuring capital market efficiency: Global and local correlations structure. Physica A: Statistical Mechanics and its Applications, 392(1), 184–193. https://doi.org/10.1016/j.physa.2012.08.003
View in Google Scholar DOI: https://doi.org/10.1016/j.physa.2012.08.003

Kristoufek, L., & Vosvrda, M. (2019). Cryptocurrencies market efficiency ranking: Not so straightforward. Physica A: Statistical Mechanics and Its Applications, 531, 2–8. https://doi.org/10.1016/j.physa.2019.04.089
View in Google Scholar DOI: https://doi.org/10.1016/j.physa.2019.04.089

Łęt, B., Sobański, K., Świder, W., & Włosik, K. (2022). Is the cryptocurrency market efficient? Evidence from an analysis of fundamental factors for Bitcoin and Ethereum. International Journal of Management and Economics, 58(4), 351–370. https://doi.org/10.2478/ijme-2022-0030
View in Google Scholar DOI: https://doi.org/10.2478/ijme-2022-0030

Linton, O. (2019). Financial econometrics. Models and methods. Cambridge University Press. https://doi.org/10.1017/9781316819302
View in Google Scholar DOI: https://doi.org/10.1017/9781316819302

Lo, A. W. (2004). The adaptive markets hypothesis: Market efficiency from an evolutionary perspective. Journal of Portfolio Management, 30(5), 15–29. https://doi.org/10.3905/jpm.2004.442611
View in Google Scholar DOI: https://doi.org/10.3905/jpm.2004.442611

Lo, A. W. (2005). Reconciling efficient markets with behavioral finance: The adaptive markets 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. https://doi.org/10.1007/s40822-021-00182-5
View in Google Scholar DOI: https://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 multifractality and long memory in Bitcoin and Ethereum markets: An asymmetric multifractal detrended fluctuation analysis. Finance Research Letters, 31, 19–25. https://doi.org/10.1016/j.frl.2019.03.029
View in Google Scholar DOI: https://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. https://doi.org/10.1016/j.resourpol.2020.101829
View in Google Scholar DOI: https://doi.org/10.1016/j.resourpol.2020.101829

Mensi, W., Tiwari, A. K., & Yoon, S. M. (2017). Global financial crisis and weak-form efficiency of Islamic sectoral stock markets: An MF-DFA analysis. Physica A: Statistical Mechanics and its Applications, 471, 135–146. https://doi.org/10.1016/j.physa.2016.12.034
View in Google Scholar DOI: https://doi.org/10.1016/j.physa.2016.12.034

Nadarajah, S., & Chu, J. (2017). On the inefficiency of Bitcoin. Economics Letters, 150(C), 6–9. https://doi.org/10.1016/j.econlet.2016.10.033
View in Google Scholar DOI: https://doi.org/10.1016/j.econlet.2016.10.033

Noda, A. (2020). On the evolution of cryptocurrency market efficiency. Applied Economic Letters, 28(6), 433–439. https://doi.org/10.1080/13504851.2020.1758617
View in Google Scholar DOI: https://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.
View in Google Scholar DOI: https://doi.org/10.1016/j.bir.2020.10.006

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 alternative currencies. Finance Research Letters, 28(C), 68–73. https://doi.org/10.1016/j.frl.2018.04.002
View in Google Scholar DOI: https://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. https://doi.org/10.1016/j.physa.2015.05.034
View in Google Scholar DOI: https://doi.org/10.1016/j.physa.2015.05.034

Tiwari, A. K., Jana, R. K., Das, D., & Roubaud, D. (2018). Informational efficiency of Bitcoin—An extension. Economics Letters, 163, 106–109. https://doi.org/10.1016/j.econlet.2017.12.006
View in Google Scholar DOI: https://doi.org/10.1016/j.econlet.2017.12.006

Tran, V. L., & Leirvik, T. (2019). A simple but powerful measure of market efficiency. Finance Research Letters, 29(C), 141–151. https://doi.org/10.1016/j.frl.2019.03.004
View in Google Scholar DOI: https://doi.org/10.1016/j.frl.2019.03.004

Tran, V. L., & Leirvik, T. (2020). Efficiency in the markets of crypto-currencies. Finance Research Letters, 35(C). https://doi.org/10.1016/j.frl.2019.101382
View in Google Scholar DOI: https://doi.org/10.1016/j.frl.2019.101382

Urquhart, A. (2016). The inefficiency of Bitcoin. Economics Letters, 148(C), 80–82. https://doi.org/10.1016/j.econlet.2016.09.019
View in Google Scholar DOI: https://doi.org/10.1016/j.econlet.2016.09.019

Wei, W. C. (2018). Liquidity and market efficiency in cryptocurrencies. Economics Letters, 168, 21–24. https://doi.org/10.1016/j.econlet.2018.04.003
View in Google Scholar DOI: https://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 Letters, 25(C), 280–284. https://doi.org/10.1016/j.frl.2017.12.009
View in Google Scholar DOI: https://doi.org/10.1016/j.frl.2017.12.009

Zargar, F. N., & Kumar, D. (2019). Informational inefficiency of Bitcoin: A study based on high-frequency data. Research in International Business and Finance, 47, 344–353. https://doi.org/10.1016/j.ribaf.2018.08.008
View in Google Scholar DOI: https://doi.org/10.1016/j.ribaf.2018.08.008

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. https://doi.org/10.1016/j.physa.2018.07.032
View in Google Scholar DOI: https://doi.org/10.1016/j.physa.2018.07.032

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Published

2023-04-17

Issue

Vol. 9 No. 1 (2023)

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Research article- regular issue

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Copyright (c) 2023 Poznań University of Economics and Business

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How to Cite

Karasiński, J. (2023). The adaptive market hypothesis and the return predictability in the cryptocurrency markets. Economics and Business Review, 9(1), 94-118. https://doi.org/10.18559/ebr.2023.1.4

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