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

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 Search 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). https://doi.org/10.1016/j.heliyon.2021.e06685 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). The econometrics of financial markets. Princeton University Press. Search in Google Scholar

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 Search 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. https://doi.org/10.1016/j.econlet.2010.11.018 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

Escanciano, J.C., & Velasco, C. (2006). Generalized spectral tests for the martingale difference hypothesis. Journal of Econometrics, 134(1), 151–185. Search in Google Scholar

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

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 Search 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. Search 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 Search in Google Scholar

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 Search in Google Scholar

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

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 Search in Google Scholar

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 Search in Google Scholar

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 Search 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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

Linton, O. (2019). Financial econometrics. Models and methods. Cambridge University Press. https://doi.org/10.1017/9781316819302 Search 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. https://doi.org/10.3905/jpm.2004.442611 Search in Google Scholar

Lo, A. W. (2005). Reconciling efficient markets with behavioral finance: The adaptive markets hypothesis. Journal of Investment Consulting, 7(2), 21–44. Search 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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

Palamalai, S., Kumar, K. K., & Maity, B. (2021). Testing the random walk hypothesis for leading cryptocurrencies. Borsa Istanbul Review, 21(3), 256–268. Search in Google Scholar

Samuelson, P. A. (1965). Proof that properly anticipated prices fluctuate randomly. Industrial Management Review, 6, 41–49. Search 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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search 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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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