References
- 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 - Aslam, F., Memon, B. A., Hunjra, A. I., & Bouri, E. (2023). The Dynamics of Market Efficiency of Major Cryptocurrencies. Global Finance Journal, 58, 100899.
https://doi.org/10.1016/j.gfj.2023.100899 - Aslan, A., & Sensoy, A. (2020). Intraday Efficiency-Frequency Nexus in the Cryptocurrency Markets. Finance Research Letters, 35, 101298.
https://doi.org/10.1016/j.frl.2019.09.013 - 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 - Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). The Econometrics of Financial Markets. Princeton: 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.
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 - 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 - Choi, I. (1999). Testing the Random Walk Hypothesis for Real Exchange Rates. Journal of Applied Econometrics, 14(3), 293–308.
https://doi.org/10.1002/(SICI)1099-1255(199905/06)14:3<;293::AID-JAE503>3.0.CO;2-5 - 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 - 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 - Fama, E. F. (1965). The Behaviour of Stock Market Prices. Journal of Business, 38(1), 34–105.
https://doi.org/10.1086/294743 - Fieberg, C., Liedtke, G., Poddig, T., Walker, T., & Zaremba, A. (2024). A Trend Factor for the Cross-Section of Cryptocurrency Returns. Journal of Financial and Quantitative Analysis.
https://dx.doi.org/10.2139/ssrn.4601972 - 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.
- 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 - Kang, H.-J., Lee, S.-G., & Park, S.-Y. (2022) Information Efficiency in the Cryptocurrency Market: The Efficient-Market Hypothesis. Journal of Computer Information Systems, 62(3), 622–631.
https://doi.org/10.1080/08874417.2021.1872046 - 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 - 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 - 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 - Kristjanpoller, W., Nekhili, R., & Bouri, E. (2024). Ethereum Futures and the Efficiency of Cryptocurrency Spot Markets. Physica A: Statistical Mechanics and its Applications, 654, 130161.
https://doi.org/10.1016/j.physa.2024.130161 - Linton, O. (2019). Financial Econometrics: Models and Methods. Cambridge: Cambridge University Press.
https://doi.org/10.1017/9781316819302 - Lo, A. W. (2004). The Adaptive Markets Hypothesis. Journal of Portfolio Management, 30(5), 15–29.
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.
- 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 - Meng, K., & Khan, K. (2023). Is Cryptocurrency Efficient? A High-Frequency Asymmetric Multifractality Analysis. Computational Economics, 63, 2225–2246.
https://doi.org/10.1007/s10614-023-10402-6 - 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 - Mokni, K., Montasser, G. E., Ajmi, A. N., & Bouri, E. (2024). On the Efficiency and its Drivers in the Cryptocurrency Market: The Case of Bitcoin and Ethereum. Financial Innovation, 10, 39.
https://doi.org/10.1186/s40854-023-00566-3 - 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 - 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 - Okorie, D. I., Bouri, E., & Mazur, M. (2024). NFTs versus Conventional Cryptocurrencies: A Comparative Analysis of Market Efficiency Around COVID-19 and the Russia-Ukraine Conflict. The Quarterly Review of Economics and Finance, 95, 126–151.
https://doi.org/10.1016/j.qref.2024.03.001 - Palamalai, S., Kumar, K. K., & Maity, B. (2021). Testing the Random Walk Hypothesis for Leading Cryptocurrencies. Borsa Istanbul Review, 21(3), 256–268.
- Polyzos, E., Rubbaniy, G., & Mazur, M. (2024). Efficient Market Hypothesis on the Blockchain: A Social-Media-Based Index for Cryptocurrency Efficiency. The Financial Review, 59(3), 807–829.
https://doi.org/10.1111/fire.12387 - Samuelson, P. A. (1965). Proof That Properly Anticipated Prices Fluctuate Randomly. Industrial Management Review, 6, 41–49.
- 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 - 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 - 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 - Urquhart. (2016). The Inefficiency of Bitcoin. Economics Letters, 148(C), 80–82.
https://doi.org/10.1016/j.econlet.2016.09.019 - Verma, R., Sharma, D., & Sam, S. (2022). Testing of Random Walk Hypothesis in the Cryptocurrency Market. FIIB Business Review, 1–9.
https://doi.org/10.1177/23197145221101238 - Yi, E., Yang, B., Jeong, M., Sohn, S., & Ahn, K. (2023). Market Efficiency of Cryptocurrency: Evidence From the Bitcoin Market. Scientific Reports, 13, 4789.
https://doi.org/10.1038/s41598-023-31618-4 - 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 - 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 - 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 - Zhang, Y., Chan, S., Chu, J., & Shih, S. (2023) The Adaptive Market Hypothesis of Decentralized Finance (DeFi), Applied Economics, 55(42), 4975–4989.
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