The Predictability of High-Frequency Returns in the Cryptocurrency Markets and the Adaptive Market Hypothesis

• Authors: Jacek Karasiński

Abstracts

EN

The objective of this study was to examine the level and behaviour of the weak-form efficiency of the 16 most capitalised cryptocurrencies using intraday data. The study employed martingale difference hypothesis tests utilising the rolling window method. The predictability of high frequency returns varied over time. For most of the time, the cryptocurrencies were unpredictable. Nevertheless, their weak-form efficiency appeared to decrease along with an increase in frequency. In general, most cryptocurrencies were marked by high levels of unpredictability. However, there were some significant differences between the most and least efficient ones. To exploit market inefficiencies, investors should focus on higher frequencies. Higher frequencies should also be a concern to regulators when it comes to ensuring market efficiency.

Keywords

EN

cryptocurrency markets; efficient market hypothesis [EMH]; predictability of returns; intraday returns; adaptive market hypothesis

• Publisher: Uniwersytet Warszawski. Wydawnictwo Naukowe Wydziału Zarządzania
• Journal: Central European Economic Journal
• Year: 2025
• Volume: 12
• Issue: 59
• Pages: 34-48

Dates

published

2025

Contributors

author

Jacek Karasiński

• University of Warsaw, Faculty of Management

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. https://doi.org/10.1080/00036846.2022.2133895

Identifiers

DOI

10.2478/ceej-2025-0003

Biblioteka Nauki

57257410