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The adaptive market hypothesis in the high frequency cryptocurrency market

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  • Chu, Jeffrey
  • Zhang, Yuanyuan
  • Chan, Stephen

Abstract

This paper investigates the adaptive market hypothesis (AMH) with respect to the high frequency markets of the two largest cryptocurrencies — Bitcoin and Ethereum, versus the Euro and US Dollar. Our findings are consistent with the AMH and show that the efficiency of the markets varies over time. We also discuss possible news and events which coincide with significant changes in the market efficiency. Furthermore, we analyse the effect of the sentiment of these news and other factors (events) on the market efficiency in the high frequency setting, and provide a simple event analysis to investigate whether specific factors affect the market efficiency/inefficiency. The results show that the sentiment and types of news and events may not be significant factor in determining the efficiency of cryptocurrency markets.

Suggested Citation

  • Chu, Jeffrey & Zhang, Yuanyuan & Chan, Stephen, 2019. "The adaptive market hypothesis in the high frequency cryptocurrency market," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 221-231.
    • Handle: RePEc:eee:finana:v:64:y:2019:i:c:p:221-231
    DOI: 10.1016/j.irfa.2019.05.008
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    Cited by:

    1. OlaOluwa S. Yaya & Ahamuefula E. Ogbonna & Robert Mudida & Nuruddeen Abu, 2021. "Market efficiency and volatility persistence of cryptocurrency during pre‐ and post‐crash periods of Bitcoin: Evidence based on fractional integration," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 1318-1335, January.
    2. Mircea Constantin Șcheau & Simona Liliana Crăciunescu & Iulia Brici & Monica Violeta Achim, 2020. "A Cryptocurrency Spectrum Short Analysis," JRFM, MDPI, vol. 13(8), pages 1-16, August.
    3. Naeem, Muhammad Abubakr & Bouri, Elie & Peng, Zhe & Shahzad, Syed Jawad Hussain & Vo, Xuan Vinh, 2021. "Asymmetric efficiency of cryptocurrencies during COVID19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    4. Wang, Jinghua & Ngene, Geoffrey M., 2020. "Does Bitcoin still own the dominant power? An intraday analysis," International Review of Financial Analysis, Elsevier, vol. 71(C).
    5. Tran, Vu Le & Leirvik, Thomas, 2020. "Efficiency in the markets of crypto-currencies," Finance Research Letters, Elsevier, vol. 35(C).
    6. Ferreira, Joaquim & Morais, Flávio, 2023. "Predict or to be predicted? A transfer entropy view between adaptive green markets, structural shocks and sentiment index," Finance Research Letters, Elsevier, vol. 56(C).
    7. Alvarez-Ramirez, Jose & Rodriguez, Eduardo, 2021. "A singular value decomposition approach for testing the efficiency of Bitcoin and Ethereum markets," Economics Letters, Elsevier, vol. 206(C).
    8. José A. Roldán-Casas & Mª B. García-Moreno García, 2022. "A procedure for testing the hypothesis of weak efficiency in financial markets: a Monte Carlo simulation," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(5), pages 1289-1327, December.
    9. Chan, Stephen & Chu, Jeffrey & Zhang, Yuanyuan & Nadarajah, Saralees, 2022. "An extreme value analysis of the tail relationships between returns and volumes for high frequency cryptocurrencies," Research in International Business and Finance, Elsevier, vol. 59(C).
    10. Katsiampa, Paraskevi & Yarovaya, Larisa & Zięba, Damian, 2022. "High-frequency connectedness between Bitcoin and other top-traded crypto assets during the COVID-19 crisis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 79(C).
    11. Marten Risius & Christoph F. Breidbach & Mathieu Chanson & Ruben Krannichfeldt & Felix Wortmann, 2023. "On the performance of blockchain-based token offerings," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-19, December.
    12. Andrew Phiri, 2022. "Can wavelets produce a clearer picture of weak-form market efficiency in Bitcoin?," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 12(3), pages 373-386, September.
    13. Carmen López-Martín & Sonia Benito Muela & Raquel Arguedas, 2021. "Efficiency in cryptocurrency markets: new evidence," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 11(3), pages 403-431, September.
    14. Mensi, Walid & Sensoy, Ahmet & Vo, Xuan Vinh & Kang, Sang Hoon, 2022. "Pricing efficiency and asymmetric multifractality of major asset classes before and during COVID-19 crisis," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    15. Ao Shu & Feiyang Cheng & Jianlei Han & Zini Liang & Zheyao Pan, 2023. "Arbitrage across different Bitcoin exchange venues: Perspectives from investor base and market related events," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(5), pages 5183-5210, December.
    16. Farman Ullah Khan & Faridoon Khan & Parvez Ahmed Shaikh, 2023. "Forecasting returns volatility of cryptocurrency by applying various deep learning algorithms," Future Business Journal, Springer, vol. 9(1), pages 1-11, December.
    17. Mariana Chambino & Rui Manuel Teixeira Dias & Nicole Rebolo Horta, 2023. "Asymmetric efficiency of cryptocurrencies during the 2020 and 2022 events," Economic Analysis Letters, Anser Press, vol. 2(2), pages 23-33, May.
    18. Yuanyuan Zhang & Stephen Chan & Jeffrey Chu & Hana Sulieman, 2020. "On the Market Efficiency and Liquidity of High-Frequency Cryptocurrencies in a Bull and Bear Market," JRFM, MDPI, vol. 13(1), pages 1-14, January.
    19. Sakemoto, Ryuta, 2021. "Economic Evaluation of Cryptocurrency Investment," MPRA Paper 108283, University Library of Munich, Germany.
    20. Alexander Musaev & Andrey Makshanov & Dmitry Grigoriev, 2022. "Evolutionary Optimization of Control Strategies for Non-Stationary Immersion Environments," Mathematics, MDPI, vol. 10(11), pages 1-17, May.
    21. Manahov, Viktor & Urquhart, Andrew, 2021. "The efficiency of Bitcoin: A strongly typed genetic programming approach to smart electronic Bitcoin markets," International Review of Financial Analysis, Elsevier, vol. 73(C).
    22. Donglian Ma & Hisashi Tanizaki, 2022. "Intraday patterns of price clustering in Bitcoin," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-25, December.
    23. Abakah, Emmanuel Joel Aikins & Gil-Alana, Luis Alberiko & Madigu, Godfrey & Romero-Rojo, Fatima, 2020. "Volatility persistence in cryptocurrency markets under structural breaks," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 680-691.
    24. Hashem A. AlNemer & Besma Hkiri & Muhammed Asif Khan, 2021. "Time-Varying Nexus between Investor Sentiment and Cryptocurrency Market: New Insights from a Wavelet Coherence Framework," JRFM, MDPI, vol. 14(6), pages 1-19, June.
    25. Leirvik, Thomas, 2022. "Cryptocurrency returns and the volatility of liquidity," Finance Research Letters, Elsevier, vol. 44(C).

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    More about this item

    Keywords

    Bitcoin; Ethereum; Martingale difference hypothesis; Adaptive market hypothesis; Efficient market hypothesis;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General

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