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Rise of the machines? Intraday high-frequency trading patterns of cryptocurrencies

Author

Listed:
  • Alla A. Petukhina
  • Raphael C. G. Reule
  • Wolfgang Karl Härdle

Abstract

This research analyses high-frequency data of the cryptocurrency market in regards to intraday trading patterns related to algorithmic trading and its impact on the European cryptocurrency market. We study trading quantitatives such as returns, traded volumes, volatility periodicity, and provide summary statistics of return correlations to CRIX (CRyptocurrency IndeX), as well as respective overall high-frequency based market statistics with respect to temporal aspects. Our results provide mandatory insight into a market, where the grand scale employment of automated trading algorithms and the extremely rapid execution of trades might seem to be a standard based on media reports. Our findings on intraday momentum of trading patterns lead to a new quantitative view on approaching the predictability of economic value in this new digital market.

Suggested Citation

  • Alla A. Petukhina & Raphael C. G. Reule & Wolfgang Karl Härdle, 2021. "Rise of the machines? Intraday high-frequency trading patterns of cryptocurrencies," The European Journal of Finance, Taylor & Francis Journals, vol. 27(1-2), pages 8-30, January.
    • Handle: RePEc:taf:eurjfi:v:27:y:2021:i:1-2:p:8-30
    DOI: 10.1080/1351847X.2020.1789684
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    Cited by:

    1. Guo, Li & Sang, Bo & Tu, Jun & Wang, Yu, 2024. "Cross-cryptocurrency return predictability," Journal of Economic Dynamics and Control, Elsevier, vol. 163(C).
    2. M. Eren Akbiyik & Mert Erkul & Killian Kaempf & Vaiva Vasiliauskaite & Nino Antulov-Fantulin, 2021. "Ask "Who", Not "What": Bitcoin Volatility Forecasting with Twitter Data," Papers 2110.14317, arXiv.org, revised Dec 2022.
    3. Wen, Zhuzhu & Bouri, Elie & Xu, Yahua & Zhao, Yang, 2022. "Intraday return predictability in the cryptocurrency markets: Momentum, reversal, or both," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    4. 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.
    5. Jahanshahloo, Hossein & Corbet, Shaen & Oxley, Les, 2022. "Seeking sigma: Time-of-the-day effects on the Bitcoin network," Finance Research Letters, Elsevier, vol. 49(C).
    6. Konstantin Häusler & Hongyu Xia, 2022. "Indices on cryptocurrencies: an evaluation," Digital Finance, Springer, vol. 4(2), pages 149-167, September.
    7. Bennett, Donyetta & Mekelburg, Erik & Williams, T.H., 2023. "BeFi meets DeFi: A behavioral finance approach to decentralized finance asset pricing," Research in International Business and Finance, Elsevier, vol. 65(C).
    8. Zinovyev, Elizaveta & Reule, Raphael C. G. & Härdle, Wolfgang, 2021. "Understanding Smart Contracts: Hype or hope?," IRTG 1792 Discussion Papers 2021-004, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    9. Ali Mehrban & Pegah Ahadian, 2024. "An adaptive network-based approach for advanced forecasting of cryptocurrency values," Papers 2401.05441, arXiv.org, revised Feb 2024.
    10. Ge, Hengshun & Yang, Haijun & Doukas, John A., 2024. "The optimal strategies of competitive high-frequency traders and effects on market liquidity," International Review of Economics & Finance, Elsevier, vol. 91(C), pages 653-679.
    11. Colombo, Jefferson A. & Cruz, Fernando I. L. & Paese, Luis H. Z. & Cortes, Renan X., 2021. "The diversification benefits of cryptocurrencies in multi-asset portfolios: cross-country evidence," Textos para discussão 542, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    12. Almeida, José & Gonçalves, Tiago Cruz, 2023. "A systematic literature review of investor behavior in the cryptocurrency markets," Journal of Behavioral and Experimental Finance, Elsevier, vol. 37(C).
    13. Wang, Yifu & Lu, Wanbo & Lin, Min-Bin & Ren, Rui & Härdle, Wolfgang Karl, 2024. "Cross-exchange crypto risk: A high-frequency dynamic network perspective," International Review of Financial Analysis, Elsevier, vol. 94(C).
    14. Danial Saef & Yuanrong Wang & Tomaso Aste, 2022. "Regime-based Implied Stochastic Volatility Model for Crypto Option Pricing," Papers 2208.12614, arXiv.org, revised Sep 2022.
    15. Jia, Yuecheng & Wu, Yangru & Yan, Shu & Liu, Yuzheng, 2023. "A seesaw effect in the cryptocurrency market: Understanding the return cross predictability of cryptocurrencies," Journal of Empirical Finance, Elsevier, vol. 74(C).
    16. Gradojevic, Nikola & Kukolj, Dragan & Adcock, Robert & Djakovic, Vladimir, 2023. "Forecasting Bitcoin with technical analysis: A not-so-random forest?," International Journal of Forecasting, Elsevier, vol. 39(1), pages 1-17.
    17. Bouri, Elie & Lau, Chi Keung Marco & Saeed, Tareq & Wang, Shixuan & Zhao, Yuqian, 2021. "On the intraday return curves of Bitcoin: Predictability and trading opportunities," International Review of Financial Analysis, Elsevier, vol. 76(C).
    18. Scharnowski, Stefan & Shi, Yanghua, 2024. "Intraday herding and attention around the clock," Journal of Behavioral and Experimental Finance, Elsevier, vol. 41(C).

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    More about this item

    JEL classification:

    • G02 - Financial Economics - - General - - - Behavioral Finance: Underlying Principles
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors

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