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Analysis Of Bitcoin Market Efficiency By Using Machine Learning

Author

Listed:
  • Yuki Hirano

    (International Christian University, Mitaka, Tokyo)

  • Lukáš Pichl

    (International Christian University, Mitaka, Tokyo)

  • Cheoljun Eom

    (Pusan National University, Busan)

  • Taisei Kaizoji

    (International Christian University, Mitaka, Tokyo)

Abstract

The issue of market efficiency for cryptocurrency exchanges has been largely unexplored. Here we put Bitcoin, the leading cryptocurrency, on a test by studying the applicability of the Efficient Market Hypothesis by Fama from two viewpoints: (1) the existence of profitable arbitrage spread among Bitcoin exchanges, and (2) the possibility to predict Bitcoin prices in EUR (time period 2013-2017) and the direction of price movement (up or down) on the daily trading scale. Our results show that the Bitcoin market in the time period studied is partially inefficient. Thus the market process is predictable to a degree, hence not a pure martingale. In particular, the F-measure for XBTEUR time series obtained by three major recurrent neural network based machine learning methods was about 67%, i.e. a way above the unbiased coin tossing odds of 50% equal chance.

Suggested Citation

  • Yuki Hirano & Lukáš Pichl & Cheoljun Eom & Taisei Kaizoji, 2018. "Analysis Of Bitcoin Market Efficiency By Using Machine Learning," CBU International Conference Proceedings, ISE Research Institute, vol. 6(0), pages 175-180, September.
    • Handle: RePEc:aad:iseicj:v:6:y:2018:i:0:p:175-180
    DOI: 10.12955/cbup.v6.1152
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    Cited by:

    1. Park, Sangjin & Jang, Kwahngsoo & Yang, Jae-Suk, 2021. "Information flow between bitcoin and other financial assets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    2. Saggese, Pietro & Belmonte, Alessandro & Dimitri, Nicola & Facchini, Angelo & Böhme, Rainer, 2023. "Arbitrageurs in the Bitcoin ecosystem: Evidence from user-level trading patterns in the Mt. Gox exchange platform," Journal of Economic Behavior & Organization, Elsevier, vol. 213(C), pages 251-270.

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