• 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
Keywords: Bitcoin, XBT, Neural Network, Gated Recurrent Unit, Long Short-Term Memory


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.


Alvarez-Ramirez, J., Rodriguez, E., Ibarra-Valdez, C. (2018) Long-range correlations and asymmetry in the Bitcoin market, Physica A: Statistical Mechanics and its Applications vol. 492, pp. 948-955.

Bariviera, A. F., M. J. Basgall, W. Hasperue, and M. Naiouf (2017) Some Stylized Facts of the Bitcoin Market, Physica A vol. 484, pp. 82-90.

Ciaian, P., Rajcaniova, M., Kancs d'A (2018) Virtual relationships: Short- and long-run evidence from BitCoin and altcoin markets, Journal of International Financial Markets, Institutions and Money vol. 52, pp. 173-195.

Coinmarketcap (2018) Cryptocurrency Market Capitalizations,, Accessed 2018/02/15.

Corbet, S., Meegan, A., Larkin, C., Lucey, B., Yarovaya, L. (2018) Exploring the dynamic relationships between cryptocurrencies and other financial assets, Economics Letters, vol. 165 pp. 28-34.

Elman, J. L. (1990) Finding Structure in Time, Cognitive Science vol. 14 No. 2, pp. 179-211.

Fama, E.F. (1970) Efficient Capital Markets: A Review of Theory and Empirical Work, The Journal of Finance vol. 25, pp. 383-417.

Fama, E.F. (1991) Efficient Capital Markets: II, The Journal of Finance vol. 46, pp. 1575-1617.

Gkillas K., Katsiampa, P. (2018) An application of extreme value theory to cryptocurrencies, Economics Letters vol. 164, pp. 109-111.

Hayes, A. S. (2017) Cryptocurrency value formation: An empirical study leading to a cost of production model for valuing bitcoin, Telematics and Informatics vol. 34 No. 7, pp. 1308-1321.

Hendrickson, J. R., Luther, W. J. (2017) Banning bitcoin, Journal of Economic Behavior & Organization vol. 141, pp. 188-195.

Lahmiri, S., Bekiros, S.. Salvi A. (2018) Long-range memory, distributional variation and randomness of bitcoin volatility, Chaos, Solitons & Fractals vol. 107, pp. 43-48.

Luther, W. J., Salter, A. W. (2017) Bitcoin and the bailout, The Quarterly Review of Economics and Finance vol. 66, pp. 50-56.

Phillip, A., Chan, J. S. K., Peiris, S. (2018) A new look at Cryptocurrencies, Economics Letters vol. 163, pp. 6-9.

Pieters, G., Vivanco, S. (2017) Financial regulations and price inconsistencies across Bitcoin markets, Information Economics and Policy vol. 39, pp. 1-14.

R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL

Ryan, J. A., Ulrich, J. M. (2017). quantmod: Quantitative Financial Modelling Framework. R package version 0.4-12.

Urquhart, A. (2017) Price clustering in Bitcoin, Economics Letters vol. 159, pp. 145-148.

Ziegeldorf, J. H., Matzutt, R., Henze, M., Grossmann, F., Wehrle, K. (2018) Secure and anonymous decentralized Bitcoin mixing, Future Generation Computer Systems vol. 80, pp. 448-466.