ANALYSIS OF BITCOIN MARKET EFFICIENCY BY USING MACHINE LEARNING

Yuki Hirano (), Lukáš Pichl (), Cheoljun Eom () and Taisei Kaizoji ()
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Yuki Hirano: International Christian University, Mitaka, Tokyo
Lukáš Pichl: International Christian University, Mitaka, Tokyo
Cheoljun Eom: Pusan National University, Busan

CBU International Conference Proceedings, 2018, vol. 6, issue 0, 175-180

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.

Keywords: BitcoinXBT; Neural Network; Gated Recurrent Unit; Long Short-Term Memory (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:aad:iseicj:v:6:y:2018:i:0:p:175-180

DOI: 10.12955/cbup.v6.1152

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