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.
Highlights
Bitcoin was the first open source distributed cryptocurrency released in 2009 after it was introduced in a paper “Bitcoin: A Peer-to-Peer Electronic Cash System” by a developer under the pseudonym Satoshi Nakamoto
In what follows we will show that in the case of Bitcoin, profitable arbitrage windows may open among Bitcoin exchanges to various fiat currencies, and that the next-day price-change direction for a single time series may be predicted to a certain degree by using machine learning methods trained on the past daily data and at a prediction level that is higher than the equal odds of fair coin tossing
In a work motivated by market efficiency reasons related to ours, Lahmiri et al, (2018) analyzed the Bitcoin time series in seven different exchanges, finding that “the values of measured entropy indicate a high degree of randomness in the series”
Summary
Bitcoin was the first open source distributed cryptocurrency released in 2009 after it was introduced in a paper “Bitcoin: A Peer-to-Peer Electronic Cash System” by a developer under the pseudonym Satoshi Nakamoto. In what follows we will show that in the case of Bitcoin, profitable arbitrage windows may open among Bitcoin exchanges to various fiat currencies, and that the next-day price-change direction (sign of logarithmic return) for a single time series may be predicted to a certain degree by using machine learning methods trained on the past daily data and at a prediction level that is higher than the equal odds of fair coin tossing.
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