Machine learning and AI-assisted trading have attracted growing interest for the past few years. Here, we use this approach to test the hypothesis that the inefficiency of the cryptocurrency market can be exploited to generate abnormal profits. We analyse daily data for cryptocurrencies for the period between Nov. 2015 and Apr. 2018. We show that simple trading strategies assisted by state-of-the-art machine learning algorithms outperform standard benchmarks. Our results show that nontrivial, but ultimately simple, algorithmic mechanisms can help anticipate the short-term evolution of the cryptocurrency market.
The popularity of cryptocurrencies has skyrocketed in 2017 due to several consecutive months of super exponential growth of their market capitalization , which peaked at more than $800 billions in Jan. 2018. Today, there are more than 1, 500 actively traded cryptocurrencies. Between 2.9 and 5.8 millions of private as well as institutional investors are in the different transaction networks, according to a recent survey, and access to the market has become easier over time. Major cryptocurrencies can be bought using fat currency in a number of online exchanges (e.g., Binance , Upbit , Kraken , etc.) and then be used in their turn to buy less popular cryptocurrencies. The volume of daily exchanges is currently superior to $15 billions. Since 2017, over 170 hedge funds specialised in cryptocurrencies have emerged and Bitcoin futures have been launched to address institutional demand for trading and hedging Bitcoin.
The market is diverse and provides investors with many different products. Just to mention a few, Bitcoin was expressly designed as a medium of exchange ; Dash offers improved services on top of Bitcoin’s feature set, including instantaneous and private transactions; Ethereum is a public, blockchain-based distributed computing platform featuring smart contract (scripting) functionality, and Ether is a cryptocurrency whose blockchain is generated by the Ethereum platform ; Ripple is a real-time gross settlement system (RTGS), currency exchange, and remittance network Ripple , and IOTA is focused on providing secure communications and payments between agents on the Internet of Things.
The emergence of a self-organised market of virtual currencies and/or assets whose value is generated primarily by social consensus] has naturally attracted interest from the scientific community. Recent results have shown that the long-term properties of the cryptocurrency marked have remained stable between 2013 and 2017 and are compatible with a scenario in which investors simply sample the market and allocate their money according to the cryptocurrency’s market shares. While this is true on average, various studies have focused on the analysis and forecasting of price fluctuations, using mostly traditional approaches for financial markets analysis and prediction.
The success of machine learning techniques for stock markets prediction suggests that these methods could be effective also in predicting cryptocurrencies prices. However, the application of machine learning algorithms to the cryptocurrency market has been limited so far to the analysis of Bitcoin prices, using random forests, Bayesian Neural network, long short-term memory neural network], and other algorithms. These studies were able to anticipate, to different degrees, the price fluctuations of Bitcoin, and revealed that best results were achieved by neural network based algorithms. Deep reinforcement learning was showed to beat the uniform buy and hold strategy in predicting the prices of 12 cryptocurrencies over one-year period.
Other attempts to use machine learning to predict the prices of cryptocurrencies other than Bitcoin come from nonacademic sources. Most of these analyses focused on a limited number of currencies and did not provide benchmark comparisons for their results.
Here, we test the performance of three models in predicting daily cryptocurrency price for 1,681 currencies. Two of the models are based on gradient boosting decision trees and one is based on long short-term memory (LSTM) recurrent neural networks. In all cases, we build investment portfolios based on the predictions and we compare their performance in terms of return on investment. We find that all of the three models perform better than a baseline ‘simple moving average’ model where a currency’s price is predicted as the average price across the preceding days and that the method based on long short-term memory recurrent neural networks systematically yields the best return on investment.
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