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Complexity
Volume 2018, Article ID 8983590, 16 pages
https://doi.org/10.1155/2018/8983590
Research Article

Anticipating Cryptocurrency Prices Using Machine Learning

1Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
2City, University of London, Department of Mathematics, London EC1V 0HB, UK
3Nokia Bell Labs, Cambridge CB3 0FA, UK
4UCL Centre for Blockchain Technologies, University College London, UK

Correspondence should be addressed to Andrea Baronchelli; ku.ca.ytic@1.illehcnorab.aerdna

Received 29 May 2018; Revised 28 September 2018; Accepted 17 October 2018; Published 4 November 2018

Academic Editor: Massimiliano Zanin

Copyright © 2018 Laura Alessandretti et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.