Machine Learning the Cryptocurrency Market

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 $1,681$ 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 non-trivial, but ultimately simple, algorithmic mechanisms can help anticipate the short-term evolution of the cryptocurrency market.

[1]  Pavlin Mavrodiev,et al.  The digital traces of bubbles: feedback cycles between socio-economic signals in the Bitcoin economy , 2014, Journal of The Royal Society Interface.

[2]  B. LeBaron,et al.  Simple Technical Trading Rules and the Stochastic Properties of Stock Returns , 1992 .

[3]  J. Friedman Stochastic gradient boosting , 2002 .

[4]  Craig Ellis,et al.  Is smarter better? A comparison of adaptive, and simple moving average trading strategies , 2005 .

[5]  Satoshi Nakamoto Bitcoin : A Peer-to-Peer Electronic Cash System , 2009 .

[6]  P. Arumugam,et al.  Financial Stock Market Forecast using Data Mining Techniques , 2010 .

[7]  Michel Rauchs,et al.  Global Cryptocurrency Benchmarking Study , 2017 .

[8]  Lawrence H. White,et al.  The Market for Cryptocurrencies , 2014 .

[9]  Alex 'Sandy' Pentland,et al.  An Experimental Study of Cryptocurrency Market Dynamics , 2018, CHI.

[10]  Hossam Faris,et al.  A Comparison between Regression, Artificial Neural Networks and Support Vector Machines for Predicting Stock Market Index , 2015 .

[11]  I. Csabai,et al.  Inferring the interplay between network structure and market effects in Bitcoin , 2014, ArXiv.

[12]  Feng Fu,et al.  Sentiment-Based Prediction of Alternative Cryptocurrency Price Fluctuations Using Gradient Boosting Tree Model , 2018, Front. Phys..

[13]  Thomas Kilgallen Testing the Simple Moving Average across Commodities, Global Stock Indices, and Currencies , 2012, The Journal of Wealth Management.

[14]  Zhengyao Jiang,et al.  Cryptocurrency portfolio management with deep reinforcement learning , 2016, 2017 Intelligent Systems Conference (IntelliSys).

[15]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[16]  Pablo Jensen,et al.  Coexistence of several currencies in presence of increasing returns to adoption , 2018 .

[17]  Michael A. Cusumano The Bitcoin ecosystem , 2014, Commun. ACM.

[18]  Isaac Madan Automated Bitcoin Trading via Machine Learning Algorithms , 2014 .

[19]  Marco Alberto Javarone,et al.  From Bitcoin to Bitcoin Cash: a network analysis , 2018, CRYBLOCK@MobiSys.

[20]  Hermann Elendner,et al.  The Cross-Section of Crypto-Currencies as Financial Assets: An Overview , 2016 .

[21]  Marcel C. Minutolo,et al.  Does Bitcoin exhibit the same asymmetric multifractal cross-correlations with crude oil, gold and DJIA as the Euro, Great British Pound and Yen? , 2018 .

[22]  Phichhang Ou,et al.  Prediction of Stock Market Index Movement by Ten Data Mining Techniques , 2009 .

[23]  Paola Ceruleo,et al.  Bitcoin: a rival to fiat money or a speculative financial asset? , 2014 .

[24]  Robleh Ali,et al.  The Economics of Digital Currencies , 2014 .

[25]  H. Stanley,et al.  Scaling properties of extreme price fluctuations in Bitcoin markets , 2018, Physica A: Statistical Mechanics and its Applications.

[26]  Blake LeBaron,et al.  The Stability of Moving Average Technical Trading Rules on the Dow Jones Index , 2000 .

[27]  T. Moore,et al.  Bitcoin: Economics, Technology, and Governance , 2014 .

[28]  Nino Antulov-Fantulin,et al.  Predicting short-term Bitcoin price fluctuations from buy and sell orders , 2018, ArXiv.

[29]  Pei-Chann Chang,et al.  An Ensemble of Neural Networks for Stock Trading Decision Making , 2009, ICIC.

[30]  Zachary Chase Lipton A Critical Review of Recurrent Neural Networks for Sequence Learning , 2015, ArXiv.

[31]  Shouyang Wang,et al.  Forecasting stock market movement direction with support vector machine , 2005, Comput. Oper. Res..

[32]  Yhlas Sovbetov Factors Influencing Cryptocurrency Prices: Evidence from Bitcoin, Ethereum, Dash, Litcoin, and Monero , 2018 .

[33]  Andrea Baronchelli,et al.  The emergence of consensus: a primer , 2017, Royal Society Open Science.

[34]  Ladislav Kristoufek,et al.  What Are the Main Drivers of the Bitcoin Price? Evidence from Wavelet Coherence Analysis , 2014, PloS one.

[35]  P. Ciaian,et al.  The economics of BitCoin price formation , 2014, 1405.4498.

[36]  David Enke,et al.  The use of data mining and neural networks for forecasting stock market returns , 2005, Expert Syst. Appl..

[37]  Andrea Baronchelli,et al.  Evolutionary dynamics of the cryptocurrency market , 2017, Royal Society Open Science.

[38]  Angela Rogojanu,et al.  The issue of competing currencies. Case study – Bitcoin , 2014 .

[39]  Piotr Indyk,et al.  Mining the stock market (extended abstract): which measure is best? , 2000, KDD '00.

[40]  G. Dwyer The Economics of Bitcoin and Similar Private Digital Currencies , 2014 .

[41]  Simon Caton,et al.  Predicting the Price of Bitcoin Using Machine Learning , 2018, 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP).

[42]  Andrew Urquhart,et al.  What causes the attention of Bitcoin , 2018 .

[43]  Jaewook Lee,et al.  An Empirical Study on Modeling and Prediction of Bitcoin Prices With Bayesian Neural Networks Based on Blockchain Information , 2018, IEEE Access.

[44]  Simon Trimborn,et al.  CRIX an Index for Blockchain Based Currencies , 2016 .

[45]  G. Thomas Friedlob,et al.  Understanding Return on Investment , 1996 .

[46]  A. Shilling Market Timing: Better Than a Buy-and-Hold Strategy , 1992 .

[47]  Didier Sornette,et al.  Classification of crypto-coins and tokens from the dynamics of their power law capitalisation distributions , 2018 .

[48]  Frank Schweitzer,et al.  Social signals and algorithmic trading of Bitcoin , 2015, Royal Society Open Science.

[49]  Neil Gandal,et al.  Can We Predict the Winner in a Market with Network Effects? Competition in Cryptocurrency Market , 2016, Games.

[50]  H. Eugene Stanley,et al.  Quantifying Wikipedia Usage Patterns Before Stock Market Moves , 2013, Scientific Reports.

[51]  岩村 充,et al.  Is bitcoin the only cryptocurrency in the town? : economics of cryptocurrency and Friedrich A. Hayek , 2014 .

[52]  Jean-Philippe Vergne,et al.  Buzz Factor or Innovation Potential: What Explains Cryptocurrencies’ Returns? , 2017, PloS one.

[53]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[54]  Laetitia Gauvin,et al.  Analysis of the Bitcoin blockchain: socio-economic factors behind the adoption , 2018, EPJ Data Science.

[55]  Ladislav Kristoufek,et al.  BitCoin meets Google Trends and Wikipedia: Quantifying the relationship between phenomena of the Internet era , 2013, Scientific Reports.