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Anticipating cryptocurrency prices using machine learning

Author

Listed:
  • Laura Alessandretti
  • Abeer ElBahrawy
  • Luca Maria Aiello
  • Andrea Baronchelli

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

Suggested Citation

  • Laura Alessandretti & Abeer ElBahrawy & Luca Maria Aiello & Andrea Baronchelli, 2018. "Anticipating cryptocurrency prices using machine learning," Papers 1805.08550, arXiv.org, revised Nov 2018.
  • Handle: RePEc:arx:papers:1805.08550
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    References listed on IDEAS

    as
    1. Urquhart, Andrew, 2018. "What causes the attention of Bitcoin?," Economics Letters, Elsevier, vol. 166(C), pages 40-44.
    2. David Garcia & Claudio Juan Tessone & Pavlin Mavrodiev & Nicolas Perony, 2014. "The digital traces of bubbles: feedback cycles between socio-economic signals in the Bitcoin economy," Papers 1408.1494, arXiv.org.
    3. Hermann Elendner & Simon Trimborn & Bobby Ong & Teik Ming Lee, 2016. "The Cross-Section of Crypto-Currencies as Financial Assets: An Overview," SFB 649 Discussion Papers SFB649DP2016-038, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    4. repec:agr:journl:v:1(590):y:2014:i:1(590):p:103-114 is not listed on IDEAS
    5. Yhlas Sovbetov, 2018. "Factors Influencing Cryptocurrency Prices: Evidence from Bitcoin, Ethereum, Dash, Litcoin, and Monero," Journal of Economics and Financial Analysis, Tripal Publishing House, vol. 2(2), pages 1-27.
    6. Stjepan Beguv{s}i'c & Zvonko Kostanjv{c}ar & H. Eugene Stanley & Boris Podobnik, 2018. "Scaling properties of extreme price fluctuations in Bitcoin markets," Papers 1803.08405, arXiv.org.
    7. Pavel Ciaian & Miroslava Rajcaniova & d’Artis Kancs, 2016. "The economics of BitCoin price formation," Applied Economics, Taylor & Francis Journals, vol. 48(19), pages 1799-1815, April.
    8. Michel Rauchs & Garrick Hileman, 2017. "Global Cryptocurrency Benchmarking Study," Cambridge Centre for Alternative Finance Reports, Cambridge Centre for Alternative Finance, Cambridge Judge Business School, University of Cambridge, number 201704-gcbs.
    9. Iwamura, Mitsuru & Kitamura, Yukinobu & Matsumoto, Tsutomu, 2014. "Is Bitcoin the Only Cryptocurrency in the Town? Economics of Cryptocurrency and Friedrich A.Hayek," Discussion Paper Series 602, Institute of Economic Research, Hitotsubashi University.
    10. Tianyu Ray Li & Anup S. Chamrajnagar & Xander R. Fong & Nicholas R. Rizik & Feng Fu, 2018. "Sentiment-Based Prediction of Alternative Cryptocurrency Price Fluctuations Using Gradient Boosting Tree Model," Papers 1805.00558, arXiv.org.
    11. Lawrence H. White, 2015. "The Market for Cryptocurrencies," Cato Journal, Cato Journal, Cato Institute, vol. 35(2), pages 383-402, Spring/Su.
    12. Sean Foley & Jonathan R Karlsen & Tālis J Putniņš, 2019. "Sex, Drugs, and Bitcoin: How Much Illegal Activity Is Financed through Cryptocurrencies?," Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 1798-1853.
    13. Marco Alberto Javarone & Craig Steven Wright, 2018. "From Bitcoin to Bitcoin Cash: a network analysis," Papers 1804.02350, arXiv.org, revised Jul 2018.
    14. Rainer Böhme & Nicolas Christin & Benjamin Edelman & Tyler Moore, 2015. "Bitcoin: Economics, Technology, and Governance," Journal of Economic Perspectives, American Economic Association, vol. 29(2), pages 213-238, Spring.
    15. Ali, Robleh & Barrdear, John & Clews, Roger & Southgate, James, 2014. "The economics of digital currencies," Bank of England Quarterly Bulletin, Bank of England, vol. 54(3), pages 276-286.
    16. Neil Gandal & Hanna Halaburda, 2016. "Can We Predict the Winner in a Market with Network Effects? Competition in Cryptocurrency Market," Games, MDPI, Open Access Journal, vol. 7(3), pages 1-21, July.
    17. Abeer ElBahrawy & Laura Alessandretti & Anne Kandler & Romualdo Pastor-Satorras & Andrea Baronchelli, 2017. "Evolutionary dynamics of the cryptocurrency market," Papers 1705.05334, arXiv.org, revised Nov 2017.
    18. Dwyer, Gerald P., 2015. "The economics of Bitcoin and similar private digital currencies," Journal of Financial Stability, Elsevier, vol. 17(C), pages 81-91.
    19. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    20. Begušić, Stjepan & Kostanjčar, Zvonko & Eugene Stanley, H. & Podobnik, Boris, 2018. "Scaling properties of extreme price fluctuations in Bitcoin markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 400-406.
    21. Brock, William & Lakonishok, Josef & LeBaron, Blake, 1992. "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns," Journal of Finance, American Finance Association, vol. 47(5), pages 1731-1764, December.
    22. Angela ROGOJANU & Liana BADEA, 2014. "The issue of competing currencies. Case study – Bitcoin," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania - AGER, vol. 0(1(590)), pages 103-114, January.
    23. David Garcia & Frank Schweitzer, 2015. "Social signals and algorithmic trading of Bitcoin," Papers 1506.01513, arXiv.org, revised Sep 2015.
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    Cited by:

    1. Andrey A. Kozlov & Andrey V. Vlasov, 2019. "Cryptoeconomics: Pilot Study on Investments in ICO Startups Using Neural Networks," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 127006, Russia, issue 1, pages 76-87, February.
    2. Abeer ElBahrawy & Laura Alessandretti & Andrea Baronchelli, 2019. "Wikipedia and Digital Currencies: Interplay Between Collective Attention and Market Performance," Papers 1902.04517, arXiv.org, revised Mar 2019.
    3. Silvia Bartolucci & Andrei Kirilenko, 2019. "A Model of the Optimal Selection of Crypto Assets," Papers 1906.09632, arXiv.org.
    4. Higor Y. D. Sigaki & Matjaz Perc & Haroldo V. Ribeiro, 2019. "Clustering patterns in efficiency and the coming-of-age of the cryptocurrency market," Papers 1901.04967, arXiv.org.
    5. Silvia Bartolucci & Fabio Caccioli & Pierpaolo Vivo, 2019. "A percolation model for the emergence of the Bitcoin Lightning Network," Papers 1912.03556, arXiv.org.
    6. Fan Fang & Carmine Ventre & Michail Basios & Hoiliong Kong & Leslie Kanthan & Lingbo Li & David Martinez-Regoband & Fan Wu, 2020. "Cryptocurrency Trading: A Comprehensive Survey," Papers 2003.11352, arXiv.org, revised Apr 2020.

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