Cryptocurrency Trading Using Machine Learning

Thomas E. Koker and Dimitrios Koutmos
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Thomas E. Koker: Worcester Polytechnic Institute, Worcester, MA 01609, USA
Dimitrios Koutmos: Department of Accounting, Finance, and Business Law, College of Business, Texas A&M University–Corpus Christi, Corpus Christi, TX 78412, USA

JRFM, 2020, vol. 13, issue 8, 1-7

Abstract: We present a model for active trading based on reinforcement machine learning and apply this to five major cryptocurrencies in circulation. In relation to a buy-and-hold approach, we demonstrate how this model yields enhanced risk-adjusted returns and serves to reduce downside risk. These findings hold when accounting for actual transaction costs. We conclude that real-world portfolio management application of the model is viable, yet, performance can vary based on how it is calibrated in test samples.

Keywords: Bitcoin; cryptocurrencies; direct reinforcement; machine learning; risk-return (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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