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Cryptocurrency Trading Using Machine Learning

1
Worcester Polytechnic Institute, Worcester, MA 01609, USA
2
Department of Accounting, Finance, and Business Law, College of Business, Texas A&M University–Corpus Christi, Corpus Christi, TX 78412, USA
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Author to whom correspondence should be addressed.
J. Risk Financial Manag. 2020, 13(8), 178; https://doi.org/10.3390/jrfm13080178
Received: 26 June 2020 / Revised: 30 July 2020 / Accepted: 31 July 2020 / Published: 10 August 2020
(This article belongs to the Special Issue Machine Learning Applications in Finance)
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. View Full-Text
Keywords: Bitcoin; cryptocurrencies; direct reinforcement; machine learning; risk-return Bitcoin; cryptocurrencies; direct reinforcement; machine learning; risk-return
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MDPI and ACS Style

Koker, T.E.; Koutmos, D. Cryptocurrency Trading Using Machine Learning. J. Risk Financial Manag. 2020, 13, 178.

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