Cryptocurrency trading using machine learning: A Technical note

Date

8/10/2020

Authors

Koker, Thomas E.
Koutmos, Dimitrios

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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.

Description

Keywords

Bitcoin, cryptocurrencies, direct reinforcement, machine learning, risk-return

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Attribution 4.0 International

Citation

Koker, T.E. and Koutmos, D., 2020. Cryptocurrency Trading Using Machine Learning. Journal of Risk and Financial Management, 13(8), p.178.