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.