Cryptocurrency trading using machine learning: A Technical note

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dc.contributor.authorKoker, Thomas E.
dc.contributor.authorKoutmos, Dimitrios
dc.date.accessioned2021-10-21T17:36:29Z
dc.date.available2021-10-21T17:36:29Z
dc.date.issued8/10/2020
dc.description.abstractWe 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.en_US
dc.identifier.citationKoker, T.E. and Koutmos, D., 2020. Cryptocurrency Trading Using Machine Learning. Journal of Risk and Financial Management, 13(8), p.178.en_US
dc.identifier.doihttps://doi.org/10.3390/jrfm13080178
dc.identifier.urihttps://hdl.handle.net/1969.6/89854
dc.language.isoen_USen_US
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectBitcoinen_US
dc.subjectcryptocurrenciesen_US
dc.subjectdirect reinforcementen_US
dc.subjectmachine learningen_US
dc.subjectrisk-returnen_US
dc.titleCryptocurrency trading using machine learning: A Technical noteen_US
dc.typeArticleen_US

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