Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/239253 
Year of Publication: 
2020
Citation: 
[Journal:] Journal of Risk and Financial Management [ISSN:] 1911-8074 [Volume:] 13 [Issue:] 8 [Publisher:] MDPI [Place:] Basel [Year:] 2020 [Pages:] 1-7
Publisher: 
MDPI, Basel
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.
Subjects: 
cryptocurrencies
machine learning
Bitcoin
direct reinforcement
risk-return
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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