Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/258590 
Year of Publication: 
2021
Citation: 
[Journal:] Journal of Risk and Financial Management [ISSN:] 1911-8074 [Volume:] 14 [Issue:] 10 [Article No.:] 486 [Publisher:] MDPI [Place:] Basel [Year:] 2021 [Pages:] 1-10
Publisher: 
MDPI, Basel
Abstract: 
In this study, we predicted the log returns of the top 10 cryptocurrencies based on market cap, using univariate and multivariate machine learning methods such as recurrent neural networks, deep learning neural networks, Holt's exponential smoothing, autoregressive integrated moving average, ForecastX, and long short-term memory networks. The multivariate long short-term memory networks performed better than the univariate machine learning methods in terms of the prediction error measures.
Subjects: 
cryptocurrencies
deep learning networks
recurrent neural networks
long short-term memory networks
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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