Next Article in Journal
Do Overlapped Audit Committee Directors Affect Tax Avoidance?
Previous Article in Journal
Self-Organising (Kohonen) Maps for the Vietnam Banking Industry
Previous Article in Special Issue
A Novel Model Structured on Predictive Churn Methods in a Banking Organization
Article

Univariate and Multivariate Machine Learning Forecasting Models on the Price Returns of Cryptocurrencies

Statistics Discipline, Division of Science and Mathematics, University of Minnesota-Morris, Morris, MN 56267, USA
*
Author to whom correspondence should be addressed.
Academic Editor: James R. Barth
J. Risk Financial Manag. 2021, 14(10), 486; https://doi.org/10.3390/jrfm14100486
Received: 25 September 2021 / Revised: 9 October 2021 / Accepted: 11 October 2021 / Published: 14 October 2021
(This article belongs to the Collection Machine Learning Applications in Finance)
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. View Full-Text
Keywords: cryptocurrencies; deep learning networks; recurrent neural networks; long short-term memory networks cryptocurrencies; deep learning networks; recurrent neural networks; long short-term memory networks
Show Figures

Figure 1

MDPI and ACS Style

Miller, D.; Kim, J.-M. Univariate and Multivariate Machine Learning Forecasting Models on the Price Returns of Cryptocurrencies. J. Risk Financial Manag. 2021, 14, 486. https://doi.org/10.3390/jrfm14100486

AMA Style

Miller D, Kim J-M. Univariate and Multivariate Machine Learning Forecasting Models on the Price Returns of Cryptocurrencies. Journal of Risk and Financial Management. 2021; 14(10):486. https://doi.org/10.3390/jrfm14100486

Chicago/Turabian Style

Miller, Dante, and Jong-Min Kim. 2021. "Univariate and Multivariate Machine Learning Forecasting Models on the Price Returns of Cryptocurrencies" Journal of Risk and Financial Management 14, no. 10: 486. https://doi.org/10.3390/jrfm14100486

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop