Predicting Bitcoin Prices Using Machine Learning
Journal article
Authors | Dimitriadou, A. and Andros Gregoriou |
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Abstract | In this paper we predict Bitcoin movements by utilizing a machine-learning framework. We compile a dataset of 24 potential explanatory variables that are often employed in the finance literature. Using daily data from 2nd of December 2014 to July 8th 2019, we build forecasting models that utilize past Bitcoin values, other cryptocurrencies, exchange rates and other macroeconomic variables. Our empirical results suggest that the traditional logistic regression model outperforms the linear support vector machine and the random forest algorithm, reaching an accuracy of 66%. Moreover, based on the results, we provide evidence that points to the rejection of weak form efficiency in the Bitcoin market. |
Keywords | Bitcoin; machine learning ; linear support vector machine |
Year | 2023 |
Journal | Entropy |
Journal citation | 25 (5), pp. 1-10 |
Publisher | MDPI AG |
ISSN | 1099-4300 |
Digital Object Identifier (DOI) | https://doi.org/10.3390/e25050777 |
Web address (URL) | https://www.mdpi.com/1099-4300/25/5/777 |
Output status | Published |
Publication dates | 10 May 2023 |
Publication process dates | |
Accepted | 07 May 2023 |
Deposited | 25 May 2023 |
https://repository.derby.ac.uk/item/9yyqw/predicting-bitcoin-prices-using-machine-learning
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