Dimitriadou, A and Gregoriou, A (2023) Predicting Bitcoin Prices Using Machine Learning. Entropy, 25 (5). p. 777. ISSN 1099-4300

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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.

Item Type:	Article
Uncontrolled Keywords:	Bitcoin; linear support vector machine; machine learning; random forest; 01 Mathematical Sciences; 02 Physical Sciences; Fluids & Plasmas
Subjects:	H Social Sciences > HF Commerce > HF5001 Business
Divisions:	Liverpool Business School
Publisher:	MDPI AG
SWORD Depositor:	A Symplectic
Date Deposited:	05 Mar 2024 10:47
Last Modified:	05 Mar 2024 10:47
DOI or ID number:	10.3390/e25050777
URI:	https://researchonline.ljmu.ac.uk/id/eprint/22735

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