Predicting Bitcoin Prices Using Machine Learning
Athanasia Dimitriadou1, Andros Gregoriou2
1College of Business, Law and Social Sciences, University of Derby, Lonsdale House, Quaker Way, Derby DE1 3HD, UK.
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.
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