Predicting Bitcoin Prices Using Machine Learning

Athanasia Dimitriadou, Andros Gregoriou

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Article number777
Number of pages10
Issue number5
Publication statusPublished - 10 May 2023


  • machine learning
  • Bitcoin
  • linear support vector machine
  • random forest


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