  Cryptocurrencies, such as Bitcoin, are one of the most controversial and
complex technological innovations in today's financial system. This study aims
to forecast the movements of Bitcoin prices at a high degree of accuracy. To
this aim, four different Machine Learning (ML) algorithms are applied, namely,
the Support Vector Machines (SVM), the Artificial Neural Network (ANN), the
Naive Bayes (NB) and the Random Forest (RF) besides the logistic regression
(LR) as a benchmark model. In order to test these algorithms, besides existing
continuous dataset, discrete dataset was also created and used. For the
evaluations of algorithm performances, the F statistic, accuracy statistic, the
Mean Absolute Error (MAE), the Root Mean Square Error (RMSE) and the Root
Absolute Error (RAE) metrics were used. The t test was used to compare the
performances of the SVM, ANN, NB and RF with the performance of the LR.
Empirical findings reveal that, while the RF has the highest forecasting
performance in the continuous dataset, the NB has the lowest. On the other
hand, while the ANN has the highest and the NB the lowest performance in the
discrete dataset. Furthermore, the discrete dataset improves the overall
forecasting performance in all algorithms (models) estimated.
