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.

翻译：加密货币（如比特币）是当今金融体系中最具争议性和复杂性的技术创新之一。本研究旨在以高准确度预测比特币价格的变动趋势。为此，除采用逻辑回归（LR）作为基准模型外，还应用了四种不同的机器学习（ML）算法，即支持向量机（SVM）、人工神经网络（ANN）、朴素贝叶斯（NB）和随机森林（RF）。为测试这些算法，除了已有的连续数据集外，还创建并使用了离散数据集。算法性能评估使用了F统计量、准确率统计量、平均绝对误差（MAE）、均方根误差（RMSE）和根绝对误差（RAE）指标。采用t检验比较SVM、ANN、NB和RF与LR的性能表现。实证结果表明：在连续数据集中，随机森林（RF）的预测性能最高，而朴素贝叶斯（NB）最低；在离散数据集中，人工神经网络（ANN）性能最高，朴素贝叶斯（NB）性能最低。此外，离散数据集显著提升了所有估算算法（模型）的整体预测性能。