Forecasting the movements of Bitcoin prices: an application of machine learning algorithms
| Autor: | Hakan Pabuçcu, Serdar Ongan, Ayse Ongan |
|---|---|
| Rok vydání: | 2023 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Mean squared error Computer science Computational Finance (q-fin.CP) Machine learning computer.software_genre bitcoin price forecasting Machine Learning (cs.LG) FOS: Economics and business Bayes' theorem Quantitative Finance - Computational Finance Approximation error machine learning algorithms lcsh:Finance lcsh:HG1-9999 Statistic Artificial neural network business.industry lcsh:T57-57.97 General Medicine Random forest cryptocurrency Support vector machine lcsh:Applied mathematics. Quantitative methods Benchmark (computing) Artificial intelligence business computer Algorithm |
| Zdroj: | Quantitative Finance and Economics, Vol 4, Iss 4, Pp 679-692 (2020) |
| DOI: | 10.48550/arxiv.2303.04642 |
| Popis: | 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. Comment: 14 pages, 2 figures and 15 tables |
| Databáze: | OpenAIRE |
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