Forecasting the movements of Bitcoin prices: an application of machine learning algorithms

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dc.authoridpabuccu, hakan/0000-0003-2267-5175
dc.contributor.authorPabuccu, Hakan
dc.contributor.authorOngan, Serdar
dc.contributor.authorOngan, Ayse
dc.date.accessioned2024-10-04T18:51:01Z
dc.date.available2024-10-04T18:51:01Z
dc.date.issued2020
dc.departmentBayburt Üniversitesien_US
dc.description.abstractCryptocurrencies, 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.en_US
dc.identifier.doi10.3934/QFE.2020031
dc.identifier.endpage692en_US
dc.identifier.issn2573-0134
dc.identifier.issue4en_US
dc.identifier.startpage679en_US
dc.identifier.urihttps://doi.org/10.3934/QFE.2020031
dc.identifier.urihttp://hdl.handle.net/20.500.12403/3332
dc.identifier.volume4en_US
dc.identifier.wosWOS:000604334700007en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherAmer Inst Mathematical Sciences-Aimsen_US
dc.relation.ispartofQuantitative Finance and Economicsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBitcoin price forecastingen_US
dc.subjectcryptocurrencyen_US
dc.subjectmachine learning algorithmsen_US
dc.titleForecasting the movements of Bitcoin prices: an application of machine learning algorithmsen_US
dc.typeArticleen_US

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