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

Hakan Pabuçcu; Serdar Ongan; Ayse Ongan; Hakan Pabuçcu; Serdar Ongan; Ayse Ongan

doi:10.3934/QFE.2020031

Quantitative Finance and Economics

2020, Volume 4, Issue 4: 679-692. doi: 10.3934/QFE.2020031

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

1.
Department of Business Administration, Bayburt University, Bayburt, Turkey
2.
Department of Economics, St. Mary's College of Maryland, Maryland, USA
3.
Fuqua School of Business, Duke University, Durham, USA

Received: 25 August 2020 Accepted: 12 November 2020 Published: 16 November 2020
JEL Codes: G1, C6, C12

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 Naï ve 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.
- Bitcoin price forecasting,
- cryptocurrency,
- machine learning algorithms
Citation: Hakan Pabuçcu, Serdar Ongan, Ayse Ongan. Forecasting the movements of Bitcoin prices: an application of machine learning algorithms[J]. Quantitative Finance and Economics, 2020, 4(4): 679-692. doi: 10.3934/QFE.2020031

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Abstract

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 Naï ve 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.

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