- Log In/Sign Up
- P-ISSN1738-3110
- E-ISSN2093-7717
- SCOPUS, ESCI
ISSN : 1738-3110
A Study on Predicting Cryptocurrency Distribution Prices Using Machine Learning Techniques
KIM, Hoik
Abstract
Purpose: Blockchain technology suggests ways to solve the problems in the existing industry. Among them, Cryptocurrency system, which is an element of Blockchain technology, is a very important factor for operating Blockchain. While Blockchain cryptocurrency has attracted attention, studies on cryptocurrency prices have been mainly conducted, however previous studies mainly conducted on Bitcoin prices. On the other hand, in the context of the creation and trading of various cryptocurrencies based on the Blockchain system, little research has been done on cryptocurrencies other than Bitcoin. Hence, this study attempts to find variables related to the prices of Dash, Litecoin, and Monero cryptocurrencies using machine learning techniques. We also attempt to find differences in the variables related to the prices for each cryptocurrencies and to examine machine learning techniques that can provide better performance. Research design, data, and methodology: This study performed Dash, Litecoin, and Monero price prediction analysis of cryptocurrency using Blockchain information and machine learning techniques. We employed number of transactions in Blockchain, amount of generated cryptocurrency, transaction fees, number of activity accounts in Blockchain, Block creation difficulty, block size, umber of created blocks as independent variables. This study tried to ensure the reliability of the analysis results through 10-fold cross validation. Blockchain information was hierarchically added for price prediction, and the analysis result was measured as RMSE and MAPE. Results: The analysis shows that the prices of Dash, Litecoin and Monero cryptocurrency are related to Blockchain information. Also, we found that different Blockchain information improves the analysis results for each cryptocurrency. In addition, this study found that the neural network machine learning technique provides better analysis results than support-vector machine in predicting cryptocurrency prices. Conclusion: This study concludes that the information of Blockchain should be considered for the prediction of the price of Dash, Litecoin, and Monero cryptocurrency. It also suggests that Blockchain information related to the price of cryptocurrency differs depending on the type of cryptocurrency. We suggest that future research on various types of cryptocurrencies is needed. The findings of this study can provide a theoretical basis for future cryptocurrency research in distribution management.
- keywords
- Blockchain, Cryptocurrency, Blockchain information, Machine Learning, Price Prediction, Distribution management
Reference
Antonopoulos, A. M. (2014). Mastering Bitcoin: Unlocking digital cryptocurrencies. O'Reilly Media, Inc.
Antonopoulos, A. M., & Wood, G. (2018). Mastering ethereum: Building smart contracts and dapps. O'Reilly Media, Inc.
Ciaian, P., Rajcaniova, M., & Kancs, D. A. (2016). The economics of Bitcoin price formation. Applied Economics, 48(19), 1799-1815.
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297.
Dang, T. T. (2019). Current situation of cryptocurrency in Vietnam. The Journal of Business, Economics, and Environmental Studies, 9(4), 29-34.
Farajnejad, E., & Lau, W. Y. (2017). A fuzzy based early warning system to predict banking distress on selected Asia-Pacific countries. The Journal of Asian Finance, Economics and Business, 4(1), 39-49.
Huang, W., Nakamori, Y., & Wang, S. Y. (2005). Forecasting stock market movement direction with support vector machine. Computers & Operations Research, 32(10), 2513-2522.
Jang, H., & Lee, J. (2018). An empirical study on modeling and prediction of Bitcoin prices with bayesian neural networks based on Blockchain information. IEEE Access, 6(1), 5427-5437.
Kara, Y., Boyacioglu, M. A., & Baykan, Ö. K. (2011). Predicting direction of stock price index movement using artificial neural networks and support vector machines:The sample of the Istanbul Stock Exchange. Expert Systems with Applications, 38(5), 5311-5319.
Kshetri, N. (2019). Blockchain and the economics of food safety. IT Professional, 21(3), 63-66.
Kuo, T. T., Kim, H. E., & Ohno-Machado, L. (2017). Blockchain distributed ledger technologies for biomedical and health care applications. Journal of the American Medical Informatics Association, 24(6), 1211-1220.
Mallqui, D. C., & Fernandes, R. A. (2019). Predicting the direction, maximum, minimum and closing prices of daily Bitcoin exchange rate using machine learning techniques. Applied Soft Computing, 75(1), 596-606.
Murphy, K. P. (2012). Machine learning: A probabilistic perspective. Cambridge, MA, USA: MIT Press.
Park, Y. E., Chaffar, S., Kim, M. S., & Ko, H. Y. (2017). Predicting Arab consumers’ preferences on the Korean contents distribution. Journal of Distribution Science, 15(4), 33-40.
Tsai, C. F., & Wu, J. W. (2008). Using neural network ensembles for bankruptcy prediction and credit scoring. Expert Systems with Applications, 34(4), 2639-2649.
Wang, P. (2011). Pricing currency options with support vector regression and stochastic volatility model with jumps. Expert Systems with Applications, 38(1), 1-7.
Wu, H., Li, Z., King, B., Ben Miled, Z., Wassick, J., & Tazelaar, J. (2017). A distributed ledger for supply chain physical distribution visibility. Information, 8(4), 1-18.
- Downloaded
- Viewed
- 0KCI Citations
- 0WOS Citations