APPLICATION OF ARTIFICIAL INTELLIGENCE TO BITCOIN COURSE MODELLING

Posted on by

Authors: Liashenko, Doctor of Sciences (Economics), Professor, ORCID ID 0000-0002-0197-4179, Kravets, PhD in Physics and Math, Associate Professor, ORCID ID 0000-0003-4823-5143, Repetskiyi, Economist Taras Schevchenko National University of Kyiv, Kyiv, Ukraine

Abstract:Artificial neural networks are modern methods suitable for solving the problem of nonlinear dependency approximation, which is successfully applied in many fields. This paper compares the predictive capabilities of Back Propagation, Radial Basis Function, Extreme Learning Machine, and Long-Short Term Memory neural networks to determine which artificial intelligence algorithm is best for modeling the price of Bitcoin opening. The criterion for comparing network performance was the standard deviation, the mean absolute deviation, and the accuracy of predicting the direction of change of course. At the same time, in the study of time series, it is recommended to perform a comprehensive data analysis using appropriate networks, depending on the length of the series and the specificity of the database.

Key words: Artificial intelligence; back propagation; radial basis function; extreme learning machine; long-short term memory; bitcoin.

Received: 29/03/2020

1st Revision: 10/04/2020

Accepted: 20/04/2020

DOI: https://doi.org/10.17721/1728-2667.2020/209-2/2

References

  1. Anderson, , Scanlon, L., Clarence-Smith, C., 2017. Bitcoin, Blockchain & Initial Coin Offerings: A Global Review. [Online]. Available: https://www.pinsentmasons.com/PDF/2017/FinTech/Bitcoin-Blockchain-guide.pdf
  2. Andersson, , Wegdell, A., 2014. Prospects of Bitcoin: An evaluation of its future. M.S. thesis, School of Economics & Management, Lund University, 2014.
  3. Yaga, , Mell, P., Roby, N., Scarfone, K., 2018. Blockchain Technology Overview. NISTIR 8202, October 2018, DOI: 10.6028/NISTI.IR.8202.
  4. Zeng, , Yang, M., Shen, Y., 2020. Fancy Bitcoin and conventional financial assets: Measuring market integration based on connectedness networks. Economic Modelling, DOI: 10.1016/j.econmod.2020.05.003.
  5. Eom, , 2020. Premium and speculative trading in bitcoin. Finance Research Letters, DOI: 10.1016/j.frl.2020.101505.
  6. Guegan, , Frunza, M.-C., 2020. Bubbles on Bitcoin Price: The Bitcoin Rush. In book: Risk Factors and Contagion in Commodity Markets and Stocks Markets, pp. 1-24, DOI: 10.1142/9789811210242_0001.
  7. Musiałkowska, , Kliber, A., Świerczyńska, K., Marszałek, P., 2020. Looking for a safe-haven in a crisis-driven Venezuela: The Caracas stock exchange vs gold, oil and bitcoin. Transforming Government People Process and Policy, DOI: 10.1108/TG-01-2020-0009.
  8. Jana, , Das, D., 2020. Did Bitcoin act as an antidote to the Chinese equity market and booster to Altcoins during the Novel Coronavirus outbreak? Preprint, DOI: 10.13140/RG.2.2.17602.94403.
  9. Chen, -W., Lin, X., 2014. Big data deep learning: Challenges and perspectives. IEEE Access, vol. 2, pp. 514-525, May 2014, DOI: 10.1109/ACCESS.2014.2325029.
  10. Chong, , Han, C., Park, F., 2017. Deep learning networks for stockmarket analysis and prediction: Methodology, data representations, and case studies. Expert Systems with Applications, vol. 83, pp. 187-205.
  11. Ding, , Zhang, Y., Liu, T., Duan, J., 2015. Deep learning for event-drivenstock prediction. Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI2015), pp. 2327-2333.
  12. Ding, , Xu, X., Ru, N., 2013. Extreme learning machine: algorithm, theory and applications. Artificial Intelligence Review, vol. 44(1), June 2013, DOI: 10.1007/s00521-013-1522-8.
  13. Schmidhuber, , 2014. Deep Learning in Neural Networks: An Overview. Neural Networks, vol. 61, pp. 85-117, 2014. DOI: 10.1016/j.neunet.2014.09.003.
  14. Ariyo Adebiyi, , Adewumi, A., Ayo, C., 2014. Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction. Journal of Applied Mathematics, vol. 2014, DOI: 10.1133/2014/614942.
  15. Zhang, P., 2003. Time Series Forecasting Using a Hybrid ARIMA and Neural Network Model. Neurocomputing, vol. 50, pp. 159- 175, 2003.
  16. Aggarwal, , Chandrasekaran, S., Annamalai, B., 2020. A complete empirical ensemble mode decomposition and support vector machine-based approach to predict Bitcoin prices. Journal of Behavioral and Experimental Finance, DOI: 10.1016/j.jbef.2020.100335.
  17. Chen, , Qiao, Z., Wang, M., Wamg, C., Du, R., Stanley, H.E., 2018. Which Artificial Intelligence Algorithm Better Predicts the Chinese Stock Market? IEEE Access, vol. 6, 2018. DOI: 10.1109/ACCESS.2018.2859809.
  18. Liashenko, , Kravets, T., 2019. The Relationship between Oil and Gas Prices, Dow Jones and US Dollar Indexes: A Wavelet Co- movement Estimation and Neural Network Forecasting. CEUR Workshop Proceedings, vol. 2393, pp. 348-363, 2019.
  19. Huang, , Nakamori, Y., Wang, S., 2005. Forecasting stock market movement direction with support vector machine. Computers & Operations Research, vol. 32, pp. 2513-2522, 2005. DOI: 10.1016/j.cor.2004.03.016.
  20. Li, , Cheng, J., Shi, J., Huang, F., 2012. Brief Introduction of Back Propagation (BP) Neural Network Algorithm and Its Improvement. In Advances in Computer Science and Information Engineering. Advances in Intelligent and Soft Computing, vol. 169, Springer, Berlin, Heidelberg, pp. 553-558, DOI: 10.1007/978-3-642-30223-7_87.
  21. Nielsen, , 2015. Neural Networks and Deep Learning. [Online]. Available: http://neuralnetworksanddeeplearning.com/.
  22. Gurney, , 1997. An introduction to neural networks. UCL Press, 316 p.
  23. Xu, , Ding, S., Shi, Z., Zhu, H., 2012. Optimizing radial basis function neural network based on rough sets and affinity propagation clustering algorithm. J. Zhejiang Univ. – Sci. C, vol. 13, pp. 131-138. DOI: 10.1631/jzus.C1100176.
  24. Wu, , Wang, H., Zhang, B., Du, K., 2012. Using Radial Basis Function Networks for Function Approximation and Classification. International Scholarly Research Notices, 34 p. DOI: 10.5402/2012/324194.
  25. Huang, , Zhu, Q., Siew, C., 2006. Extreme learning machine: Theory and applications. Neurocomputing, vol. 70, is. 1-3, pp. 489- 501, December 2006. DOI: 10.1016/j.neucom.2005.12.126.
  26. Hochreiter, , Bengio, Y., Frasconi, P., Schmidhuber, J., 2001. Gradient Flow in Recurrent Nets: the Difficulty of Learning Long- Term Dependencies. [Online]. Available: https://www.bioinf.jku.at/publications/older/ch7.pdf.
  27. Hochreiter, , Schmidhuber, J., 1997. Long Short-Term Memory. Neural Computation, vol. 9, no. 8, pp. 1735-1780.
  28. Zheng, , Xie, S., Dai, H., Chen, X., Wang, H., 2017. An Overview of Blockchain Technology:Architecture, Consensus, and Future Trends. IEEE International Congress on Big Data (BigData Congress), pp. 557-564, DOI: 10.1109/BigDataCongress.2017.85.

Download