A Research: Investigation of Financial Applications with Blockchain Technology

Abstract

Cryptocurrencies have revolutionized the financial landscape by providing decentralized and anonymous payment systems, making them an intriguing subject for investors and researchers. This article delves into applying machine learning techniques for predicting cryptocurrency prices, mainly focusing on Bitcoin, Ethereum, and Binance Coin. Employing a range of machine learning models, including XGBoost, Linear Regression, and Gaussian Processes, the study aims to evaluate their predictive performance comprehensively. The results are promising; our models outperform existing studies, achieving impressively low RMSE values of 0.0040 for Bitcoin, 0.028 for Ethereum, and 0.027 for Binance Coin. These findings contribute valuable insights into the volatility and dynamics of cryptocurrency prices and underscore the potential of machine learning in shaping financial decision-making. Future directions include integrating advanced deep learning models, additional data sources, and ensemble methods to enhance prediction accuracy and robustness.

Keywords

• Cryptocurrencies
• Machine Learning
• Price Prediction
• Bitcoin
• Ethereum
• Binance Coin

References

1. Marc Pilkington. 11 Blockchain technology: principles and applications. Research handbook on digital transformations, 225(2016), 2016.
2. Pinyaphat Tasatanattakool and Chian Techapanupreeda. Blockchain: Challenges and applications. In 2018 International Conference on Information Networking (ICOIN), pages 473–475. IEEE, 2018.
3. Thippa Reddy Gadekallu, Thien Huynh-The, Weizheng Wang, Gokul Yenduri, Pasika Ranaweera, Quoc-Viet Pham, Daniel Benevides da Costa, and Madhusanka Liyanage. Blockchain for the metaverse: A review. arXiv preprint arXiv:2203.09738, 2022.
4. Saveen A Abeyratne and Radmehr P Monfared. Blockchain-ready manufacturing supply chain using distributed ledger. International journal of research in engineering and technology, 5(9):1–10, 2016.
5. Han-Min Kim, Gee-Woo Bock, and Gunwoong Lee. Predicting ethereum prices with machine learning based on blockchain information. Expert Systems with Applications, 184:115480, 2021.
6. Evangeline Ducas and Alex Wilner. The security and financial implications of blockchain technologies: Regulating emerging technologies in canada. International Journal, 72(4):538–562, 2017.
7. Peter Gomber, Robert J Kauffman, Chris Parker, and Bruce W Weber. On the fintech revolution: Interpreting the forces of innovation, disruption, and transformation in financial services. Journal of management information systems, 35(1):220–265, 2018.
8. David Koops. Predicting the confirmation time of bitcoin transactions. arXiv preprint arXiv:1809.10596, 2018.

9. Azizah Hitam and Amelia Ritahani Ismail. Comparative performance of machine learning algorithms for cryptocurrency forecasting. Ind. J. Electr. Eng. Comput. Sci, 11(3):1121–1128, 2018.
10. S Yogeshwaran, Maninder Jeet Kaur, and Piyush Maheshwari. Project based learning: predicting bitcoin prices using deep learning. In 2019 IEEE global engineering education conference (EDUCON), pages 1449– 1454. IEEE, 2019.
11. Harsh Jot Singh and Abdelhakim Senhaji Hafid. Transaction confirmation time prediction in ethereum blockchain using machine learning. arXiv preprint arXiv:1911.11592, 2019.
12. Temesgen Awoke, Minakhi Rout, Lipika Mohanty, and Suresh Chandra Satapathy. Bitcoin price prediction and analysis using deep learning models. In Communication Software and Networks: Proceedings of INDIA 2019, pages 631–640. Springer, 2020.
13. Hakan Pabuc¸cu, Serdar Ongan, and Ayse Ongan. Forecasting the movements of bitcoin prices: an application of machine learning algorithms. arXiv preprint arXiv:2303.04642, 2023.
14. Rowel Gundlach, Martijn Gijsbers, David Koops, and Jacques Resing. Predicting confirmation times of bitcoin transactions. ACM SIGMET- RICS Performance Evaluation Review, 48(4):16–19, 2021.
15. Alvin Ho, Ramesh Vatambeti, and Sathish Kumar Ravichandran. Bitcoin price prediction using machine learning and artificial neural network model. Indian Journal of Science and Technology, 14(27):2300–2308, 2021.
16. Nishant Jagannath, Tudor Barbulescu, Karam M Sallam, Ibrahim Elgendi, Asuquo A Okon, Braden McGrath, Abbas Jamalipour, and Kumudu Munasinghe. A self-adaptive deep learning-based algorithm for predictive analysis of bitcoin price. IEEE Access, 9:34054–34066, 2021.
17. Rabia Musheer Aziz, Mohammed Farhan Baluch, Sarthak Patel, and Abdul Hamid Ganie. Lgbm: a machine learning approach for ethereum fraud detection. International Journal of Information Technology, 14(7):3321–3331, 2022.
18. Alex Graves and Alex Graves. Long short-term memory. Supervised sequence labelling with recurrent neural networks, pages 37–45, 2012.
19. Jiuxiang Gu, Zhenhua Wang, Jason Kuen, Lianyang Ma, Amir Shahroudy, Bing Shuai, 19. Ting Liu, Xingxing Wang, Gang Wang, Jianfei Cai, et al. Recent advances in convolutional neural networks. Pattern recognition, 77:354–377, 2018.
20. Alex J Smola and Bernhard Scho¨lkopf. A tutorial on support vector regression. Statistics and computing, 14:199–222, 2004.
21. Padraig Cunningham and Sarah Jane Delany. k-nearest neighbour classifiers-a tutorial. ACM computing surveys (CSUR), 54(6):1–25, 2021.
22. Syeda Sarah Azmi and Shwetha Baliga. An overview of boosting decision tree algorithms utilizing adaboost and xgboost boosting strategies. Int. Res. J. Eng. Technol, 7(5), 2020.
23. Snigdha Sen, Sonali Agarwal, Pavan Chakraborty, and Krishna Pratap Singh. Astronomical big data processing using machine learning: A comprehensive review. Experimental Astronomy, 53(1):1–43, 2022.
24. Lukas Meier, Sara Van De Geer, and Peter Bu¨hlmann. The group lasso for logistic regression. Journal of the Royal Statistical Society Series B: Statistical Methodology, 70(1):53–71, 2008.
25. Arthur E Hoerl and Robert W Kennard. Ridge regression: applications to nonorthogonal problems. Technometrics, 12(1):69–82, 1970.
26. Sanford Weisberg. Applied linear regression, volume 528. John Wiley & Sons, 2005.
27. Sotiris B Kotsiantis. Decision trees: a recent overview. Artificial Intelligence Review, 39:261–283, 2013.
28. Christopher Williams and Carl Rasmussen. Gaussian processes for regression. Advances in neural information processing systems, 8, 1995.
29. Baviskar, V. S., Radha, D., & Sankari, S. U.. Cryptocurrency Price Prediction and Analysis. In 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-7). July,2023.