Abstract
Cryptocurrencies like Bitcoin are a contentious and difficult technological innovation in today’s financial system. With huge improvements in financial markets, machine learning and artificial intelligence aided trading have piqued interest in recent years. This study suggests a predicting model for blockchain Bitcoin cryptocurrency prices and its profitability trading strategies using machine learning algorithms (ICA-Firefly and SVMs). For the prediction analysis of Bitcoin cryptocurrency data, this study combines ICA-Firefly with SVM algorithms. The model was tested on a large dataset of 2194 samples, and its performance was analyzed in terms of evaluation metrics. In evaluation to the state of the art, the ICA-Firefly with L-SVM and Sigmoid SVM classification approach performs well on the Bitcoin sample dataset, with an accuracy of 95% and 97%, respectively. The ICA-Firefly with the SVM model can be adopted as a viable financial system sustainability management strategy.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Podgorelec, B., Turkanović, M., & Karakatič, S. (2019). A machine learning-based method for automated Blockchain transaction signing including personalized anomaly detection. Sensors, 20, 147. https://doi.org/10.3390/s20010147
Żbikowski, K. (2016). Application of machine learning algorithms for bitcoin automated trading. Presented at the (2016). https://doi.org/10.1007/978-3-319-30315-4_14.
Gulihar, P., & Gupta, B. B. (2019). A taxonomy of bitcoin security issues and defense mechanisms. In Machine learning for computer and cyber security (pp. 209–232). CRC Press. https://doi.org/10.1201/9780429504044-9. Taylor & Francis Group, [2019] | “A science publishers book”.
Pabuçcu, H., Ongan, S., & Ongan, A. (2020). Forecasting the movements of bitcoin prices: An application of machine learning algorithms. Quantitative Finance and Economics, 4, 679–692. https://doi.org/10.3934/QFE.2020031
Li, X., & Wang, C. A. (2017). The technology and economic determinants of cryptocurrency exchange rates: The case of bitcoin. Decision Support Systems, 95, 49–60. https://doi.org/10.1016/j.dss.2016.12.001
Casino, F., Dasaklis, T. K., & Patsakis, C. (2019). A systematic literature review of blockchain-based applications: Current status, classification and open issues. Telematics and Informatics, 36, 55–81. https://doi.org/10.1016/j.tele.2018.11.006
Arowolo, M. O., Adebiyi, M. O., Ariyo, A. A., & Okesola, O. J. (2021). A genetic algorithm approach for predicting ribonucleic acid sequencing data classification using KNN and decision tree. TELKOMNIKA (Telecommunication Computing Electronics and Control), 19, 310. https://doi.org/10.12928/telkomnika.v19i1.16381
Alabi, K.O., Abdulsalam, S.O., Ogundokun, R.O., Arowolo, M.O. (2021). Credit risk prediction in commercial Bank using Chi-Square with SVM-RBF. Presented at the (2021). https://doi.org/10.1007/978-3-030-69143-1_13.
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, 5427–5437. https://doi.org/10.1109/ACCESS.2017.2779181
Gupta, A., Nain, H. (2021). Bitcoin price prediction using time series analysis and machine learning techniques. Presented at the (2021). https://doi.org/10.1007/978-981-15-7106-0_54.
Ampountolas, A., Nyarko Nde, T., Date, P., & Constantinescu, C. (2021). A machine learning approach for micro-credit scoring. Risks, 9, 50. https://doi.org/10.3390/risks9030050
Fan, M.-H., Chen, M.-Y., & Liao, E.-C. (2021). A deep learning approach for financial market prediction: Utilization of Google trends and keywords. Granular Computing, 6, 207–216. https://doi.org/10.1007/s41066-019-00181-7
Gerlein, E. A., McGinnity, M., Belatreche, A., & Coleman, S. (2016). Evaluating machine learning classification for financial trading: An empirical approach. Expert Systems with Applications, 54, 193–207. https://doi.org/10.1016/j.eswa.2016.01.018
Huang, J., Chai, J., & Cho, S. (2020). Deep learning in finance and banking: A literature review and classification. Frontiers of Business Research in China, 14, 13. https://doi.org/10.1186/s11782-020-00082-6
Khalid Salman, M., & Abdu Ibrahim, A. (2020). Price prediction of different cryptocurrencies using technical trade indicators and machine learning. IOP Conference Series: Materials Science and Engineering, 928, 032007. https://doi.org/10.1088/1757-899X/928/3/032007
Sebastião, H., & Godinho, P. (2021). Forecasting and trading cryptocurrencies with machine learning under changing market conditions. Financial Innovation, 7, 3. https://doi.org/10.1186/s40854-020-00217-x
Zhengyang, W., Xingzhou, L., Jinjin, R., & Jiaqing, K. (2019). Prediction of cryptocurrency price dynamics with multiple machine learning techniques. In Proceedings of the 2019 4th International Conference on Machine Learning Technologies – ICMLT 2019 (pp. 15–19). ACM Press. https://doi.org/10.1145/3340997.3341008
Koker, T. E., & Koutmos, D. (2020). Cryptocurrency trading using machine learning. Journal of Risk and Financial Management, 13, 178. https://doi.org/10.3390/jrfm13080178
Cho, H., Lee, K.-H., Kim, C. (2021). Machine learning and cryptocurrency in the financial markets. Presented at the (2021). https://doi.org/10.1007/978-981-33-6137-9_13.
Cocco, L., Tonelli, R., & Marchesi, M. (2021). Predictions of bitcoin prices through machine learning based frameworks. Computer Science – PeerJ, 7, e413. https://doi.org/10.7717/peerj-cs.413
Alessandretti, L., ElBahrawy, A., Aiello, L. M., & Baronchelli, A. (2018). Anticipating cryptocurrency prices using machine learning. Complexity, 2018, 1–16. https://doi.org/10.1155/2018/8983590
Lahmiri, S., & Bekiros, S. (2021). Deep learning forecasting in cryptocurrency high-frequency trading. Cognitive Computation, 13, 485–487. https://doi.org/10.1007/s12559-021-09841-w
Jameel, F., Javaid, U., Khan, W. U., Aman, M. N., Pervaiz, H., & Jäntti, R. (2020). Reinforcement learning in Blockchain-enabled IIoT networks: A survey of recent advances and open challenges. Sustainability, 12, 5161. https://doi.org/10.3390/su12125161
Tanwar, S., Bhatia, Q., Patel, P., Kumari, A., Singh, P. K., & Hong, W.-C. (2020). Machine learning adoption in Blockchain-based smart applications: The challenges, and a way forward. IEEE Access, 8, 474–488. https://doi.org/10.1109/ACCESS.2019.2961372
Gupta, R., Tanwar, S., Al-Turjman, F., Italiya, P., Nauman, A., & Kim, S. W. (2020). Smart contract privacy protection using AI in cyber-physical systems: Tools, techniques and challenges. IEEE Access., 8, 24746–24772. https://doi.org/10.1109/ACCESS.2020.2970576
Liu, Y., Yu, F. R., Li, X., Ji, H., & Leung, V. C. M. (2020). Blockchain and machine learning for communications and networking systems. IEEE Communication Surveys and Tutorials, 22, 1392–1431. https://doi.org/10.1109/COMST.2020.2975911
Shahbazi, Z., & Byun, Y.-C. (2021). Integration of Blockchain, IoT and machine learning for multistage quality control and enhancing security in smart manufacturing. Sensors, 21, 1467. https://doi.org/10.3390/s21041467
Jaquart, P., Dann, D., & Weinhardt, C. (2021). Short-term bitcoin market prediction via machine learning. The Journal of Financial Data Science, 7, 45–66. https://doi.org/10.1016/j.jfds.2021.03.001
Sharma, M. P., Bhardwaj, A. V. V., Sharma, V., Iqbal, A. P., & Kumar, R. (2020). Prediction of the price of Ethereum blockchain cryptocurrency in an industrial finance system. Computers and Electrical Engineering, 81, 106527. https://doi.org/10.1016/j.compeleceng.2019.106527
Weng, J., Weng, J., Zhang, J., Li, M., Zhang, Y., & Luo, W. (2019). DeepChain: Auditable and privacy-preserving deep learning with Blockchain-based incentive. IEEE Transactions on Dependable and Secure Computing, 1–1. https://doi.org/10.1109/TDSC.2019.2952332
Negar, M., Alireza, N., Masoud, R., & Yasser, Z. (2020). Bitcoin price prediction based on other cryptocurrencies using machine learning and time series analysis. Iranian Journal of Science and Technology. https://doi.org/10.24200/SCI.2020.55034.4040
Reddy, G. T., Reddy, M. P. K., Lakshmanna, K., Kaluri, R., Rajput, D. S., Srivastava, G., & Baker, T. (2020). Analysis of dimensionality reduction techniques on big data. IEEE Access, 8, 54776–54788. https://doi.org/10.1109/ACCESS.2020.2980942
Awotunde, J.B., Ogundokun, R.O., Jimoh, R.G., Misra, S., Aro, T.O. (2021). Machine learning algorithm for cryptocurrencies price prediction. Presented at the (2021). https://doi.org/10.1007/978-3-030-72236-4_17.
Vaddi, L. (2020). Predicting crypto currency prices using machine learning and deep learning techniques. International Journal of Advanced Trends in Computer Science and Engineering, 9, 6603–6608. https://doi.org/10.30534/ijatcse/2020/351942020
Tharwat, A. (2021). Independent component analysis: An introduction. Applied Computing and Informatics, 17, 222–249. https://doi.org/10.1016/j.aci.2018.08.006
Maghrebi, H., Prouff, E. (2018). On the use of independent component analysis to denoise side-channel measurements. Presented at the (2018). https://doi.org/10.1007/978-3-319-89641-0_4.
Gadekallu, T. R., Khare, N., Bhattacharya, S., Singh, S., Maddikunta, P. K. R., Ra, I.-H., & Alazab, M. (2020). Early detection of diabetic retinopathy using PCA-firefly based deep learning model. Electronics, 9, 274. https://doi.org/10.3390/electronics9020274
Veysel, A., Ahmet, N., & T., Farah, Hatem, K., Bashar, Ahmed, K. (2020). Wrapper feature selection approach based on binary firefly algorithm for spam E-mail filtering. Journal of Soft Computing and Data Mining, 1, 44–52. https://doi.org/10.30880/jscdm.2020.01.02.005
Ferdiansyah, F., Negara, E. S., & Widyanti, Y. (2019). Bitcoin-USD trading using SVM to detect the current DAY’S trend in the market. Journal of Information Systems and Informatics, 1, 70–77. https://doi.org/10.33557/journal-isi.v1i1.7
Ali Alahmari, S. (2020). PREDICTING THE PRICE OF CRYPTOCURRENCY USING SUPPORT VECTOR REGRESSION METHODS. J. Mech. Contin. The Mathematical Scientist, 15. https://doi.org/10.26782/jmcms.2020.04.00023
Majeed, Y., Zhang, S., & Ren. (2021). A big data-driven framework for sustainable and smart additive manufacturing. Robotics and Computer-Integrated Manufacturing, 67, 102026. https://doi.org/10.1016/j.rcim.2020.102026
Mudassir, M., Bennbaia, S., Unal, D., & Hammoudeh, M. (2020). Time-series forecasting of bitcoin prices using high-dimensional features: A machine learning approach. Neural Computing and Applications. https://doi.org/10.1007/s00521-020-05129-6
Li, Y., & Dai, W. (2020). Bitcoin price forecasting method based on CNN-LSTM hybrid neural network model. Journal of Engineering, 2020, 344–347. https://doi.org/10.1049/joe.2019.1203
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Arowolo, M.O., Ayegba, P., Yusuff, S.R., Misra, S. (2022). A Prediction Model for Bitcoin Cryptocurrency Prices. In: Misra, S., Kumar Tyagi, A. (eds) Blockchain Applications in the Smart Era. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-89546-4_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-89546-4_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-89545-7
Online ISBN: 978-3-030-89546-4
eBook Packages: EngineeringEngineering (R0)