Comparing the Results of Neural Networks in Predicting Cryptocurrency Prices Under Risk Conditions
Keywords:
RBF, Bitcoin, MLP, artificial neural network, price predictionAbstract
This study aimed to compare the performance of different neural networks in predicting cryptocurrency prices, with a focus on Bitcoin, under risk conditions. The research was applied and descriptive-analytical in nature. Historical data of Bitcoin, Ethereum, and Ripple from 2018 to 2023 were collected from reliable exchanges and financial platforms. After preprocessing, the data were divided into training and testing sets. Two neural network models, Multilayer Perceptron (MLP) and Radial Basis Function (RBF), were designed and implemented. Model performance was evaluated using error measurement indices including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R²). The results indicated that the MLP model outperformed the RBF model in prediction accuracy. The coefficient of determination reached 99.5% for MLP and 98.9% for RBF. Comparative analysis of error metrics showed significant reductions in RMSE and MAE in the MLP model compared to RBF. Moreover, the proposed Multi-Input Deep Learning (MICDL) model surpassed comparative CNN-LSTM models in classifying short-term price movements, particularly in directional prediction accuracy. Neural networks, especially the MLP model, demonstrated high accuracy and flexibility in forecasting cryptocurrency price fluctuations. These models can serve as effective tools for investors, analysts, and policymakers in risk management and informed decision-making. However, incorporating more diverse and real-time data along with hybrid deep learning techniques could further enhance predictive performance in future studies.
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Aboutalebi, A., Rafiei Attani, A., & Khanizad, R. (2020). Comparative Study of the Efficiency of Some Artificial Intelligence Algorithms for Cryptocurrency Price Prediction Iran University of Science and Technology].
Abusaleh Mohammadsharifi, K., Khalil Damghani, F., & Sardar, S. (2021). Predicting Bitcoin price using a hybrid ARIMA and deep learning model. Quarterly Journal of Industrial Management Studies, 19(61), 125-146.
Akouaouch, I., & Bouayad, A. (2025). A new deep learning approach for predicting high -frequency short -term cryptocurrency price. Bulletin of Electrical Engineering and Informatics, 14(1), 513-523. https://doi.org/https://doi.org/10.11591/eei.v14i1.7377
Al -Aydi, H., Talebian, S. R., & Nikoo Ghadam, M. (2019). Predicting the Price of Bitcoin Virtual Currency Using a Neural Network Improved with a Genetic Algorithm Imam Reza International University].
Alnami, H., Mohzary, M., Assiri, B., & Zangoti, H. (2025). An Integrated Framework for Cryptocurrency Price Forecasting and Anomaly Detection Using Machine Learning. Applied Sciences, 15(4), 1864. https://doi.org/https://doi.org/10.3390/app15041864
Azizi, Z. (2022). Management and analysis of cryptocurrency and Bitcoin price prediction using deep neural networks in the blockchain context. Journal of New Research in Entrepreneurship and Business Development Management, 3(2).
Crisostomo, J., Bacao, F., & Lobo, V. (2025). Machine learning methods for detecting smart contracts vulnerabilities within Ethereum blockchain− A review. Expert Systems with Applications. https://doi.org/https://doi.org/10.1016/j.eswa.2024.126353
Cv, A., Negi, N., & Aprem, A. (2025). Bayesian machine learning framework for characterizing structural dependency, dynamics, and volatility of cryptocurrency market using potential field theory.
Darvish Motavalli, M. H., & Amini, S. (2023). Presenting a model for predicting cryptocurrency prices under environmental uncertainty with a fuzzy artificial neural network. Journal of Computational Economics, 2(2), 135-157.
Das, B. C., Sarker, B., Saha, A., Bishnu, K. K., Sartaz, M. S., Hasanuzzaman, M., & Khan, M. M. (2025). Detecting Cryptocurrency Scams in the USA: A Machine Learning -Based Analysis of Scam Patterns and Behaviors. Journal of Ecohumanism, 4(2), 2091-2111. https://doi.org/https://doi.org/10.62754/joe.v4i2.6604
Ghorbani, A., Rabbani, Y., Kamran Rad, R., & Falsafi, P. (2022). Predicting Bitcoin price changes using sentiment analysis on social networks and celebrities, along with a data mining approach. Journal of Econometric Modeling, 7(3), 163-182.
Hashimian, S. F., & Akhavan Niaki, S. T. (2020). Analysis and Prediction of Cryptocurrency Prices Using Time Series Analysis and Machine Learning Sharif University of Technology].
Ibrahim, B. A., Elamer, A. A., & Abdou, H. A. (2025). The role of cryptocurrencies in predicting oil prices pre and during COVID -19 pandemic using machine learning. Annals of Operations Research, 345(2), 909-952. https://doi.org/https://doi.org/10.1007/s10479 -022 -05024 -4
Ibrahimpour Moghadam, S. M., & Haji Pour, A. (2019). Bitcoin Price Prediction Using Support Vector Machine (SVM) with the Help of the Grey Wolf Algorithm Hakim Sabzevari University].
Islam, M. S., Bashir, M., Rahman, S., Al Montaser, M. A., Bortty, J. C., Nishan, A., & Haque, M. R. (2025). Machine Learning -Based Cryptocurrency Prediction: Enhancing Market Forecasting with Advanced Predictive Models. Journal of Ecohumanism, 4(2), 2498-2519. https://doi.org/https://doi.org/10.62754/joe.v4i2.6663
Islam, M. Z., Islam, M. S., Al Montaser, M. A., Rasel, M. A. B., Bhowmik, P. K., & Dalim, H. M. (2024). Evaluating the effectiveness of machine learning algorithms in predicting cryptocurrency prices under market volatility: A study based on the usa financial market. The American Journal of Management and Economics Innovations, 6(12), 15-38. https://doi.org/https://doi.org/10.37547/tajmei/Volume06Issue12 -03
Jahanbin, K., & Chahooki, M. A. Z. (2025). Cryptocurrency Trend Prediction Through Hybrid Deep Transfer Learning. International Journal of Intelligent Systems, 2025(1), 4211799. https://doi.org/https://doi.org/10.1155/int/4211799
Khonsarian, F., Timourpour, B., & Rostegar, M. A. (2020). Forecasting Price and Portfolio Selection Model for Financial Assets and Cryptocurrencies Tarbiat Modares University].
Kiranmai Balijepalli, N. S. S., & Thangaraj, V. (2025). Prediction of cryptocurrency's price using ensemble machine learning algorithms. European Journal of Management and Business Economics. https://doi.org/https://doi.org/10.1108/EJMBE -08 -2023 -0244
Lari, S., & Nikseresht, A. (2020). Increasing the accuracy of cryptocurrency price volatility prediction using user sentiment analysis. Proceedings of the Third International Conference on Interdisciplinary Research in Electrical, Computer, Mechanical, and Mechatronics Engineering in Iran and the Islamic World,
Nouira, A. Y., Bouchakwa, M., & Amara, M. (2024). Role of social networks and machine learning techniques in cryptocurrency price prediction: a survey. Social Network Analysis and Mining, 14(1), 152. https://doi.org/https://doi.org/10.1007/s13278 -024 -01316 -8
Parvini, N., Fazilzadeh, A., & Jabbarzadeh, Y. (2019). Cryptocurrency Price Prediction Using AI -Based Forecasting Models University of Tabriz].
Sayadi Nejad, S., Ismailzadeh, A., & Rostami, M. R. (2023). Presenting a Bitcoin return prediction model using a hybrid deep learning -signal decomposition algorithm (CEEMD -DL). Journal of Financial Economics, 17(62), 217-238.
Shahnavazi, B., Zamanian, G., & Hatifi Majoumard, M. (2022). Evaluation of Bitcoin price index volatility on economic development using univariate GARCH models. Journal of Iranian Economic Development Analyses, 8(1), 133-152.
Shivaei, V., Pazhouhan, M. R., & Yazdian Dehkordi, M. (2019). Forecasting Cryptocurrency Price Using Hybrid Machine Learning Methods Yazd University].
Song, D. (2025). Machine Learning for Price Prediction and Risk -Adjusted Portfolio Optimization in Cryptocurrencies. https://doi.org/https://doi.org/10.4018/979 -8 -3693 -8186 -1.ch013
Tabyanian, H., & Farsian, H. (2020). A systematic review of digital currency: Predicting the future of Bitcoin in 2021. Proceedings of the Fifth International Conference on Modern Management and Accounting Studies in Iran,
Torabi, B., Fazalali, M., & Farahani, B. (2020). An Approach to Bitcoin Price Time Series Forecasting Using High -Dimensional Features Shahid Beheshti University].
Torabi, M. A. (2021). A systematic review of digital currency: Predicting the future of Bitcoin in 2022. Journal of Smart Marketing Management, 2(5), 1-14.
Varastehpour, S., Bakhit, S., Salem, M., Rabiei, M., & Abdol Abadi, A. (2019). Forecasting the Price of the First Virtual Cryptocurrency Based on Machine Learning Techniques.
Viéitez, A., Santos, M., & Naranjo, R. (2024). Machine learning Ethereum cryptocurrency prediction and knowledge -based investment strategies. Knowledge -Based Systems, 299. https://doi.org/https://doi.org/10.1016/j.knosys.2024.112088
Yeganeh, A., Hu, X., Shongwe, S. C., & Koning, F. F. (2025). Using the attention layer mechanism in construction of a novel ratio control chart: An application to Ethereum price prediction and automated trading strategy. Engineering Applications of Artificial Intelligence, 141. https://doi.org/https://doi.org/10.1016/j.engappai.2024.109652
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Copyright (c) 2025 Mohammad Danial Jahed (Author); Zadollah Fathi (Corresponding author); Gholamreza Zomorodian (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
