Artificial Intelligence Application of Back-propagation Neural Network in Cryptocurrency Price Prediction

Muhammad Sahi, Galan Ramadan Harya Galib

Abstract


This study explores the use of Deep Learning and Artificial Intelligence (AI), particularly Artificial Neural Networks (ANN), for cryptocurrency price prediction. Given the high volatility of crypto markets, traditional models often underperform. A backpropagation-based ANN with a 7-5-1 architecture is proposed and tested using historical Bitcoin data. The model achieves high accuracy, with a Mean Squared Error (MSE) of 4.0431e-04, equivalent to 99.96% accuracy, demonstrating its ability to capture complex nonlinear patterns. However, overfitting remains a concern, emphasizing the need for robust generalization and feature selection. The results validate the potential of ANN in crypto forecasting and encourage further research using diverse features and assets.

Keywords


Artificial Intelligence; Neural Network; Cryptocurrency; Bitcoin

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References


[1] Y. Chen, “Understanding Bitcoin: A Case Study Method to Understand Market Dynamics, Strategies, and Risks of Cryptocurrency,” Adv. Econ. Manag. Polit. Sci., vol. 62, pp. 61–68, 2023.

[2] A. ElBahrawy, L. Alessandretti, A. Kandler, R. Pastor-Satorras, and A. Baronchelli, “Evolutionary dynamics of the cryptocurrency market,” R. Soc. open Sci., vol. 4, no. 11, p. 170623, 2017.

[3] I. Feyzullah, “The Transformative Role of Cryptocurrencies in Modern Finance: Opportunities, Risks, and Future Directions,” in FinTech and Robotics Advancements for Green Finance and Investment, IGI Global Scientific Publishing, 2025, pp. 129–172.

[4] G. Dudek, P. Fiszeder, P. Kobus, and W. Orzeszko, “Forecasting cryptocurrencies volatility using statistical and machine learning methods: A comparative study,” Appl. Soft Comput., vol. 151, p. 111132, 2024.

[5] Y. Wu, Y. Wu, J. M. Guerrero, and J. C. Vasquez, “Decentralized transactive energy community in edge grid with positive buildings and interactive electric vehicles,” Int. J. Electr. Power Energy Syst., vol. 135, p. 107510, 2022.

[6] D. La Torre, F. P. Appio, H. Masri, F. Lazzeri, and F. Schiavone, Impact of Artificial Intelligence in business and society: Opportunities and challenges. Routledge, 2023.

[7] R. Amirzadeh, A. Nazari, and D. Thiruvady, “Applying artificial intelligence in cryptocurrency markets: A survey,” Algorithms, vol. 15, no. 11, p. 428, 2022.

[8] R. Farell, “An analysis of the cryptocurrency industry,” 2015.

[9] S. Jamwal, J. Cano, G. M. Lee, N. H. Tran, and N. Truong, “A survey on ethereum pseudonymity: Techniques, challenges, and future directions,” J. Netw. Comput. Appl., p. 104019, 2024.

[10] M. Ghaemi Asl and D. Roubaud, “Asymmetric interactions among cutting-edge technologies and pioneering conventional and Islamic cryptocurrencies: fresh evidence from intra-day-based good and bad volatilities,” Financ. Innov., vol. 10, no. 1, p. 89, 2024.

[11] S. Aggarwal and N. Kumar, “Cryptocurrencies,” in Advances in Computers, vol. 121, Elsevier, 2021, pp. 227–266.

[12] M. Hashemi Joo, Y. Nishikawa, and K. Dandapani, “Cryptocurrency, a successful application of blockchain technology,” Manag. Financ., vol. 46, no. 6, pp. 715–733, 2020.

[13] M. A. Fauzi, N. Paiman, and Z. Othman, “Bitcoin and cryptocurrency: Challenges, opportunities and future works,” J. Asian Financ. Econ. Bus., vol. 7, no. 8, pp. 695–704, 2020.

[14] A. Seetharaman, A. S. Saravanan, N. Patwa, and J. Mehta, “Impact of Bitcoin as a world currency,” Account. Financ. Res., vol. 6, no. 2, pp. 230–246, 2017.

[15] R. Auer, G. Cornelli, S. Doerr, J. Frost, and L. Gambacorta, “Crypto trading and Bitcoin prices: evidence from a new database of retail adoption,” IMF Econ. Rev., pp. 1–36, 2025.

[16] S. Nakamoto, “Bitcoin: A peer-to-peer electronic cash system. Manubot. 2019; Lemieux, P., Who Is Satoshi Nakamoto,” Regulation, vol. 36, no. 3, 2013.

[17] A. Hairudin, I. M. Sifat, A. Mohamad, and Y. Yusof, “Cryptocurrencies: A survey on acceptance, governance and market dynamics,” Int. J. Financ. Econ., vol. 27, no. 4, pp. 4633–4659, 2022.

[18] A. Fageh and A. K. N. Iman, “Cryptocurrency as Investment in Commodity Futures Trading in Indonesia; Based on Maqās id al-Sharī’ah Approach‖,” J. Huk. Islam, vol. 19, no. 2, pp. 175–192, 2021.

[19] J. McNamara et al., “Characterising wildlife trade market supply-demand dynamics,” PLoS One, vol. 11, no. 9, p. e0162972, 2016.

[20] K.-A. Aivaz, I. F. Munteanu, and F. V. Jakubowicz, “Bitcoin in conventional markets: A study on blockchain-induced reliability, investment slopes, financial and accounting aspects,” Mathematics, vol. 11, no. 21, p. 4508, 2023.

[21] V. Mahalakshmi, N. Kulkarni, K. V. P. Kumar, K. S. Kumar, D. N. Sree, and S. Durga, “The role of implementing artificial intelligence and machine learning technologies in the financial services industry for creating competitive intelligence,” Mater. Today Proc., vol. 56, pp. 2252–2255, 2022.

[22] O. Olubusola, N. Z. Mhlongo, D. O. Daraojimba, A. O. Ajayi-Nifise, and T. Falaiye, “Machine learning in financial forecasting: A US review: Exploring the advancements, challenges, and implications of AI-driven predictions in financial markets,” World J. Adv. Res. Rev., vol. 21, no. 2, pp. 1969–1984, 2024.

[23] C. C. Aggarwal, Neural networks and deep learning, vol. 10, no. 978. Springer, 2018.

[24] H. Poddar, “From neurons to networks: Unravelling the secrets of artificial neural networks and perceptrons,” in Deep Learning in Engineering, Energy and Finance, CRC Press, 2024, pp. 25–79.

[25] R. Verma and D. K. Pandiya, “The Role of AI and Machine Learning in US Financial Market Predictions: Progress, Obstacles, and Consequences,” Int. J. Glob. Innov. Solut., 2024.

[26] M. Sahi, M. Faisal, Y. M. Arif, and C. Crysdian, “Analysis of the Use of Artificial Neural Network Models in Predicting Bitcoin Prices”.

[27] R. Wijaya, “Kinerja keuangan dan ukuran perusahaan terhadap harga saham dengan kebijakan dividen sebagai variabel intervening,” J. Keuang. dan Perbank., vol. 21, no. 3, p. 196434, 2017.

[28] Y. B. Wijaya, S. Kom, and T. A. Napitupulu, “Stock price prediction: comparison of Arima and artificial neural network methods-An Indonesia Stock’s Case,” in 2010 Second International Conference on Advances in Computing, Control, and Telecommunication Technologies, 2010, pp. 176–179.

[29] H. Mulyo, “Statistical Technique Dan Parameter Optimization Pada Neural Network Untuk Forecasting Harga Emas,” J. Disprotek, vol. 7, no. 2, 2016.

[30] A. Radityo, Q. Munajat, and I. Budi, “Prediction of Bitcoin exchange rate to American dollar using artificial neural network methods,” in 2017 International Conference on Advanced Computer Science and Information Systems (ICACSIS), 2017, pp. 433–438.

[31] S. McNally, “Predicting the price of Bitcoin using Machine Learning.” Dublin, National College of Ireland, 2016.

[32] M. Hiransha, E. A. Gopalakrishnan, V. K. Menon, and K. P. Soman, “NSE stock market prediction using deep-learning models,” Procedia Comput. Sci., vol. 132, pp. 1351–1362, 2018.

[33] V. Yadav, P. Verma, and V. Katiyar, “Long short term memory (LSTM) model for sentiment analysis in social data for e-commerce products reviews in Hindi languages,” Int. J. Inf. Technol., vol. 15, no. 2, pp. 759–772, 2023.

[34] A. Aggarwal, I. Gupta, N. Garg, and A. Goel, “Deep learning approach to determine the impact of socio economic factors on bitcoin price prediction,” in 2019 twelfth international conference on contemporary computing (IC3), 2019, pp. 1–5.

[35] A. Greaves and B. Au, “Using the bitcoin transaction graph to predict the price of bitcoin,” No data, vol. 8, pp. 416–443, 2015.

[36] Z. Jiang and J. Liang, “Cryptocurrency portfolio management with deep reinforcement learning,” in 2017 Intelligent systems conference (IntelliSys), 2017, pp. 905–913.

[37] I. Madan, S. Saluja, and A. Zhao, “Automated bitcoin trading via machine learning algorithms,” URL http//cs229. stanford. edu/proj2014/Isaac% 20Madan, vol. 20, 2015.

[38] C. Krauss, X. A. Do, and N. Huck, “Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500,” Eur. J. Oper. Res., vol. 259, no. 2, pp. 689–702, 2017.

[39] K. Żbikowski, “Using volume weighted support vector machines with walk forward testing and feature selection for the purpose of creating stock trading strategy,” Expert Syst. Appl., vol. 42, no. 4, pp. 1797–1805, 2015.

[40] I. M. Baytas, C. Xiao, X. Zhang, F. Wang, A. K. Jain, and J. Zhou, “Patient subtyping via time-aware LSTM networks,” in Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, 2017, pp. 65–74.

[41] A. Subiyakto, A. R. Ahlan, M. Kartiwi, and S. J. Putra, “Measurement of the information system project success of the higher education institutions in Indonesia: a pilot study,” Int. J. Bus. Inf. Syst., vol. 23, no. 2, pp. 229–247, 2016.

[42] A. Z. Ausop and E. S. N. Aulia, “Teknologi Cryptocurrency Bitcoin Dalam Transaksi Bisnis Menurut Syariat Islam,” J. Sosioteknologi, vol. 17, no. 1, pp. 74–92, 2018.

[43] L. Fausett, “Fundamentals of Neural Networks: Architectures, Applications and Algorithms.” Prentice-Hall International, Inc, 1994.

[44] X. Ying, “An overview of overfitting and its solutions,” in Journal of physics: Conference series, 2019, vol. 1168, p. 22022.

[45] B. Badieah, R. Gernowo, and B. Surarso, “Metode Jaringan Syaraf Tiruan Untuk Prediksi Performa Mahasiswa Pada Pembelajaran Berbasis Problem Based Learning (PBL),” JSINBIS (Jurnal Sist. Inf. Bisnis), vol. 6, no. 1, pp. 46–58, 2016.




DOI: https://doi.org/10.18860/ijeie.v1i1.33800

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