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ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. AI in Finance
Volume 8 - 2025 | doi: 10.3389/frai.2025.1519805
This article is part of the Research Topic Applications of AI and Machine Learning in Finance and Economics View all 9 articles
Predicting the Bitcoin's Price Using AI
Provisionally accepted
Gil Cohen 1,2*
Avishay Aiche 2- 1 Western Galilee College, Acre, Israel
- 2 Western Galilee College, Acre, Northern District, Israel
This study investigates the application of Artificial Intelligence (AI) and Machine Learning (ML) in predicting Bitcoin price movements and developing adaptive investment strategies. An analysis of Bitcoin performance from January 2018 to January 2024 revealed that the AI-driven strategy, leveraging an ensemble of neural networks, achieved a total return of 1640.32%, significantly surpassing the ML-based approach with a return of 304.77% and the traditional Buy-and-Hold (B&H) strategy at 223.40%. By incorporating predictive analytics and technical indicators, the AI strategy dynamically adjusted its market exposure, enabling it to mitigate losses during downturns and maximize gains during favorable market conditions. These findings underscore the transformative potential of AI in financial markets, particularly in emerging asset classes like cryptocurrencies. Using a broader spectrum of data and employing advanced analytical techniques, AI can provide a more nuanced understanding of market dynamics and investor behavior providing significant implications for portfolio management, risk assessment, and trading system design.
Keywords: bitcoin, AI, machine learning, Cryptocurrencies, algorythm
Received: 30 Oct 2024; Accepted: 04 Apr 2025.
Copyright: © 2025 Cohen and Aiche. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Gil Cohen, Western Galilee College, Acre, Israel
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