A Multisource Fusion Framework for Cryptocurrency Price Movement Prediction

• URL: http://arxiv.org/abs/2409.18895v2
• Date: Mon, 25 Aug 2025 16:29:47 GMT
• Title: A Multisource Fusion Framework for Cryptocurrency Price Movement Prediction
• Authors: Saeed Mohammadi Dashtaki, Reza Mohammadi Dashtaki, Mehdi Hosseini Chagahi, Behzad Moshiri, Md. Jalil Piran,
• Abstract summary: This study proposes a multisource fusion framework that integrates quantitative financial indicators, such as historical prices and technical indicators, with qualitative sentiment signals derived from X (formerly Twitter)<n> Experimental results on a large-scale Bitcoin dataset demonstrate that the proposed approach substantially outperforms single-source models.
• Score: 5.252967226385235
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Predicting cryptocurrency price trends remains a major challenge due to the volatility and complexity of digital asset markets. Artificial intelligence (AI) has emerged as a powerful tool to address this problem. This study proposes a multisource fusion framework that integrates quantitative financial indicators, such as historical prices and technical indicators, with qualitative sentiment signals derived from X (formerly Twitter). Sentiment analysis is performed using Financial Bidirectional Encoder Representations from Transformers (FinBERT), a domain-specific BERT-based model optimized for financial text, while sequential dependencies are captured through a Bidirectional Long Short-Term Memory (BiLSTM) network. Experimental results on a large-scale Bitcoin dataset demonstrate that the proposed approach substantially outperforms single-source models, achieving an accuracy of approximately 96.8\%. The findings underscore the importance of incorporating real-time social sentiment alongside traditional indicators, thereby enhancing predictive accuracy and supporting more informed investment decisions.

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