A Multisource Fusion Framework for Cryptocurrency Price Movement Prediction
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2024
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| author | Dashtaki, Saeed Mohammadi Dashtaki, Reza Mohammadi Chagahi, Mehdi Hosseini Moshiri, Behzad Piran, Md. Jalil |
| author_facet | Dashtaki, Saeed Mohammadi Dashtaki, Reza Mohammadi Chagahi, Mehdi Hosseini Moshiri, Behzad Piran, Md. Jalil |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_18895 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Multisource Fusion Framework for Cryptocurrency Price Movement Prediction Dashtaki, Saeed Mohammadi Dashtaki, Reza Mohammadi Chagahi, Mehdi Hosseini Moshiri, Behzad Piran, Md. Jalil Machine Learning Artificial Intelligence 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. |
| title | A Multisource Fusion Framework for Cryptocurrency Price Movement Prediction |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2409.18895 |