# A Multisource Fusion Framework for Cryptocurrency Price Movement Prediction

arXiv: https://arxiv.org/abs/2409.18895 · HTML: https://mlsanity.com/paper/2409.18895/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 38 references that are in the map.
- First posted on arXiv 27 Sep 2024.

## Builds on (most cited first, 5 of 5)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://mlsanity.com/paper/1908.10063/) (2019)
- [BABD: A Bitcoin Address Behavior Dataset for Pattern Analysis](https://mlsanity.com/paper/2204.05746/) (2022)
- [Algorithmic Trading Using Continuous Action Space Deep Reinforcement Learning](https://mlsanity.com/paper/2210.03469/) (2022)
- [Forecasting the movements of Bitcoin prices: an application of machine learning algorithms](https://mlsanity.com/paper/2303.04642/) (2023)

## Abstract (arXiv)

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.