Bitcoin Price Direction Prediction via Regime-Aware Multi-Modal Fusion of Social Sentiment and Technical Features
基于市场状态的社交情绪与技术特征融合：比特币价格方向预测AI 解读 ↓
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基于市场状态的社交情绪与技术特征融合：比特币价格方向预测
- 研究问题
- 在比特币小时级方向预测中，依据市场波动状态动态调整社交情绪与价格特征的权重，能否改善预测表现？
- 方法与思路
- 作者用滚动二十四小时波动率划分稳定与波动状态，以可学习门控融合价格和 Reddit 情绪表示，并开展模型比较与消融。
- 材料陈述的贡献
- 摘要报告动态融合模型在所用样本与预测期限上的部分指标较优，并据消融结果主张市场状态和自适应融合具有作用。
- 阅读关注点
- 应核对时间切分、信息可用时点、样本覆盖与指标不确定性；尤其应区分 AUC 的区分能力与概率校准表现。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核实；摘要不足以验证无信息泄漏、概率校准或动态融合的普遍必要性。
原文摘要
Bitcoin price prediction on sub-daily timescales is a hard open problem in computational finance. Bitcoin exhibits fat-tailed returns, non-stationary dynamics, and a price discovery process influenced by social discourse on Reddit and Twitter. Conventional approaches fuse OHLCV technical features with sentiment via static concatenation, applying identical fusion weights regardless of market state. This is inconsistent with the behavioural finance literature, which shows that retail sentiment is most predictive during volatile periods and noisy during calm ones. This paper proposes Regime-Aware Multi-Modal Learning (RAML), which conditions fusion of sentiment and price features on a dynamically detected binary market regime. Rolling 24-hour volatility partitions observations into stable and volatile regimes; a learnable sigmoid gate adjusts the weight of the sentiment embedding relative to the price embedding, trusting sentiment more during volatility and price dynamics more during stable phases. The system is evaluated on 3,491 hourly observations (July 2024-September 2025), combining Bitcoin OHLCV data with Reddit /r/Bitcoin FinBERT sentiment. Four models are compared - price-only BiLSTM, sentiment-only classifier, static-concatenation BiLSTM, and RAML - across 3-hour and 6-hour horizons, with an ablation study isolating the sentiment branch, regime detection, and adaptive fusion. RAML achieves macro-F1 of 0.5474 (3h) and 0.5513 (6h), with the highest AUC at 3 hours (0.5084), indicating better calibration. Ablation confirms every component is necessary, and replacing adaptive weighting with concatenation causes recall collapse at 6 hours (F1: 0.14). These results establish regime-conditioned adaptive fusion as a necessary design principle for multi-modal financial forecasting.