通过社会情绪与技术特征的状态感知多模态融合预测比特币价格走势
Bitcoin Price Direction Prediction via Regime-Aware Multi-Modal Fusion of Social Sentiment and Technical Features
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中文总结 AI 辅助
研究比特币亚日价格预测难题，提出状态感知多模态学习（RAML）方法，依据市场状态动态融合社会情绪与技术特征，实验表明该方法在预测中表现良好，确立状态条件自适应融合为多模态金融预测的必要原则。
中文摘要 AI 辅助
比特币在亚日时间尺度上的价格预测是计算金融领域一个棘手的开放问题。比特币呈现出厚尾回报、非平稳动态，其价格发现过程受Reddit和Twitter上的社会话语影响。传统方法通过静态拼接将OHLCV技术特征与情绪融合，无论市场状态都应用相同的融合权重，这与行为金融文献不符。本文提出状态感知多模态学习（RAML），基于动态检测的二元市场状态对情绪和价格特征进行融合。滚动24小时波动率将观察结果分为稳定和波动状态；一个可学习的 sigmoid 门调整情绪嵌入相对于价格嵌入的权重，在波动期间更信任情绪，在稳定阶段更信任价格动态。该系统在3491个每小时观察值（2024年7月 - 2025年9月）上进行评估，结合比特币OHLCV数据与Reddit /r/Bitcoin FinBERT情绪。比较了四个模型——仅价格的BiLSTM、仅情绪的分类器、静态拼接BiLSTM和RAML——在3小时和6小时的预测范围内，并进行了消融研究以分离情绪分支、状态检测和自适应融合。RAML在3小时达到宏观F1为0.5474，6小时为0.5513，3小时时AUC最高为0.5084，表明校准更好。消融证实每个组件都是必要的，用拼接代替自适应加权会导致6小时时召回率崩溃（F1：0.14）。这些结果确立了状态条件自适应融合作为多模态金融预测的必要设计原则。
英文摘要
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.
发表机构
- FAST National University of Computer and Emerging Sciences (FAST-NUCES)（快速国立计算机与新兴科学大学）
机构由 AI 辅助整理，请以论文原文为准。
