Bitcoin Price Direction Prediction via Regime-Aware Multi-Modal Fusion of Social Sentiment and Technical Features

Analysis

Why This Paper Matters

Bitcoin price prediction on sub-daily timescales remains a hard open problem due to fat-tailed returns, non-stationary dynamics, and the influence of social discourse. While prior work has fused OHLCV technical features with social sentiment (e.g., from Reddit and Twitter), most approaches use static concatenation, applying identical fusion weights regardless of market state. This paper identifies a critical flaw in that design: behavioral finance literature shows retail sentiment is most predictive during volatile periods and noisy during calm ones. By proposing a regime-aware adaptive fusion mechanism, the paper directly addresses this mismatch, offering a principled way to modulate modality importance based on market conditions.

From a practitioner's perspective, this work is significant because it moves beyond one-size-fits-all fusion strategies. The ablation study starkly demonstrates that static concatenation can cause recall collapse at longer horizons (F1 drops to 0.14 at 6 hours), while the adaptive approach maintains performance. This suggests that many existing multi-modal financial models may be suboptimal, especially for longer prediction windows.

Technical Contributions

• Regime-Aware Multi-Modal Learning (RAML): A framework that conditions fusion of sentiment and price features on a dynamically detected binary market regime (stable vs. volatile).
• Rolling 24-hour volatility partition: A simple yet effective method to classify each observation into a regime based on recent price volatility.
• Learnable sigmoid gate: A differentiable mechanism that adjusts the weight of the sentiment embedding relative to the price embedding, trusting sentiment more during volatility and price dynamics more during stable phases.
• BiLSTM backbone: Both price-only and sentiment-only baselines use bidirectional LSTMs, with RAML fusing their outputs via the adaptive gate.
• Ablation study: Isolates the sentiment branch, regime detection, and adaptive fusion, confirming each component is necessary for the reported performance.

Results

RAML is evaluated on 3,491 hourly observations from July 2024 to 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.

• Macro-F1: RAML achieves 0.5474 (3h) and 0.5513 (6h), outperforming all baselines.
• AUC: Highest at 3 hours (0.5084), indicating better calibration than alternatives.
• Ablation: Replacing adaptive weighting with concatenation causes recall collapse at 6 hours (F1: 0.14), confirming the necessity of regime-aware fusion.
• Comparison: Static concatenation BiLSTM yields lower F1 and AUC, especially at longer horizons, highlighting the fragility of fixed fusion.

Significance

This paper establishes regime-conditioned adaptive fusion as a necessary design principle for multi-modal financial forecasting. The core insight—that modality relevance varies with market state—is likely transferable to other domains where context determines which data sources are most informative (e.g., medical diagnosis, autonomous driving). For AI practitioners, the work provides a concrete, implementable architecture (rolling volatility detection + sigmoid gate) that can be integrated into existing multi-modal pipelines. The ablation results serve as a cautionary tale: static fusion can actively harm performance, especially when modality reliability shifts over time. Future work could extend this to multi-class regimes, other asset classes, or more sophisticated volatility estimators.