基於狀態感知的多模態融合社交情緒與技術特徵之比特幣價格方向預測

摘要

比特幣價格在亞日時間尺度下的預測是計算金融領域中一個艱鉅的開放性問題。比特幣表現出厚尾收益率、非平穩動態，以及受Reddit和Twitter社交討論影響的價格發現過程。傳統方法透過靜態拼接將OHLCV技術特徵與情緒特徵融合，無論市場狀態為何皆採用相同的融合權重。這與行為金融學文獻不一致——後者顯示零售情緒在波動期最具預測力，而在平穩期則充滿雜訊。本文提出「狀態感知多模態學習」（Regime-Aware Multi-Modal Learning，RAML），該方法將情緒與價格特徵的融合條件設定為動態偵測的二元市場狀態。滾動24小時波動率將觀測值劃分為穩定狀態與波動狀態；一個可學習的S型門控會根據價格嵌入相對於情緒嵌入的權重進行調整，在波動期間更信任情緒訊號，在穩定階段則更依賴價格動態。該系統在3491個小時級觀測值（2024年7月至2025年9月）上進行評估，結合比特幣OHLCV數據與Reddit /r/Bitcoin的FinBERT情緒分析。比較了四種模型——僅使用價格的BiLSTM、僅使用情緒的分類器、靜態拼接BiLSTM，以及RAML——針對3小時與6小時預測區間，並搭配消融研究來分離情緒分支、狀態偵測與自適應融合。RAML在3小時取得宏平均F1值0.5474，在6小時取得0.5513，且3小時的AUC最高（0.5084），顯示校準效果更佳。消融實驗證實每個組件皆不可或缺，若以拼接取代自適應加權，將導致6小時預測的召回率崩潰（F1值：0.14）。這些結果確立了「以狀態為條件的自適應融合」作為多模態金融預測的必要設計原則。

English

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.