Crypto Noise vs. Signal: A Hybrid Sentiment-Time Series Framework for Predicting Short-Term Bitcoin Volatility

Abstract

Cryptocurrency markets, particularly Bitcoin, are characterized by rapid and unpredictable price swings driven by speculative trading, macroeconomic developments, and collective sentiment expressed through digital channels. Traditional forecasting approaches that rely solely on technical indicators or econometric models often overlook the nuanced emotional and behavioral signals embedded in online discourse. This study introduces a hybrid forecasting framework that unites sentiment analysis with advanced time series modeling to predict short-term Bitcoin volatility. Central to our approach is a signal-to-noise filtration mechanism that quantifies the relevance of sentiment streams by evaluating linguistic coherence, engagement patterns, and their historical association with market movements. Filtered sentiment features are integrated alongside price and volume data into a multi-model pipeline comprising autoregressive, recurrent neural, and transformer-based architectures. Our evaluation employs a rolling validation procedure across varying temporal resolutions, assessing both accuracy in magnitude forecasts and correctness of directional predictions. Findings indicate that models enriched with high‑quality sentiment signals outperform those trained on market data alone, yielding more reliable forecasts during periods of heightened market turbulence. Analysis of lead‑lag relationships further reveals that distilled sentiment signals can provide advanced indicators of volatility surges, offering practical value for algorithmic strategies and risk management. This work advances the intersection of behavioral finance and machine learning by demonstrating the feasibility and benefits of blending NLP‑derived sentiment insights with robust time series methods. The proposed framework not only enhances predictive performance but also offers a scalable blueprint for real‑time volatility monitoring in sentiment‑driven asset classes. Future research may extend this methodology to additional cryptocurrencies, incorporate multilingual data sources, and explore causal inference techniques to deepen understanding of sentiment’s impact on market behavior.