Synergistic Alpha: A Deep Learning Framework for Forecasting Cryptocurrency Returns by Fusing On-Chain, Sentiment, and Market Data

Abstract

The inherent volatility and unique economic characteristics of cryptocurrencies pose significant challenges to conventional asset-pricing models. This study investigates whether a synergistic fusion of the network’s fundamental data (on-chain metrics), market behavioral dynamics (social media sentiment), and historical market data can uncover statistically and economically significant predictive power when analyzed by advanced deep learning architectures. We developed a sophisticated forecasting and backtesting framework to predict the daily log returns of Bitcoin (BTC). The methodology is grounded in rigorous time-series analysis, beginning with Augmented Dickey-Fuller tests to ensure data stationarity. We constructed a multi-modal dataset from specified, high-frequency sources (Kaiko, Glassnode, and a custom-built FinBERT sentiment model) spanning January 1, 2018, to December 31, 2023. We systematically compared the performance of a state-of-the-art Transformer model against Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and robust econometric baselines, including GARCH(1,1) and ARIMA. The models were evaluated not only on statistical accuracy (such as Root Mean Squared Error and Directional Accuracy) but also on their economic significance via a realistic trading backtest that incorporates transaction costs. The fully integrated Hybrid Transformer model demonstrated superior forecasting accuracy, achieving the highest Directional Accuracy (61.25%). More importantly, in a transaction-cost-aware backtest, a trading strategy guided by this model yielded an annualized Sharpe Ratio of 1.58, significantly outperforming a buy-and-hold benchmark (Sharpe Ratio: 0.72). The strategy generated a statistically significant Jensen's Alpha of 0.18 (p < 0.01), indicating substantial risk-adjusted excess returns. Feature importance analysis via SHAP confirmed that social media sentiment and the NVT Signal were the most influential predictors beyond past returns. In conclusion, the findings provide strong evidence that the cryptocurrency market exhibits exploitable inefficiencies. The fusion of on-chain, sentiment, and market data, when processed by attention-based neural networks, uncovers a statistically and economically significant predictive edge. This work challenges the semi-strong form of market efficiency for digital assets and suggests that alpha is derivable from the complex, high-dimensional data footprints unique to this asset class, providing a robust framework for quantitative investment strategies.