Bitcoin’s sentiment spillover effect and hybrid CNN-LSTM framework for cryptocurrency price forecasting
Abstract
This paper focuses on investigating how sentiments extracted from news surrounding Bitcoin affect other major cryptocurrencies, and on using Bitcoin sentiments along with other features to predict the price of these assets. Since Bitcoin is the market leader and several studies have shown the interconnectedness of the cryptocurrency market, understanding and quantifying the effect of Bitcoin-related sentiment on other coins can provide new insights for investors and traders, as well as expand the horizon of trading signals. After applying statistical tests, we found that sentiment affects different cryptocurrencies in different ways and across various market regimes. In particular, Ethereum demonstrated a bidirectional relationship with Bitcoin sentiment, while Solana reacted almost instantly, and XRP displayed a delayed response. Building on these results, we developed a deep learning model based on a hybrid CNN–LSTM architecture, using Bitcoin sentiment, Bitcoin features, Solana features, and their interactions to predict Solana’s price. Lagged features were used in some combinations with other attributes. Through experimentation with different lookback windows and forecast horizons, one of the models, called Model 5, achieved the strongest results, recording the lowest errors overall (MSE = 255.35, MAE = 12.01). Notably, it employed the shortest lookback configuration (24 h CNN, 72 h LSTM). This framework, which focuses on extracting market conditions from Bitcoin sentiment and features while incorporating asset-specific reactions, offers a unique way of integrating broader market signals into predictive models of major cryptocurrencies.