Enhancing stock market price prediction through FinGPT-driven sentiment analysis, technical indicators, historical...

Accurate stock price forecasting is increasingly dependent on models that can integrate heterogeneous market signals beyond traditional quantitative indicators. Classical sentiment analysis tools and lexicon-based methods often fail to capture the contextual and domain-specific nuances of financial language, limiting their predictive contribution. To address these limitations, this study introduces a multimodal forecasting framework that leverages FinGPT, an advanced financial large language model specifically trained for market-related text understanding. FinGPT is employed to extract high-resolution sentiment polarity scores from Twitter data, replacing conventional sentiment models and providing a more robust representation of investor psychology. These sentiment features are fused with historical stock prices and technical indicators to form a comprehensive input space. The integrated dataset is used to evaluate four deep learning architectures: CNN, LSTM, Attention-based LSTM (ALSTM), and a newly proposed Hybrid ALSTM-CNN model with an attention mechanism. Experiments performed on eight major NASDAQ and NYSE stocks demonstrate that incorporating FinGPT-derived sentiment significantly enhances forecasting accuracy across all models. The Hybrid model consistently achieves the best performance, effectively capturing both long-term dependencies and short-term market dynamics. Results confirm that FinGPT-driven sentiment modeling provides substantial predictive value compared with traditional sentiment approaches, enabling more realistic, behavior-aware stock price forecasting. For instance, in the case of AMD stock, it recorded the lowest MSE (46.06) and the highest R² (0.624), highlighting its superior ability to capture nonlinear dependencies and market dynamics. Overall, the proposed sentiment-enhanced hybrid framework provides a more robust and realistic procedure for stock price prediction by integrating quantitative and qualitative market signals.