LLM-Based Stock Price and Volatility Prediction: A Multi-Modal Deep Learning Framework Integrating Financial News Sentiment and Technical Indicators

Stock price prediction is the most enduring problem of financial engineering because financial markets are nonlinear, non-stationary, and informationally dense. Classical models based on historical price trends and fundamentals often miss the context of qualitative financial narratives. This paper proposes FinLLM-Predict, a new multimodal deep learning framework based on Large Language Models (LLMs) and quantitative financial signals for directional price forecasting and volatility estimation. In our architecture, we combine domain-specific LLMs tuned to semantic analysis of financial news, earnings transcripts, and technical indicators based on price series via a market-responsive dynamic cross-modal attention mechanism adjusting modality contributions. Hierarchical multitask learning predicts price direction, magnitude, and realized volatility simultaneously for coordinated optimization on related objectives. Experiments conducted over a six-year horizon on the S&P 500 index show that FinLLM-Predict achieves a 12.3% improvement in directional accuracy and an 18.7% reduction in volatility prediction error over state-of-the-art baselines, and is particularly robust in high-volatility regimes. Ablation studies show that each component is needed, and attention analyses indicate that textual signals become more informative during periods of market uncertainty, consistent with economic intuition. The interpretability, performance, and adaptability of the framework make it an interesting instrument for algorithmic trading and risk management.