Predicting stock price movements is a longstanding challenge in financial research due to the market’s inherent volatility and the interplay of both quantitative indicators and qualitative sentiment. Recent advances in generative artificial intelligence (AI), particularly large language models (LLMs), have opened new avenues for integrating textual information into predictive models. In this study, we propose a novel attention-based framework that leverages LLM-generated text embeddings as predictive features, combining them with numerical financial indicators to forecast stock prices. Unlike conventional cross-attention approaches, our unified self-attention strategy, which processes concatenated multi-modal features as a single sequence, achieves superior predictive accuracy by enabling richer intra-modal interactions. To further capture the temporal dependencies within financial indicators, we incorporate a long short-term memory (LSTM) module into the framework. Extensive experiments on real-world financial datasets demonstrate that our model could achieve promising performance, highlighting the effectiveness of generative AI-driven textual representations in enhancing financial forecasting. This work underscores the potential of combining structured data with LLM-derived features through attention-based architectures for more robust and interpretable stock price prediction.