A Unified Multi-Modal Framework for Intelligent Financial Systems: Integrating Reinforcement Learning, High-Frequency Trading, and Game-Theoretic Approaches with Cross-Modal Sentiment Analysis

The paper presents a unified multi-modal framework that integrates Proximal Policy Optimization, advanced time-series prediction models, in-context learning, game-theoretic approaches, and cross-modal sentiment analysis for intelligent financial systems. Experimental results demonstrate significant performance improvements, including a 23.7% enhancement in portfolio optimization metrics and a 31.2% reduction in prediction error for high-frequency trading. This framework addresses the limitations of isolated financial AI technologies, offering a comprehensive solution that can adapt to the complexities of modern financial markets, which is essential for practitioners aiming to build more robust and interconnected AI systems.