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

Financial technology is rapidly evolving, necessitating advanced AI systems that can address multiple challenges simultaneously. A unified framework was created that combines Proximal Policy Optimization for robo-advisory services, advanced time-series prediction models for high-frequency trading, and game-theoretic strategies for competitive banking.

Additionally, it employs unified embeddings for cross-modal sentiment analysis, allowing for a comprehensive understanding of market dynamics. Extensive experiments on various financial datasets demonstrated that this integrated approach outperformed traditional single-domain systems.

Specifically, it achieved a 23.7% improvement in portfolio optimization metrics, a 31.2% reduction in prediction error for high-frequency trading, and an 18.9% increase in investment recommendation accuracy. The framework also optimized competitive banking strategies, enhancing Nash equilibrium convergence speed by 27.4% and improving sentiment analysis accuracy by 15.6%.

These findings not only advance financial AI but also provide a roadmap for developing adaptable intelligent systems in complex financial markets.