AI-Driven Financial Advisory Systems: Optimizing Investment Strategies with Sentiment Analysis and Predictive Modeling

The exponential growth of real-time financial data and increasing market complexities necessitate intelligent systems capable of providing actionable insights for investors. This paper introduces an AI-driven financial advisory system that integrates machine learning and natural language processing techniques to deliver individualized investment recommendations. Key features include stock trend prediction, portfolio optimization customized towards user-defined goals, and real-time sentiment analysis on unstructured data sources like news and social media. This system uses advanced models including LSTM networks for temporal analysis, FinBERT for domain-specific sentiment classification, and so forth. A conversational AI chatbot improves user interaction by offering real-time assistance and investment advice. With the modular architecture, using Flutter as the frontend and Flask as the backend processing, this system uses APIs such as Alpha Vantage and NotDiamond AI for real-time financial and sentiment data. Experiments show that the system provides better performance in terms of predictive accuracy, sentiment classification, and portfolio optimization compared with existing platforms. By combining transparency, explainability, and user personalization, this work advances further in the democratization of AI in finance, making tools with such sophistication accessible to an audience beyond the narrowly qualified few. The proposed system will be scalable and implementable for the complexities of contemporary financial markets.