Zixuan Zhou¹ and Carlos Gonzalez², ¹Brandeis University, USA, ²California State Polytechnic University, USA

We aim to address the challenge of understanding stock market behavior during economic uncertainty by integrating S&P 500 stock data with macroeconomic indicators and leveraging Generative AI [6]. Traditional approaches often focus on technical or fundamental analysis, but few incorporate real-time data or AI-driven insights. Our solution combines Python-based data analysis, OpenAI’s API, and interactive visualizations to create a user-friendly platform for exploring stock trends and generating financial models [7]. Key challenges included ensuring AI accuracy and balancing functionality with simplicity, which we addressed through feedback mechanisms and modular design. The platform allows users to input queries (e.g., "Tesla stock performance") and receive real-time insights, including text-based responses and visualizations. Preliminary results highlight the platform's potential to democratize access to financial knowledge and improve decision-making. By combining modern AI technologies with traditional financial analysis, our project offers a versatile tool for investors, researchers, and policymakers, making it a valuable resource for navigating complex financial markets [8].

Web Application, Generative AI, Macroeconomic Indicators, Causal Inference, Financial Data