Can LLMs improve stock analysis accuracy?
MarketSenseAI 2.0: Enhancing Stock Analysis through LLM Agents
MarketSenseAI 2.0 uses multiple LLM agents (News, Fundamentals, Dynamics, Macroeconomic, Signal) working together to analyze diverse financial data (news, company filings, market trends, macroeconomic reports) for improved stock selection. Key enhancements include a chain-of-agents approach for deeper fundamental analysis using SEC filings and earnings calls, and a retrieval-augmented generation module with hypothetical document embeddings for richer macroeconomic insights. Experiments show significant, risk-adjusted outperformance compared to S&P benchmarks, highlighting the potential of multi-agent LLM systems in finance. The framework emphasizes explainability and modularity, enabling independent agent usage and facilitating future integration of advanced LLMs.