MarketSenseAI 2.0: Enhancing Stock Analysis through LLM Agents
This work addresses stock analysis for investors by providing an incremental improvement to an existing LLM-based framework.
The paper tackles stock analysis by enhancing the MarketSenseAI framework with LLM agents and Retrieval-Augmented Generation, processing financial data to improve decision-making, achieving 125.9% cumulative returns on S&P 100 stocks over two years compared to 73.5% for the index and a 33.8% higher Sortino ratio on S&P 500 stocks in 2024.
MarketSenseAI is a novel framework for holistic stock analysis which leverages Large Language Models (LLMs) to process financial news, historical prices, company fundamentals and the macroeconomic environment to support decision making in stock analysis and selection. In this paper, we present the latest advancements on MarketSenseAI, driven by rapid technological expansion in LLMs. Through a novel architecture combining Retrieval-Augmented Generation and LLM agents, the framework processes SEC filings and earnings calls, while enriching macroeconomic analysis through systematic processing of diverse institutional reports. We demonstrate a significant improvement in fundamental analysis accuracy over the previous version. Empirical evaluation on S\&P 100 stocks over two years (2023-2024) shows MarketSenseAI achieving cumulative returns of 125.9% compared to the index return of 73.5%, while maintaining comparable risk profiles. Further validation on S\&P 500 stocks during 2024 demonstrates the framework's scalability, delivering a 33.8% higher Sortino ratio than the market. This work marks a significant advancement in applying LLM technology to financial analysis, offering insights into the robustness of LLM-driven investment strategies.