MarketSenseAI 2.0: Enhancing Stock Analysis through LLM Agents

• URL: http://arxiv.org/abs/2502.00415v1
• Date: Sat, 01 Feb 2025 12:33:23 GMT
• Title: MarketSenseAI 2.0: Enhancing Stock Analysis through LLM Agents
• Authors: George Fatouros, Kostas Metaxas, John Soldatos, Manos Karathanassis,
• Abstract summary: We present the latest advancements on MarketSenseAI, driven by rapid technological expansion in Large Language Models (LLMs)<n>MarketSenseAI processes SEC filings and earnings calls, while enriching macroeconomic analysis through systematic processing of diverse institutional reports.<n> 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%.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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