用大模型整合新闻与财报，让选股更准还更稳
MarketSenseAI 2.0: Enhancing Stock Analysis through LLM Agents
- 用检索增强生成+智能体架构，自动分析财报和宏观经济报告
- 两年内对标普100股收益达125.9%，超指数73.5%且风险相当
- 可扩展至标普500，夏普比率表现优于市场33.8%
MarketSenseAI 是一个融合大型语言模型（LLMs）的股票分析框架，综合处理金融新闻、历史价格、公司基本面及宏观经济环境，支持股票分析与选择决策。本文介绍该框架在大模型技术快速演进背景下的最新进展。通过结合检索增强生成与 LLM 智能体的新架构，系统可处理美国证监会文件与财报电话会，并通过系统化解析多类机构报告提升宏观经济分析能力。实证评估显示，对 2023-2024 年标普 100 股票的回测中，该框架实现累计收益率 125.9%，显著高于同期指数收益 73.5%，同时保持相近风险水平。进一步在 2024 年对标普 500 股票的验证表明其可扩展性，其夏普比率较市场高出 33.8%。本研究标志着大模型在金融分析中的重要突破，为基于 LLM 的投资策略稳健性提供了新证据。
原文摘要 · Abstract (English)
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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