MarketSenseAI 2.0: Enhancing Stock Analysis through LLM Agents

ArXiv ID: 2502.00415 “View on arXiv”

Authors: Unknown

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.

Keywords: Retrieval-Augmented Generation, LLM Agents, Fundamental Analysis, MarketSenseAI, SEC Filings, Stocks

Complexity vs Empirical Score

• Math Complexity: 2.0/10
• Empirical Rigor: 8.5/10
• Quadrant: Street Traders
• Why: The paper relies primarily on LLM agent architectures (Chain-of-Agents, RAG) rather than advanced mathematical proofs, but it demonstrates very strong empirical rigor with detailed backtests on S&P 100/500 over 2023-2024, specific performance metrics (125.9% cumulative returns vs 73.5% benchmark, 33.8% higher Sortino ratio), and real-world implementation details.

  flowchart TD
    A["Research Goal:<br/>Enhance Stock Analysis via LLM Agents"] --> B{"Key Methodology:<br/>RAG + LLM Agents"}
    B --> C["Data Inputs"]
    C --> D["Financial News<br/>Historical Prices"]
    C --> E["Company Fundamentals<br/>(SEC Filings, Earnings Calls)"]
    C --> F["Macroeconomic<br/>(Institutional Reports)"]
    
    D & E & F --> G["Computational Process:<br/>Holistic Analysis & Selection"]
    G --> H["Key Findings"]
    
    subgraph H ["Outcomes S&P 100 2023-2024"]
        H1["125.9% Cumulative Returns<br/>(vs. Index 73.5%)"]
    end
    
    subgraph I ["Outcomes S&P 500 2024"]
        I1["33.8% Higher Sortino Ratio<br/>Comparable Risk Profile"]
    end
    
    H --> I