用大模型自动分析上市公司年报，快速评估企业表现与战略变化。
A Scalable Data-Driven Framework for Systematic Analysis of SEC 10-K Filings Using Large Language Models
- 基于大模型系统化分析SEC 10-K年报，提取关键信息进行评分。
- 自动提取并预处理年报内容，准确识别监管要求的章节与重点段落。
- 提供无代码交互界面，支持多公司年度对比和可视化洞察。
纽约证券交易所上市公司数量持续激增，给市场分析师、交易员和股东带来了巨大挑战，他们需定期监控并评估大量公司的业绩与战略动向。迫切需要一种快速、低成本且全面的方法，以高效评估企业绩效并检测、比较多家公司的战略变化。本文提出一种新颖的数据驱动方法，利用大语言模型（LLMs）系统分析和评估企业基于其提交的SEC 10-K年报的表现。这些文件包含公司财务状况与战略方向的详细年度报告，是评估企业健康度的关键数据源，涵盖信心水平、环境可持续性、创新能力及人力资源管理等方面。我们还设计了一个自动化系统，用于提取和预处理10-K文件，精确识别并分割出美国证券交易委员会（SEC）规定的必要章节，同时分离出包含关键信息的文本内容。经过清洗的数据输入Cohere的Command-R+ LLM，生成多个性能指标的量化评分。评分结果被进一步处理并可视化，以提供可操作的洞察。该方案最终被实现为一个交互式图形用户界面（GUI），作为无需编程的解决方案，运行数据流水线并生成可视化图表。应用展示评分结果，并支持企业间跨年度对比。
原文摘要 · Abstract (English)
The number of companies listed on the NYSE has been growing exponentially, creating a significant challenge for market analysts, traders, and stockholders who must monitor and assess the performance and strategic shifts of a large number of companies regularly. There is an increasing need for a fast, cost-effective, and comprehensive method to evaluate the performance and detect and compare many companies' strategy changes efficiently. We propose a novel data-driven approach that leverages large language models (LLMs) to systematically analyze and rate the performance of companies based on their SEC 10-K filings. These filings, which provide detailed annual reports on a company's financial performance and strategic direction, serve as a rich source of data for evaluating various aspects of corporate health, including confidence, environmental sustainability, innovation, and workforce management. We also introduce an automated system for extracting and preprocessing 10-K filings. This system accurately identifies and segments the required sections as outlined by the SEC, while also isolating key textual content that contains critical information about the company. This curated data is then fed into Cohere's Command-R+ LLM to generate quantitative ratings across various performance metrics. These ratings are subsequently processed and visualized to provide actionable insights. The proposed scheme is then implemented on an interactive GUI as a no-code solution for running the data pipeline and creating the visualizations. The application showcases the rating results and provides year-on-year comparisons of company performance.
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