用大模型+检索增强生成，自动写公司投资简报。
Augmenting Fundamental Analysis with Large Language Models: A RAG-Based System for Generating Investor Briefs
- 用RAG架构整合财报、宏观数据和SEC文件
- 4周分析9家公司，自动生成简报并获投资者认可
- 适合个人投资者快速获取公司基本面洞察
本研究探讨大型语言模型（LLMs）在企业基本面分析中的应用，基于公司报告、宏观经济数据（如GDP与通胀变化）以及美国证券交易委员会（SEC）的EDGAR数据库文件。我们对数据进行预处理，并通过API将信息输入gpt-4o模型，在检索增强生成（RAG）框架下运行。同时构建了基于基钦周期的投资者知识文档，持续扫描9家公司的关键数据长达4周，利用大模型生成自动化投资简报。这些简报发送给9位个人投资者，以评估该方法在数据分析中的实用性。
原文摘要 · Abstract (English)
In this study, we examine the opportunities brought by Large Language Models (LLMs) to various aspects of fundamental analysis of companies based on their reports as well as data and documents describing macroeconomic situation like GDP and inflation changes as well as documents filled to the U.S. Securities and Exchange Commission (SEC) which can be found in EDGAR. We were preprocessing those data and than sending via API to gpt-4o model in a Retrieval-Augmented Generation (RAG) like regime. We prepared as well a document describing an exemplar investor knowledge based on Kitchin cycles. We were scanning data important for analysis of 9 companies for 4 weeks. Using LLM we were producing automatic briefs about them. They were sent to nine participants who are individual investors to evaluate usefulness of such approach to data analysis.
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