用大语言模型增强基本面分析：基于RAG的投资者简报生成系统
Augmenting Fundamental Analysis with Large Language Models: A RAG-Based System for Generating Investor Briefs
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中文总结 AI 辅助
研究利用大语言模型，通过对公司报告、宏观经济数据及SEC文件等进行预处理，以类似RAG方式借助gpt - 4o模型，结合基于基钦周期的投资者知识文档，扫描分析9家公司数据并生成简报供投资者评估，探索其在基本面分析中的应用。
中文摘要 AI 辅助
在本研究中，我们探讨大语言模型（LLMs）给基于公司报告、宏观经济数据（如GDP和通胀变化）以及美国证券交易委员会（SEC）文件（可在EDGAR中获取）的公司基本面分析各方面带来的机遇。我们对这些数据进行预处理，然后通过API以类似检索增强生成（RAG）的方式发送到gpt - 4o模型。我们还准备了一份基于基钦周期的典型投资者知识文档。我们对9家公司的数据进行了4周的扫描分析，利用LLMs生成自动简报，并将其发送给9位个人投资者参与者评估这种数据分析方法的实用性。
英文摘要
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.
发表机构
- Faculty of Computer Science, AGH University of Krakow（克拉科夫AGH科技大学计算机科学学院）
机构由 AI 辅助整理，请以论文原文为准。
