For both public and private firms, comparable companies analysis is widely used as a method for company valuation. In particular, the method is of great value for valuation of private equity companies. The several approaches to the comparable companies method usually rely on a qualitative approach to identifying similar peer companies, which tends to use established industry classification schemes and/or analyst intuition and knowledge. However, more quantitative methods have started being used in the literature and in the private equity industry, in particular, machine learning clustering, and natural language processing (NLP). For NLP methods, the process consists of extracting product entities from e.g., the company's website or company descriptions from some financial database system and then to perform similarity analysis. Here, using companies descriptions/summaries from publicly available companies' Wikipedia websites, we show that using large language models (LLMs), such as GPT from openaAI, has a much higher precision and success rate than using the standard named entity recognition (NER) which uses manual annotation. We demonstrate quantitatively a higher precision rate, and show that, qualitatively, it can be used to create appropriate comparable companies peer groups which can then be used for equity valuation.

翻译：对于上市公司和非上市公司而言，可比公司分析被广泛用作公司估值的一种方法。特别地，该方法对私募股权公司的估值具有重要价值。可比公司方法的几种传统途径通常依赖于定性方法来识别相似的同行公司，这些方法往往使用既定的行业分类方案和/或分析师的直觉与知识。然而，在文献和私募股权行业中，更多定量方法已开始被使用，特别是机器学习聚类和自然语言处理（NLP）。对于NLP方法，其流程包括从公司网站或某些金融数据库系统中的公司描述中提取产品实体，然后进行相似性分析。本文利用维基百科上公开发布的公司描述/摘要数据，我们展示了使用大型语言模型（LLMs），例如OpenAI的GPT，比使用基于人工标注的标准命名实体识别（NER）具有更高的精确度和成功率。我们定量地证明了更高的精确度，并定性表明该方法可用于创建合适的可比公司同行组，进而用于股权估值。