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 tend 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 OpenAI, has a much higher precision and success rate than using the standard named entity recognition (NER) methods which use 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 could then be used for equity valuation.

翻译：对于上市公司和非上市公司，可比公司分析被广泛用作公司估值的方法。该方法对私募股权公司的估值尤其具有重要价值。可比公司法的几种方法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/或分析师直觉及知识。然而，文献和私募股权行业中已开始应用更多定量方法，特别是机器学习聚类和自然语言处理（NLP）。对于NLP方法，该过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。本文利用公开可用的公司维基百科网站上的公司描述/摘要，证明使用大型语言模型（LLMs），例如OpenAI的GPT，比使用需要手动标注的标准命名实体识别（NER）方法具有更高的精确率和成功率。我们定量地展示了更高的精确率，并定性表明，该方法可用于创建适当的可比公司同行组，进而用于股权估值。