  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.
