Named entity recognition using GPT for identifying comparable companies

TL;DR

This paper demonstrates that GPT-based named entity recognition significantly improves the identification of comparable companies from textual descriptions, enhancing valuation processes in finance.

Contribution

It introduces the use of GPT for NER in identifying comparable companies, outperforming traditional methods in precision and qualitative peer group creation.

Findings

GPT-based NER achieves higher precision than standard methods.

Qualitative peer groups created with GPT are more appropriate for valuation.

GPT can effectively extract company entities from Wikipedia descriptions.

Abstract

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.