  Identifying companies with similar profiles is a core task in finance with a
wide range of applications in portfolio construction, asset pricing and risk
attribution. When a rigorous definition of similarity is lacking, financial
analysts usually resort to 'traditional' industry classifications such as
Global Industry Classification System (GICS) which assign a unique category to
each company at different levels of granularity. Due to their discrete nature,
though, GICS classifications do not allow for ranking companies in terms of
similarity. In this paper, we explore the ability of pre-trained and finetuned
large language models (LLMs) to learn company embeddings based on the business
descriptions reported in SEC filings. We show that we can reproduce GICS
classifications using the embeddings as features. We also benchmark these
embeddings on various machine learning and financial metrics and conclude that
the companies that are similar according to the embeddings are also similar in
terms of financial performance metrics including return correlation.
