Company Similarity using Large Language Models

ArXiv ID: 2308.08031 “View on arXiv”

Authors: Unknown

Abstract

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.

Keywords: Large Language Models (LLMs), Company Embeddings, SEC Filings, GICS Classification, Similarity Metrics, Equities

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced NLP and ML techniques (BERT embeddings, fine-tuning) with moderate mathematical complexity, while demonstrating strong empirical rigor through rigorous backtesting on SEC filings, benchmarking against GICS, and validation with financial metrics like return correlation.

  flowchart TD
    A["Research Goal<br>Model Company Similarity<br>using LLMs & SEC Filings"] --> B["Data Source<br>SEC Business Descriptions"]
    B --> C["Methodology<br>Pre-train & Fine-tune LLMs"]
    C --> D["Process<br>Generate Company Embeddings"]
    D --> E{"Evaluation"}
    E --> F["Reproduce GICS Classifications"]
    E --> G["Benchmark vs Financial Metrics<br>e.g., Return Correlation"]
    F & G --> H["Findings<br>Embeddings capture similarity<br>validating finance utility"]