Linking Industry Sectors and Financial Statements: A Hybrid Approach for Company Classification

Abstract

We explore the potential of machine learning algorithms and language models to analyze the relationship between industry sector categories and companies' financial statements. We propose a supervised company classification methodology analyzing several types of representations for financial statements. We show that textual information in financial records can be leveraged by language models to match decision tree-based classifier performance while providing better explainability. Our proposed Text-Numeric Transformer — a fusion of tag embeddings with amounts via a gating mechanism — achieves the best MCC of 0.71. LLM-gen (generative classification) with FinLLaMA3 achieves MCC 0.66 and provides explainable predictions, while LightGBM establishes strong baselines with MCC 0.69.

Key Results

• Text-Numeric Transformer achieves MCC 0.71 — best overall performance
• LLM-gen with FinLLaMA3 provides explainable predictions at MCC 0.66
• LightGBM baseline reaches MCC 0.69 as a strong non-neural baseline
• Textual information matches numerical performance with added explainability