Improving the Completeness and Comparability of Segment Disclosures: A Large Language Model Approach

Battle Count: 70

The paper is primarily focused on accounting research and financial reporting measurement rather than trading strategies. However, improved segment data completeness and comparability could indirectly benefit quantitative trading by enabling better fundamental analysis, more accurate firm-level risk assessment, and improved cross-firm comparisons for factor construction. The segment-level revenue and profitability data could inform sector rotation or geographic exposure strategies, but the paper does not directly address trading applications.

Moderate complexity. Requires: (1) access to SEC EDGAR for Form 10-K filings, (2) OpenAI API with Responses API and Files API, (3) structured prompt engineering for multi-stage extraction, (4) thread-based concurrent processing architecture for scalability, (5) RAG system design for cross-document retrieval. The core logic is well-described but implementation requires careful prompt design, handling of diverse filing formats, and quality control for extraction accuracy. No open-source code is provided.

Data sets are available from public sources (SEC EDGAR). The paper describes the workflow, prompt templates (Appendix A and B), and evaluation procedure in detail. However, no code repository is mentioned, and the specific API parameters, thread architecture details, and full prompt engineering are only partially disclosed. The use of proprietary OpenAI API (GPT-4.1) limits full reproducibility.