Improving the Completeness and Comparability of Segment Disclosures: A Large Language Model Approach
arXiv:2605.23924v1 Announce Type: new Abstract: Segment-level disclosures are a central component of financial reporting, providing insight into firms' internal organization and the allocation of economic activities across operating units. However, segment information is often presented in both qualitative and quantitative forms, dispersed across tables and narrative sections of Form 10-K filings. Empirical research relying on structured databases faces both completeness and comparability challenges, as some firm-year observations may be missing, nested segment disclosures are not captured, an
The proliferation of advanced large language models (LLMs) provides a new computational tool to address long-standing challenges in financial data extraction and analysis, specifically around unstructured segment disclosures.
This development allows for more complete and comparable financial reporting data, enhancing the fidelity of financial research, investment decisions, and regulatory oversight by overcoming limitations of current structured databases.
The ability to accurately extract and standardize complex segment disclosures from unstructured financial documents will improve the quality and availability of granular corporate financial data, leading to better insights into firm operations.
- · Financial analysts
- · Quantitative researchers
- · Regulatory bodies
- · Financial data providers
- · Companies with opaque segment reporting
- · Manual data entry services
Improved accuracy and depth of financial models relying on segment data.
Increased transparency in corporate financial reporting could lead to more efficient capital allocation.
New competitive landscape for financial data analytics, favoring firms adept at deploying AI for unstructured data extraction.
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