Detecting Semantic Mismatches in XBRL Tag Mapping for SEC 10-K Filings: A Text Comparison and Historical Consistency Analysis
SEC 10-K報告書におけるXBRLタグマッピングのセマンティックミスマッチ検出：テキスト比較と時系列一貫性分析 (AI 翻訳)
D. Liang, Zijie Chen, Chuanli Wei
🤖 gxceed AI 要約
日本語
本研究は、SECの10-K年次報告書における財務諸表項目ラベルとXBRLタクソノミ要素間の意味的ミスマッチを検出する手法を提案する。TF-IDFとBM25を用いたテキスト類似度スコアリングと、業界別のベンチマーク分析により、カスタムタグ使用の異質性を明らかにした。軽量な検証手法は既存の開示管理ワークフローに統合可能であり、データ品質向上に貢献する。
English
This study proposes a method to detect semantic mismatches between financial statement line-item labels and XBRL taxonomy elements in SEC 10-K filings. Using TF-IDF and BM25 for text similarity scoring combined with industry peer benchmarking, it reveals persistent heterogeneity in custom tag usage across filer sizes and sectors. The lightweight verification methods are designed for integration into existing disclosure management workflows without complex infrastructure.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
米国SECのXBRLタグを対象とするが、日本でもEDINETやSSBJ開示において同様のデータ品質課題が存在し、カスタムタグの増加が比較可能性を損なう可能性がある。本手法は日本の開示実務にも応用可能な示唆を含む。
In the global GX context
While focused on SEC 10-K filings, this paper addresses a universal challenge in structured disclosure: semantic misalignment between reported items and taxonomy elements. As global frameworks like ISSB and ESRS adopt XBRL, ensuring tag accuracy becomes critical for cross-firm comparability and automated analysis, making these findings relevant beyond U.S. financial reporting.
👥 読者別の含意
🔬研究者:The text comparison and consistency analysis methodology can be adapted to detect data quality issues in ESG or sustainability XBRL filings.
🏢実務担当者:Corporate disclosure teams can use the lightweight verification methods to improve the accuracy of XBRL tagging in their financial and—potentially—sustainability reports.
🏛政策担当者:Regulators can leverage the identified tag mapping patterns to refine validation rules and taxonomy design, enhancing overall data quality in structured disclosures.
📄 Abstract（原文）
The accuracy of eXtensible Business Reporting Language (XBRL) tag mapping in SEC financial filings directly affects the reliability of automated financial analysis conducted by millions of investors through the EDGAR system. This study investigates semantic mismatches between financial statement line-item labels and their corresponding XBRL taxonomy elements in 10-K annual reports filed with the U.S. Securities and Exchange Commission. Drawing on SEC Financial Statement Data Sets and the XBRL US Data Quality Committee (DQC) validation rule library, this research analyzes custom tag usage patterns across filer categories and industry sectors over the period 2014–2024, with cross-sectional tabulation of filer-category rates at selected benchmark years (2014, 2017, 2019, and 2020) and aggregate trend data through 2024 drawn from SEC Office of Structured Disclosure publications. A tiered text comparison approach combining lexical similarity scoring (TF-IDF and BM25) with domain-specific contextual features is applied to evaluate the semantic alignment between reported line items and assigned taxonomy tags. Cross-period consistency analysis and SIC-code industry peer benchmarking are employed to identify anomalous tag selection changes that may indicate data quality degradation rather than substantive business changes. The findings reveal persistent heterogeneity in custom tag rates across industries and filer sizes, with specific tag mapping patterns that warrant targeted validation checkpoints. The proposed lightweight verification methods are designed for integration into existing disclosure management workflows without requiring complex computational infrastructure.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://ciajournal.com/index.php/jcia/article/download/73/68first seen 2026-07-18 08:34:17
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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。