用多维文本分析评估日本改革后风险披露质量变化
Assessing Post-Reform Changes in Risk Disclosure Quality with a Multidimensional Text Analysis Approach
- 融合日语NLP与配对检验，多维度追踪披露变化
- 披露量增但可读性下降，结构改善但描述质量停滞
- 适合关注公司信息披露与监管政策效果的研究者
企业叙事披露为资本市场提供关键信息，但全面评估其随时间的质变仍具挑战。文本具有多维特性，某一维度的改进常伴随其他维度变化。本文提出一种结合日语NLP指标提取、配对检验、转移函数分析及指标相关性分析的纵向文本分析方法，扩展了现有指标体系，引入跨截面相关性指标以衡量风险披露与管理策略的主题一致性。基于2015至2024年共19,770个公司-年度观测值，应用于评估日本2019年披露改革。联合分析揭示了传统单指标方法常掩盖的复杂披露模式变化：披露总量显著增加，但可读性下降；整体信息结构改善，但特定描述质量停滞，且适应程度在不同市场板块间存在差异。
原文摘要 · Abstract (English)
While corporate narrative disclosures provide crucial information to capital markets, comprehensively evaluating their qualitative changes over time remains challenging. Narrative text is inherently multidimensional, meaning that an improvement in one textual dimension often occurs alongside changes in others. To capture these underlying dynamics, we propose a longitudinal text analysis approach combining Japanese-language NLP metric extraction with paired testing, shift function analysis, and inter-metric correlation. Our framework extends prior indicator sets by incorporating a cross-section relevance indicator to measure topical alignment between risk disclosures and management strategies. Applying this approach to evaluate Japan's 2019 disclosure reforms, we analyze 19,770 firm-year observations over a 10-year period (FY2015-FY2024). The joint analysis reveals complex shifts in disclosure patterns that are frequently masked by conventional single-indicator methods. Specifically, we find that while disclosure volume increased substantially, it was accompanied by a decline in readability. Furthermore, although the overall information structure improved, specific descriptive quality stagnated, and the degree of adaptation varied across market segments.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。