Assessing Post-Reform Changes in Risk Disclosure Quality with a Multidimensional Text Analysis Approach

2026-06-24

AI Summary

This study investigates how Japan’s 2019 risk disclosure regulatory reform influenced the multidimensional quality dynamics of corporate disclosure texts. Employing a longitudinal, multidimensional textual analysis framework—integrating Japanese-language NLP-based metrics, paired statistical tests, transfer functions, and cross-sectional correlation modeling—the paper introduces, for the first time, a topic coherence metric to assess the alignment between risk disclosures and corporate management strategies. The findings reveal a significant increase in disclosure length accompanied by reduced readability, overall improvements in information structure yet stagnation in descriptive quality, and divergent adaptation patterns across market segments. This approach uncovers complex, nuanced shifts that single-dimensional metrics would overlook, thereby offering a comprehensive multidimensional framework for evaluating regulatory impact on disclosure practices.

Abstract

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.

Problem

• risk disclosure
• text analysis
• disclosure quality
• multidimensional evaluation
• corporate narrative

Innovation

• multidimensional text analysis
• longitudinal NLP
• risk disclosure quality
• cross-section relevance indicator
• shift function analysis