Assessing Post-Reform Changes in Risk Disclosure Quality with a Multidimensional Text Analysis Approach
arXiv:2606.26522v1 Announce Type: new 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
The proliferation of digital data and advanced NLP techniques enables more sophisticated textual analysis of financial disclosures.
Improved methods for assessing narrative disclosure quality can provide better insights into corporate transparency and financial health, but this specific research is highly academic.
This academic paper contributes to a methodological improvement in text analysis, rather than immediately changing market dynamics or corporate behavior.
More robust academic research in financial text analysis emerges.
Potentially, better-informed financial models and regulatory oversight could develop years later.
This could incrementally contribute to a more efficient capital allocation over a very long timeframe.
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Read at arXiv cs.CL