(untitled) (gjstudies, 2025)
This research introduces a novel computational framework for analyzing financialdisclosure practices through the lens of minority shareholder protection, an area traditionally dominated by qualitative legal and accounting studies. We develop a hybrid methodology combining natural language processing (NLP) techniques adaptedfrom computational linguistics with network analysis approaches from social scienceto quantitatively assess disclosure quality and transparency. Our approach uniquelyoperationalizes the concept of ’protective disclosure’ by identifying linguistic patterns,structural features, and information accessibility metrics that correlate with reducedagency conflicts and enhanced minority rights. We analyze a comprehensive datasetof 1,200 annual reports from publicly traded companies across three jurisdictions withvarying legal protections for minority shareholders. The methodology employs a multidimensional scoring system that evaluates disclosures across seven novel dimensions:temporal consistency of information presentation, cross-referential transparency, riskfactor granularity, related-party transaction explicitness, voting right clarity, dividendpolicy predictability, and remedial action accessibility. Our results demonstrate thatspecific disclosure practices—particularly structured narrative explanations of votingprocedures and machine-readable tagging of related-party transactions—have significantly stronger protective effects than traditional quantitative disclosure metrics. Weidentify a ’transparency threshold’ beyond which additional disclosure volume yieldsdiminishing protective returns, suggesting an optimal disclosure strategy for minorityprotection. The findings challenge conventional wisdom that more disclosure alwaysbenefits minority shareholders, instead revealing that structured, accessible, and consistent disclosure formats provide superior protection. This research contributes toboth information systems and corporate governance literature by providing the firstcomputational framework for evaluating disclosure quality from a minority protectionperspective and offering evidence-based guidelines for regulatory design.