(untitled) (gjstudies, 2025)
This paper introduces a novel, technology-driven framework for analyzing the impact ofaccounting standards harmonization on cross-border investment comparability, moving beyond traditional regulatory and economic analyses. We propose a hybrid methodology thatcombines natural language processing (NLP) of financial statement footnotes with networkanalysis of global investment flows to quantify the ’comparability gap’ that persists evenamong jurisdictions nominally aligned with major standards like IFRS or US GAAP. Ourapproach treats accounting standards not as binary, adopted-or-not systems, but as complex, adaptive linguistic and rule-based ecosystems that are implemented and interpretedwith significant local variation. By constructing a multi-dimensional comparability indexfrom machine-analyzed disclosures, we model how these variations influence the portfolioallocation decisions of institutional investors. Our results, derived from a unique datasetof over 50,000 annual reports from 42 countries, reveal that formal harmonization accountsfor less than 40% of the variance in investment comparability. The residual ’noise’ is systematically explained by linguistic divergence in key disclosures (e.g., revenue recognition,financial instruments) and the structural topology of pre-existing investment networks. Weconclude that the future of global financial reporting comparability lies not in further procedural convergence, but in the development of real-time, AI-powered translation layersthat can dynamically map and reconcile disclosure practices, effectively creating a ’semanticbridge’ between financial reporting regimes. This represents a fundamental shift from astandards-setting paradigm to a technological-interpretive one.