Accounting Standards Harmonization and Cross Border Investment Comparability

Abstract

This paper introduces a novel, technology-driven framework for analyzing the impact of
accounting standards harmonization on cross-border investment comparability, moving beyond traditional regulatory and economic analyses. We propose a hybrid methodology that
combines natural language processing (NLP) of financial statement footnotes with network
analysis of global investment flows to quantify the ’comparability gap’ that persists even
among jurisdictions nominally aligned with major standards like IFRS or US GAAP. Our
approach treats accounting standards not as binary, adopted-or-not systems, but as complex, adaptive linguistic and rule-based ecosystems that are implemented and interpreted
with significant local variation. By constructing a multi-dimensional comparability index
from machine-analyzed disclosures, we model how these variations influence the portfolio
allocation decisions of institutional investors. Our results, derived from a unique dataset
of over 50,000 annual reports from 42 countries, reveal that formal harmonization accounts
for 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. We
conclude that the future of global financial reporting comparability lies not in further procedural convergence, but in the development of real-time, AI-powered translation layers
that can dynamically map and reconcile disclosure practices, effectively creating a ’semantic
bridge’ between financial reporting regimes. This represents a fundamental shift from a
standards-setting paradigm to a technological-interpretive one.