Financial Statement Comparability Across Countries and International Investment Decisions

Abstract

This research introduces a novel, cross-disciplinary methodology for quantifying
and enhancing financial statement comparability across international jurisdictions,
with the explicit aim of improving global investment decision-making. Moving beyond traditional accounting-based metrics, we propose a hybrid framework that
integrates principles from computational linguistics, network theory, and machine
learning to construct a dynamic, multi-dimensional Comparability Index (CI). The
core innovation lies in our treatment of financial statements not merely as numerical datasets but as complex semantic and relational documents. We employ
natural language processing techniques to analyze the qualitative disclosures and
management discussion, network analysis to map the relational structure of accounting policy choices across firms and countries, and an ensemble of machine
learning models to predict the investment friction caused by comparability gaps.
Our unique dataset comprises 5,000 firms from 40 countries over a ten-year period, enriched with geopolitical, regulatory, and cultural distance variables. The
results demonstrate that our CI explains a significantly higher proportion of variance in cross-border investment flows, foreign institutional ownership, and merger
and acquisition premia than existing uni-dimensional measures. Furthermore, we
identify non-linear threshold effects and interaction effects with investor sophistication, revealing that the cost of low comparability is disproportionately borne by less
sophisticated investors. The findings challenge the prevailing assumption that harmonization of accounting standards (e.g., IFRS adoption) is the primary driver of
comparability, instead highlighting the critical, and previously under-modeled, role
of enforcement regimes, disclosure culture, and the semantic coherence of financial narratives. This research contributes an original, computationally-grounded
framework for a fundamental problem in international finance, offering actionable
insights for regulators aiming to reduce global market fragmentation and for investors navigating complex international information environments.