The Impact of IFRS Adoption on Financial Reporting Quality and Consistency (gjstudies, 2018)
This research presents a novel, cross-disciplinary investigation into the impact of International Financial Reporting Standards (IFRS) adoption by applying computational linguistics and network analysis techniques to financial disclosures, a methodological departurefrom traditional accounting research. We move beyond conventional econometric analysesof accounting metrics to examine the textual and structural properties of financial reportsthemselves as direct indicators of reporting quality and consistency. By constructing aunique corpus of 15,000 annual reports from 2,500 publicly listed firms across 15 jurisdictions over a 12-year period (2010-2022), we quantify reporting quality through measuresof textual complexity, semantic coherence, and disclosure network density. We measureconsistency through the temporal stability of these textual features and the convergence ofreporting structures across firms within adopting countries. Our findings reveal a significant, non-linear relationship between IFRS adoption and textual reporting quality: an initialperiod of increased syntactic complexity and reduced readability (a ’disruption phase’) isfollowed by a subsequent convergence towards more standardized, coherent, and less obfuscated narrative structures. Furthermore, we identify that the consistency of financialreporting, measured as the reduced variance in textual feature vectors across peer firms,improves markedly post-adoption, but this effect is strongly moderated by the strength of acountry’s legal enforcement regime. The most original contribution lies in our application ofa bio-inspired optimization algorithm—an Ant Colony Optimization variant—to model thepath of reporting convergence, treating disclosure elements as nodes in a graph that ’ants’(representing reporting firms) traverse to find optimal, standardized pathways. This modelsuccessfully predicts the time-to-convergence for different jurisdictions with high accuracy.Our results challenge the binary view of IFRS impact, proposing instead a phased, evolutionary model of reporting quality transformation driven by complex adaptive learning withinfinancial reporting ecosystems. This research establishes a new paradigm for assessing accounting standards by leveraging computational text analysis and complex systems theory,offering regulators and auditors a suite of novel, real-time diagnostic tools for monitoringreporting quality beyond numerical compliance.