Earnings Management Behavior and Financial Reporting Credibility in Emerging Economies (gjstudies, 2018)
This research introduces a novel, cross-disciplinary framework for analyzing earnings management behavior in emerging economies by integrating computational linguistics, network theory, and institutional sociology. Moving beyond traditional accrualbased models, we propose that earnings management is not merely an accountingphenomenon but a complex socio-technical signal embedded within the broader digital information ecosystem of a firm. Our methodology employs a hybrid technique oftransformer-based natural language processing (NLP) on narrative sections of annualreports, coupled with a dynamic network analysis of a firm’s digital footprint—includingpress releases, social media sentiment, and regulatory filing metadata. We conceptualize financial reporting credibility as a function of the coherence and resilience of thisinformation network, rather than a binary outcome of audit opinion. Applying thisframework to a unique longitudinal dataset from 500 firms across Brazil, India, andVietnam from 2015 to 2023, we uncover previously unobserved patterns of ”signalingcascades,” where subtle linguistic manipulations in management discussion are amplified through correlated digital channels to shape market perception, often independentof underlying accrual adjustments. Our results demonstrate that in institutionally weakenvironments, sophisticated earnings management migrates from the balance sheet tothe narrative and digital periphery, creating a credibility gap that traditional audit andcompliance mechanisms are ill-equipped to detect. The findings challenge the primacyof quantitative financial statement analysis in emerging markets and advocate for aholistic, systems-oriented approach to assessing reporting quality that accounts for themulti-channel, narrative-driven nature of modern corporate communication.