(untitled) (gjstudies, 2020)
This research introduces a novel methodological framework for analyzing earnings management incentives and their impact on financial disclosure credibility, departing fromtraditional econometric models by integrating principles from computational linguistics, network theory, and behavioral economics. We propose that earnings management is not merely a function of isolated financial variables but emerges from complex,multi-layered incentive structures that can be modeled as dynamic networks of influence. Our approach conceptualizes financial disclosures as semantic networks where therelationships between accounting concepts reveal underlying management intentions.We develop a credibility scoring algorithm that evaluates disclosures along three noveldimensions: semantic coherence under alternative accounting treatments, incentivealignment transparency, and temporal consistency patterns across reporting periods.The methodology employs a hybrid analytical engine combining natural language processing of management discussion and analysis (MDA) sections, multi-agent simulationof stakeholder influence networks, and anomaly detection in time-series financial datausing bio-inspired optimization algorithms. We test this framework on a unique longitudinal dataset of technology and manufacturing firms from 1995-2004, deliberatelyavoiding post-2005 data to establish a baseline before major regulatory shifts. Ourfindings reveal previously undocumented patterns of incentive clustering where specific combinations of corporate governance structures, market pressures, and executivecompensation arrangements create predictable credibility degradation pathways. Weidentify three novel earnings management archetypes: ’selective transparency’ patternswhere firms disclose extensively on favorable metrics while obfuscating unfavorableones, ’narrative anchoring’ techniques that use consistent qualitative language to maskquantitative volatility, and ’temporal smoothing networks’ that distribute earningsmanagement across multiple periods in non-linear patterns. The results demonstratethat disclosure credibility cannot be adequately assessed through traditional quantitative measures alone, but requires analysis of the structural relationships betweenincentives, narrative framing, and numerical reporting. This research contributes toPublished: 2020-09-07financial analysis methodology by providing the first integrated framework for evaluating the architecture of disclosure credibility, with implications for auditors, regulators,and investors seeking to identify sophisticated earnings management strategies thatevade conventional detection methods.