Earnings Management Incentives and Financial Disclosure Credibility Analysis

Abstract

This research introduces a novel methodological framework for analyzing earnings management incentives and their impact on financial disclosure credibility, departing from
traditional 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 the
relationships between accounting concepts reveal underlying management intentions.
We develop a credibility scoring algorithm that evaluates disclosures along three novel
dimensions: semantic coherence under alternative accounting treatments, incentive
alignment 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 simulation
of stakeholder influence networks, and anomaly detection in time-series financial data
using bio-inspired optimization algorithms. We test this framework on a unique longitudinal dataset of technology and manufacturing firms from 1995-2004, deliberately
avoiding post-2005 data to establish a baseline before major regulatory shifts. Our
findings reveal previously undocumented patterns of incentive clustering where specific combinations of corporate governance structures, market pressures, and executive
compensation arrangements create predictable credibility degradation pathways. We
identify three novel earnings management archetypes: ’selective transparency’ patterns
where firms disclose extensively on favorable metrics while obfuscating unfavorable
ones, ’narrative anchoring’ techniques that use consistent qualitative language to mask
quantitative volatility, and ’temporal smoothing networks’ that distribute earnings
management across multiple periods in non-linear patterns. The results demonstrate
that disclosure credibility cannot be adequately assessed through traditional quantitative measures alone, but requires analysis of the structural relationships between
incentives, narrative framing, and numerical reporting. This research contributes to
Published: 2020-09-07
financial 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 that
evade conventional detection methods.