(untitled) (gjstudies, 2021)
This research introduces a novel, hybrid machine learning framework for assessing earnings quality that fundamentally reimagines traditional accounting approaches through computational innovation. We propose a methodology that integrates quantum-inspired optimization algorithms with explainable artificial intelligence (XAI) techniques to address the persistent challenges of subjectivity, complexity, and opacity in earnings quality evaluation. Unlike conventional modelsthat rely on predefined financial ratios and linear regression analyses, our approachemploys a multi-modal neural architecture that processes both structured financial data and unstructured narrative disclosures from corporate reports, capturingsubtle patterns and contextual relationships previously inaccessible to traditionalmethods. The framework incorporates a bio-inspired optimization component basedon slime mold algorithms to dynamically weight financial indicators according toindustry-specific and temporal contexts, moving beyond static weighting schemes.Our results, derived from a comprehensive dataset spanning 15 years and multipleglobal markets, demonstrate that the proposed model achieves a 34% improvementin predictive accuracy for earnings manipulation detection compared to establishedbenchmarks, while simultaneously providing interpretable insights into the specificfinancial statement elements contributing to quality assessments. The researchmakes three original contributions: (1) a novel computational architecture thatbridges quantitative financial analysis with qualitative narrative assessment, (2)the first application of quantum-inspired optimization to earnings quality modeling,enabling more nuanced consideration of uncertainty and probabilistic relationships,and (3) an explainability framework that translates complex machine learning decisions into audit-actionable insights. This work represents a paradigm shift infinancial analysis methodology, offering auditors, regulators, and investors a morerobust, transparent, and adaptive tool for evaluating corporate financial reportingintegrity in increasingly complex business environments.