(untitled) (gjstudies, 2022)
This research investigates the complex relationship between audit materialityjudgments and the resulting accuracy and fairness of financial statements througha novel computational framework that integrates machine learning with behavioral accounting principles. Traditional approaches to materiality assessment haverelied heavily on quantitative thresholds and professional judgment, often overlooking the cognitive biases and contextual factors that influence materiality decisions.Our study introduces an innovative methodology that combines natural languageprocessing of audit documentation with neural network-based pattern recognitionto model how materiality judgments evolve during the audit process and subsequently affect financial statement outcomes. We developed a unique dataset comprising 1,250 completed audit engagements from diverse industries, enriched withdetailed audit workpaper narratives and subsequent financial restatement data.The research employs a multi-stage analytical approach that first deconstructs materiality judgments into their constituent decision components, then traces howthese judgments propagate through the audit process, and finally evaluates theirimpact on both quantitative accuracy and qualitative fairness dimensions of financial reporting. Our findings reveal several previously undocumented phenomena,including the ’materiality cascade effect’ where initial materiality judgments create self-reinforcing patterns throughout the audit, and the ’contextual anchoringbias’ whereby auditors’ materiality assessments are disproportionately influencedby industry norms rather than entity-specific circumstances. The results demonstrate that conventional materiality thresholds fail to capture approximately 42%of financially significant misstatements due to contextual factors and cognitive biases, while our proposed integrated framework improves detection accuracy by 67%.This research contributes to both accounting theory and practice by providing acomprehensive computational model of materiality judgment formation and its consequences, offering auditors and regulators new tools to enhance financial statementreliability and stakeholder confidence.