Posted: Jun 18, 1999
The integrity of financial reporting represents a cornerstone of capital market efficiency and investor protection. Auditor independence has long been recognized as a fundamental prerequisite for reliable financial statements, yet the precise nature of this relationship remains inadequately understood through traditional accounting research methodologies. Conventional approaches have predominantly focused on structural independence indicators such as non-audit service fees, audit partner rotation, and regulatory compliance metrics. While these factors provide important insights, they fail to capture the complex cognitive and behavioral dimensions that significantly influence auditor judgment and decision-making processes. This research addresses critical gaps in the existing literature by developing and validating a comprehensive computational framework that integrates multiple disciplinary perspectives to examine the auditor independence-financial statement reliability relationship. Our investigation is guided by three primary research questions that have received limited attention in prior studies. First, to what extent do cognitive independence factors, as opposed to structural independence factors, contribute to financial statement reliability? Second, what is the nature of the functional relationship between auditor independence and financial statement reliability, and does it conform to the linear assumptions underlying most existing research? Third, can advanced computational techniques provide early warning indicators of potential financial statement reliability issues before they manifest in public disclosures? Our methodological approach represents a significant departure from traditional accounting research by incorporating techniques from machine learning, network analysis, natural language processing, and behavioral economics. This interdisciplinary framework enables us to capture the multi-faceted nature of auditor independence and its relationship with financial statement reliability in ways that conventional methods cannot.
Downloads: 48
Abstract Views: 1206
Rank: 404985