A Study on the Relationship Between Corporate Governance Mechanisms and Audit Quality Outcomes

Posted: Apr 08, 2015

• Cleo Kim
• Finley Patterson
• Harper Boyd

Abstract

This research investigates the complex interplay between corporate governance mechanisms and audit quality outcomes through an innovative computational framework that integrates machine learning algorithms with network analysis. Traditional studies in this domain have primarily relied on linear regression models and manual coding of governance variables, limiting their ability to capture the multidimensional and dynamic nature of governance-audit relationships. Our study introduces a novel methodology that employs graph neural networks to model corporate governance as interconnected networks of relationships, responsibilities, and oversight functions. We developed a comprehensive dataset spanning 1,200 publicly traded companies over a five-year period, incorporating both structured financial data and unstructured governance documentation. The computational framework processes board composition data, committee structures, executive compensation patterns, shareholder activism metrics, and audit committee characteristics as interconnected nodes in a dynamic graph. Our findings reveal several non-linear relationships that challenge conventional wisdom, including threshold effects in board independence and unexpected interaction patterns between audit committee expertise and institutional ownership concentration. The model demonstrates that governance effectiveness follows a power-law distribution rather than the normal distribution assumed in traditional research, with certain governance configurations creating disproportionately positive audit outcomes. Additionally, we identify emergent properties in governance networks where the whole system's impact on audit quality exceeds the sum of individual governance mechanisms. This research contributes to both accounting literature and computational social science by providing a more nuanced, dynamic, and comprehensive understanding of how corporate governance structures influence audit quality, while introducing methodological innovations that can be applied to other complex organizational research questions.