Examining the Relationship Between Audit Firm Rotation and the Quality of Financial Statement Audits

Posted: May 18, 2023

• Isabella Ford
• Dahlia Sanchez
• Ethan Woods

Abstract

This research investigates the complex relationship between audit firm rotation and financial statement audit quality through a novel computational framework that integrates machine learning algorithms with traditional econometric analysis. While mandatory audit firm rotation has been widely debated in regulatory circles, existing literature presents contradictory findings regarding its effectiveness. Our study introduces an innovative methodological approach by developing a multi-dimensional audit quality assessment system that evaluates 127 distinct quality indicators across technical competence, independence metrics, and stakeholder perception dimensions. We analyze a comprehensive dataset spanning 15 years and encompassing 3,472 public companies across 12 jurisdictions with varying rotation policies. The research employs a hybrid analytical framework combining natural language processing of audit opinions, neural network-based anomaly detection in financial statements, and Bayesian structural time series modeling to assess causal relationships. Our findings reveal a non-linear relationship between rotation frequency and audit quality, challenging conventional binary perspectives. Specifically, we identify an optimal rotation interval of 12-15 years that maximizes quality benefits while minimizing transition costs. The study also uncovers significant interaction effects between rotation policies and corporate governance structures, suggesting that one-size-fits-all regulatory approaches may be suboptimal. This research contributes to both auditing literature and regulatory policy by providing a sophisticated analytical framework for evaluating complex audit quality dynamics and offering evidence-based recommendations for rotation policy design.