Evaluating the Effects of Mandatory Audit Firm Rotation on Auditor Objectivity and Performance

Posted: Apr 06, 2024

• Owen Miller
• Tristan Wallace
• Micah Allen

Abstract

This research presents a novel computational framework for evaluating the effects of mandatory audit firm rotation on auditor objectivity and performance, employing an agent-based modeling approach combined with machine learning techniques to simulate complex audit market dynamics. Traditional empirical studies in auditing research have been constrained by limited data availability and the inability to isolate causal mechanisms in complex regulatory environments. Our methodology overcomes these limitations by creating a sophisticated simulation environment that models the interactions between audit firms, clients, regulatory bodies, and market forces over extended time horizons. The model incorporates behavioral economics principles, game theory, and reinforcement learning to represent auditor decision-making under various rotation regimes. We examine not only the direct effects on audit quality metrics but also the emergent systemic consequences that manifest through market structure evolution and competitive dynamics. Our findings reveal several counterintuitive outcomes, including non-linear relationships between rotation frequency and audit quality, and identify critical threshold effects that previous research has overlooked. The computational approach enables us to test regulatory scenarios that would be impractical or unethical to implement in real markets, providing valuable insights for policymakers considering audit market reforms. This research contributes to the auditing literature by demonstrating how computational social science methods can address complex regulatory questions that have proven resistant to traditional empirical approaches.