Posted: Dec 13, 2021
The audit market represents a critical component of financial market infrastructure, serving as a mechanism for ensuring financial reporting quality and maintaining investor confidence. Regulatory changes in this domain have historically aimed to enhance audit quality, promote competition, and address systemic risks. However, the complex interplay between regulatory interventions and market structure remains inadequately understood through traditional economic frameworks. This research addresses this gap by developing a novel computational approach that captures the dynamic, multi-dimensional nature of regulatory impacts on audit market competitiveness and structure. Traditional analyses of regulatory effects have predominantly relied on linear regression models and concentration ratios, which often fail to account for the emergent properties and adaptive behaviors that characterize complex market systems. The limitations of these approaches become particularly apparent when examining the unintended consequences of regulatory changes, such as the emergence of regulatory arbitrage opportunities, the formation of new market niches, and the strategic repositioning of audit firms. Our research addresses these limitations by conceptualizing the audit market as a complex adaptive system where regulatory changes initiate cascading effects across multiple dimensions of market organization. This paper makes three primary contributions to the literature. First, we develop an integrated computational framework that combines network analysis, evolutionary game theory, and machine learning to model regulatory impacts on audit market dynamics. Second, we identify and characterize non-linear market
Downloads: 54
Abstract Views: 1274
Rank: 265231