Posted: Dec 03, 2023
The relationship between auditor reputation and market outcomes represents a fundamental nexus in financial economics, yet traditional approaches have largely relied on oversimplified proxies for auditor quality. This research introduces a paradigm shift by developing a comprehensive computational framework that captures the multidimensional nature of auditor reputation and its complex transmission mechanisms through financial markets. While existing literature has established broad correlations between auditor brand names and various market metrics, our approach transcends these limitations by integrating advanced computational techniques from natural language processing, network theory, and machine learning to construct a dynamic, granular reputation assessment system. Our research addresses several critical gaps in the current understanding of auditor reputation effects. First, we move beyond the binary classification of Big Four versus non-Big Four auditors that has dominated the literature, recognizing that reputation exists on a continuum with nuanced variations even within auditor tiers. Second, we account for the temporal dynamics of reputation, acknowledging that auditor credibility evolves in response to regulatory actions, litigation events, media coverage, and market perceptions. Third, we examine the network effects of auditor reputation, investigating how reputation spillovers affect not only the audited entities but also their business partners and industry peers. The theoretical foundation of this research integrates signaling theory from economics.
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