Evaluating the Impact of External Audit Quality on Firm Performance and Investor Confidence

Posted: Nov 15, 2023

• Declan Greene
• Riley Douglas
• Isabella Ford

Abstract

The relationship between external audit quality and corporate outcomes represents a fundamental concern in financial markets and corporate governance. Traditional approaches to assessing audit quality have predominantly relied on manual review processes, regulatory checklists, and basic quantitative metrics such as auditor tenure, firm size, and industry specialization. These conventional methods, while valuable, often fail to capture the nuanced dimensions of audit quality that manifest through linguistic patterns, behavioral consistency, and procedural subtleties embedded within audit documentation. This research introduces a computational paradigm shift by developing and validating a machine learning framework that extracts previously inaccessible insights from audit reports and related documentation. Our investigation addresses a critical gap in the literature by moving beyond surface-level indicators of audit quality to examine the semantic and syntactic structures that characterize high-quality audit engagements. We propose that audit quality is not merely a function of compliance with standards but rather a multidimensional construct that leaves distinctive patterns in the language, timing, and structure of audit communications. These patterns, when properly decoded through advanced computational techniques, can serve as powerful predictors of both firm performance and investor confidence.