Assessing the Impact of Audit Quality on Capital Market Efficiency and Investor Decision-Making
Posted: Sep 09, 2013
Abstract
This research introduces a novel computational framework for quantifying audit quality through machine learning analysis of auditor behavioral patterns and linguistic signatures in audit documentation. Traditional audit quality metrics have relied heavily on binary indicators and static financial ratios, failing to capture the nuanced dimensions of auditor judgment and professional skepticism. Our methodology employs natural language processing and deep learning techniques to analyze over 50,000 audit working papers and communications, extracting subtle patterns in auditor questioning techniques, documentation completeness, and risk assessment thoroughness. We develop a multi-dimensional Audit Quality Index (AQI) that incorporates both quantitative financial metrics and qualitative behavioral indicators. Through experimental market simulations involving 1,200 professional investors, we demonstrate that higher AQI scores correlate significantly with improved capital allocation efficiency and reduced information asymmetry. Our findings reveal that investors exposed to high-quality audit signals make capital allocation decisions that are 27
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