Exploring the Relationship Between Auditor Independence and Financial Reporting Quality in Listed Companies

Posted: May 28, 2025

• Wyatt Franklin
• Caden Sanders
• Tobias Romero

Abstract

This research investigates the complex relationship between auditor independence and financial reporting quality through a novel computational framework that integrates machine learning algorithms with traditional econometric analysis. Unlike previous studies that rely primarily on regulatory proxies for independence, we develop a multidimensional independence index using natural language processing techniques applied to auditor-client communications, fee structures, and tenure patterns. Our methodology employs ensemble learning models to analyze financial statement data from 2,500 listed companies across multiple jurisdictions over a ten-year period. The findings reveal a non-linear relationship between auditor independence and reporting quality, challenging conventional linear assumptions. We identify critical threshold effects where marginal improvements in independence yield diminishing returns, and discover contextual factors that moderate this relationship, including industry volatility, corporate governance structures, and regulatory environments. The research introduces a dynamic independence assessment tool that adapts to changing market conditions and provides real-time monitoring capabilities. This study contributes to both accounting theory and computational finance by offering a more nuanced understanding of how independence mechanisms function in practice and developing predictive models that can anticipate reporting quality issues before they manifest in financial statements.