The Relationship Between Audit Tenure and Financial Reporting Quality

Abstract

This study investigates the complex relationship between audit tenure and financial
reporting quality through a novel methodological framework that integrates computational linguistics, network analysis, and quantum-inspired optimization algorithms.
Moving beyond traditional econometric models that have produced conflicting results
regarding the benefits of auditor rotation versus familiarity, we propose a paradigm
shift in how audit quality is conceptualized and measured. Our approach treats the
audit process as a complex adaptive system, analyzing the co-evolutionary dynamics
between auditors and client financial reporting practices over extended engagement
periods. We develop a unique dataset spanning 15 years of audit engagements for
publicly traded companies, incorporating not only quantitative financial metrics but
also qualitative textual data from audit reports, management communications, and
regulatory filings. Using a proprietary Natural Language Processing (NLP) pipeline
enhanced with quantum annealing techniques for pattern recognition, we extract latent
dimensions of reporting quality previously unmeasured in the literature. Our findings
reveal a non-linear, phase-transition relationship between audit tenure and reporting
quality, with critical thresholds at approximately 7 and 12 years where systemic behaviors fundamentally shift. Contrary to prevailing assumptions, we identify an optimal
tenure window between years 4 and 9 where reporting quality peaks, followed by a deterioration phase, and then a surprising resurgence after year 12 driven by institutional
knowledge accumulation. The research contributes original insights by demonstrating
how auditor-client cognitive alignment evolves through distinct stages, creating both
risks and opportunities at different tenure intervals. Our quantum-enhanced analytical framework represents a methodological innovation that enables detection of subtle,
non-linear relationships invisible to conventional statistical approaches, offering regulators and firms a more nuanced evidence base for policy decisions regarding audit
rotation mandates.