Examining the Relationship Between Financial Distress Prediction Models and Audit Quality Assessments

Posted: Jun 21, 2021

• Mason Scott
• Mateo Brown
• Mateo Garcia

Abstract

The intersection of financial distress prediction and audit quality assessment represents a critical yet underexplored domain in accounting and computational finance research. Traditional approaches have typically treated these two areas as separate disciplines, with financial distress models focusing on quantitative indicators of corporate instability and audit quality assessments concentrating on procedural compliance and professional judgment. However, the increasing sophistication of predictive analytics and machine learning applications in finance necessitates a more integrated understanding of how these domains interact and influence each other. This research addresses a significant gap in the literature by examining the bidirectional relationship between financial distress prediction models and audit quality assessments. We propose that these two domains are not merely adjacent but fundamentally interconnected, with predictive financial analytics directly shaping auditor behavior and, conversely, audit findings influencing the development and validation of distress prediction frameworks.