Evaluating the Relationship Between Corporate Governance and Financial Restatements in Public Corporations

Posted: Mar 22, 2025

• Joseph Campbell
• Joseph Clark
• Joseph Davis

Abstract

This research presents a novel computational framework for analyzing the complex relationship between corporate governance structures and financial restatements in public corporations. Unlike traditional econometric approaches that rely on linear regression models and predefined governance metrics, our methodology employs a multi-modal neural architecture that integrates both quantitative governance indicators and qualitative textual data from corporate disclosures. The system processes board composition data, executive compensation structures, audit committee characteristics, and corporate policy documents through a hybrid convolutional-recurrent neural network, enabling the identification of subtle governance patterns that correlate with financial reporting quality. Our analysis of a comprehensive dataset spanning 2,500 publicly traded corporations over a ten-year period reveals several counterintuitive findings, including the limited predictive power of conventional governance metrics when considered in isolation and the emergence of previously unrecognized governance configurations that significantly influence financial reporting integrity. The model demonstrates an 87.3% accuracy in predicting restatement likelihood, substantially outperforming traditional logistic regression approaches. This research contributes to the corporate governance literature by introducing a computational paradigm that captures the multidimensional nature of governance quality and its relationship to financial reporting outcomes, while also providing practical insights for regulators, investors, and corporate boards seeking to enhance financial reporting reliability through governance improvements.