Abstract
As capital markets evolve, corporate financial misconduct garners more scrutiny. This study, using data from China's A-share companies (2006-2023), develops a model to predict financial irregularities with the random forest algorithm and SHAP value analysis. It analyzes the influence of corporate governance and executive party traits on non-compliance and their predictive roles. Findings indicate that financial characteristics and governance significantly impact predictions, while executive party traits have a lower influence. The model's AUC improves with the inclusion of executive party characteristics. SHAP analysis highlights feature importance and influence direction. The results offer practical insights for regulators, companies, and investors, aiding regulatory efficiency, governance optimization, and investment decisions, and guide strategies for market health.
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