Intelligent Bankruptcy Prediction Models Involving Corporate Governance Indicators, Financial Ratios and SMOTE

Abstract

This study enhances bankruptcy prediction models by investigating synergies between predictors, utilizing a diverse dataset of financial statements and corporate governance data. Rigorous feature selection identifies key financial ratios (FRs) and corporate governance indicators (CGIs) to enhance model interpretability. Multiple machine learning algorithms construct and assess the models, including Logistic Regression, Decision Trees, Random Forests, Support Vector Machines, and Neural Networks. Integration of CGIs with FRs aims to identify effective combinations that improve model performance with an accuracy respectively 90%, 95%, 97%, and 98%. Researchers explore feature weighting techniques and ensemble methods, examining their impact on accuracy, sensitivity, and specificity. The study also explores how regulatory frameworks and governance practices affect bankruptcy prediction, analyzing data across periods to uncover changes in predictive power under varying conditions. The findings have implications for investors, institutions, and policymakers, offering more accurate risk assessments and emphasizing the interplay between financial performance and governance quality for corporate well-being.