Determinants of Corporate Bankruptcy: A Logistic Regression Analysis with Evidence from Taiwan

Corporate bankruptcy has significant implications for investors, governments, and society. Predicting bankruptcy through financial indicators provides an early-warning mechanism to mitigate risks. Previous studies have commonly employed financial ratios, logistic regression, and machine learning methods. However, many existing studies focus more on the performance of the model itself instead of interpretability. The aim of this study is to analyse publicly available company data from Taiwan, apply mutual information and correlation-based feature selection, and estimate a logistic regression model to identify the most important factors influencing bankruptcy positively or negatively. By combining the feature selection step with a transparent and interpretable model, this study contributes to the field in two ways: first, it provides a list of key financial ratios under a trustworthy dataset; and second, it provides interpretable evidence on how multiple elements, such as debt ratio, affect bankruptcy risk. The findings are intended to inform firm managers, lenders, and regulators by offering a practical set of early-warning indicators.