Predictive Analytics for Corporate Bankruptcy: A Machine Learning Framework Using Financial Ratios (gjstudies, 2021)
This research develops a comprehensive machine learning frameworkfor predicting corporate bankruptcy using }nancial ratios and accounting metrics. We analyze a dataset of 1,500 publicly traded companiesover a 10-year period, employing multiple classi}cation algorithms including logistic regression, random forests, and support vector machines. Ourmethodology incorporates feature selection techniques to identify the mostpredictive }nancial indicators and addresses class imbalance through synthetic data generation. The results demonstrate that ensemble methodsachieve 94.2% accuracy in predicting bankruptcy events 12 months priorto occurrence, signi}cantly outperforming traditional statistical models.The study contributes to the accounting literature by providing a robustpredictive tool for financial distress assessment and offers practical implications for auditors, investors, and regulatory bodies in early risk detectionand mitigation strategies.