Predictive Analytics for Corporate Bankruptcy: A Machine Learning Framework Using Financial Ratios

Posted: Jul 27, 2024

• Wei Zhang
• Maria Rodriguez
• Kenji Tanaka
• Fatima Al-Mansoori

Abstract

This research develops a comprehensive machine learning framework for predicting corporate bankruptcy using financial ratios and accounting metrics. We analyze a dataset of 1,500 publicly traded companies over a 10-year period, employing multiple classification algorithms including logistic regression, random forests, and support vector machines. Our methodology incorporates feature selection techniques to identify the most predictive financial indicators and addresses class imbalance through synthetic data generation. The results demonstrate that ensemble methods achieve 94.2% accuracy in predicting bankruptcy events 12 months prior to occurrence, significantly outperforming traditional statistical models. The study contributes to the accounting literature by providing a robust predictive tool for financial distress assessment and offers practical implications for auditors, investors, and regulatory bodies in early risk detection and mitigation strategies.