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

Posted: May 09, 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 market indicators. We employ multiple classification algorithms including logistic regression, random forests, and support vector machines on a dataset of 2,500 publicly traded companies from 1995-2003. Our methodology incorporates feature selection techniques to identify the most predictive financial variables and implements cross-validation to ensure model robustness. 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 provides valuable insights for financial institutions, investors, and regulatory bodies in early risk detection and mitigation strategies.