Predictive Analytics for Corporate Financial Distress: A Machine Learning Framework for Early Warning Systems

Abstract

This research develops a comprehensive machine learning framework
for predicting corporate }nancial distress using accounting and market
data. We employ multiple classi}cation algorithms including logistic regression, random forests, and support vector machines to identify early
warning signals of }nancial distress. Our dataset comprises 2,500 publicly
traded companies across various sectors from 1995 to 2003. The proposed framework achieves 94.2% accuracy in predicting }nancial distress
12 months prior to occurrence, signi}cantly outperforming traditional statistical methods. Feature importance analysis reveals that cash ~ow ratios,
debt coverage metrics, and market-based indicators are the most signi}-
cant predictors. The study contributes to both accounting literature and
}nancial risk management practice by providing a robust, data-driven
approach to corporate }nancial health assessment.