Financial Distress Prediction with Enseamble Machine Learning Models

Abstract

Predicting financial distress is an essential task in corporate finance, as it enables early intervention to prevent bankruptcy and severe economic losses. Traditional statistical techniques, such as logistic regression and discriminant analysis, often struggle to model the nonlinear relationships inherent in financial data [11] [12]. This study introduces an ensemble machine learning framework that integrates Random Forest (RF), Gradient Boosting Machines (GBM), Extreme Gradient Boosting (XGBoost), and Voting Classifiers to improve prediction accuracy. The proposed method is evaluated using a dataset of publicly listed firms, incorporating historical financial ratios, macroeconomic indicators, and binary distress labels. The research methodology includes rigorous data preprocessing, feature selection, and cross-validated model training [5]. Experimental results reveal that ensemble approaches consistently outperform single classifiers in terms of accuracy, F1-score, and AUC-ROC, with the Stacked Ensemble delivering the highest performance [7]. These results highlight the effectiveness of ensemble learning in capturing complex patterns and enhancing robustness in financial distress prediction, offering practical implications for investors, auditors, and policymakers [21] [23].