Advanced Machine Learning Techniques for Corporate Bankruptcy Prediction: A Gradient Boosting TreeNet Approach

Abstract

This study aims to enhance bankruptcy prediction models by employing the gradient‐boosting TreeNet algorithm. This research assesses the predictive accuracy of TreeNet in bankruptcy classification using a high‐dimensional approach. Specifically, it categorizes bankruptcy as nonbankrupt, Chapter 7, or Chapter 11 categories. This research utilizes a large dataset comprised of 76,069 firm‐year observations for the years between 1991 and 2019. The findings highlight the high classification accuracy of TreeNet, particularly in predicting Chapter 7 cases, and its robust performance across different time horizons. The TreeNet prediction model is a valuable tool for decision‐making and risk management in finance, auditing, and policymaking sectors.