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