Article citationsMore>>

Wang, G. and Ma, J. (2011) Study of Corporate Credit Risk Prediction Based on Integrating Boosting and Random Subspace. Expert Systems with Applications, 38, 13871-13878.
https://doi.org/10.1016/j.eswa.2011.04.191

has been cited by the following article:

• TITLE: Ensemble Machine Learning Models in Financial Distress Prediction: Evidence from China

  AUTHORS: Yi Ling, Pang Paul Wang

  KEYWORDS: Bankruptcy Prediction, Machine Learning, Ensemble Models

  JOURNAL NAME: Journal of Mathematical Finance, Vol.14 No.2, May 28, 2024

  ABSTRACT: Corporate distress signals are important for both institutions and banks when evaluating firms’ performances. This paper evaluates five different models in predicting the distress for listed companies in China based on 22 dimensions of financial data from 2014 to 2022. The models include three ensemble machine learning models: Adaboost, Bagging, and Random Forest, as well as a single machine learning model Decision Tree, along with a benchmark Logistic Regression. The comparative analysis found Random Forest to be the most promising method with the highest accuracy ratio and lowest Type I and Type II errors. This paper concludes that ensemble learning models could be an easy-to-replicate and highly efficient tool for institutions and banks to evaluate and predict potential distress in firms.