Financial ratios as indicators in bankruptcy prediction: A comparative analysis of statistical and machine learning models

This paper investigates the optimal approach for predicting corporate bankruptcy risk within the context of Vietnam based on financial ratios. As a unique dataset of listed Vietnamese firms from 2010 to 2021 is employed, we confirm that machine learning models for bankruptcy prediction significantly surpass the traditional logistic regression. In addition, our dataset is divided into two subsets for training and testing models with proportions of 75% and 25%, respectively. The results demonstrate that the XGBoost and Random Forest techniques are superior to K-Nearest Neighbor and Logistic Regression in forecasting failure in both periods. Notably, our paper reveals that the predictive performance was slightly decreased compared to the two periods, and the forecasting after one year is higher than two years ahead.