Dự báo kiệt quệ tài chính của các doanh nghiệp Việt Nam: Ứng dụng Machine Learning - Lê Hồng , Ngọc; Nguyễn Thế , Long; Hồ Thị , Lam; Hồ Thu , Hoài (JOURNAL OF ASIAN BUSINESS AND ECONOMIC STUDIES, 2024)
Forecasting financial distress is one of the important tasks in enterprise risk assessment. In this study, the authors apply machine learning algorithms to predict the financial distress and consider factors affecting financial distress ability of Vietnamese companies. This article uses data of 657 listed companies on the HOSE and HNX over the period of 2009–2022 with six algorithms, including Logistic Regression, KNN, Decision Trees, Random Forests, AdaBoost, and XGBoost. The results show that the XGBoost algorithm is the most suitable for forecasting financial distress in Vietnam. From these results, this article proposes some management implications and policy implications in choosing a financial distress forecasting model and monitoring factors affecting financial distress for the sustainable development of the company.