Credit rating by clustering algorithm in the Vietnam Stock Exchange market

Abstract

This study employs the K-means clustering algorithm to develop a corporate credit rating framework tailored to the Vietnamese market. By analyzing financial data from 568 non-financial firms listed on the Ho Chi Minh City Stock Exchange and the Hanoi Stock Exchange between 2019 and 2023, the research identifies vital financial indicators, including financial health ratios, management efficiency ratios, growth ratios, and dividend payout ratios. The K-means clustering model effectively categorizes these companies into six distinct clusters, each representing different levels of financial performance and credit risk. The clusters range from A+ (very low credit risk) to C (very high credit risk), providing a clear differentiation based on financial stability and operational efficiency. This systematic approach offers valuable insights for investors, managers, and government agencies, enhancing their ability to make informed decisions. Despite some limitations, such as reliance on historical data and sensitivity to initial cluster centroids, the K-means clustering model proves to be a robust starting point for assessing the creditworthiness of companies. This research contributes to the growing body of literature on machine learning applications in credit rating by demonstrating the superiority of clustering algorithms over traditional methods. It highlights how financial health and management efficiency indicators can be integrated into a data-driven framework to enhance credit risk assessment. The results suggest that the K-means clustering approach improves the accuracy of credit ratings and promotes transparency and efficiency in the financial market. Furthermore, the proposed framework can be a foundation for developing more sophisticated models, incorporating additional financial and non-financial variables. Future research could expand on this by integrating real-time data and exploring the impact of external economic factors on credit risk. By leveraging advanced machine learning techniques, this study paves the way for more reliable and comprehensive credit rating systems, ultimately supporting the stability and growth of financial markets in emerging economies like Vietnam.