Business Failure Prediction by Hybrid Data Mining Approach: A Case of Thailand Agribusiness - Pumjaroen, Jeerawadee (Journal of Applied Statistics and Information Technology, 2023)
The failure of a business could significantly impact private companies, the government, and the whole economy. Therefore, predicting business failure is always one major research problem in business and economics. Many methods, such as theoretical models, statistical models, and data mining techniques, were applied to predict business failures. This research developed a business failure prediction model to classify failed and non-failed companies from one to three years before the failure by a hybrid data mining technique. The interest of this research is to integrate clustering and classification techniques to predict business failure, which can be beneficial for further research related to business failure prediction or early warning models. The study involved 3,118 agribusiness companies that submitted their financial statements from 2016 to 2020 in Thailand. Based on the data of financial statements, a single classifier, including decision tree (DT), logistic regression (LR), and neural network (NN), was compared with a hybrid data mining technique—clustering and classification. The results showed that applying the hybrid method, k-mean and DT, helped to improve the business failure prediction performance.