Predicting the listing status of Chinese listed companies with multi-class classification models

In China’s stock markets, a listed company’s different listing statuses are signals for different risk levels. It is therefore vital for investors and other stakeholders to predict the listing status of listed companies due to the difficulty of providing sufficient measurement of such risks. Existing studies tend to classify listing status into two categories for simple measurement purposes by applying binary classification models; however, such classification models cannot provide accurate risk management. Considering the existence of four different listing statuses of Chinese listed companies in practice, this study introduces three different types of multi-class classification models to predict listing status in order to achieve better performance in terms of accuracy measures. These three types of models are based on One-versus-One and One-versus-All with parallel and hierarchy strategies. The performances of the three different models with two different types of feature selection strategies are compared. Further, the effectiveness and accuracy of the models’ performance are tested on a large test dataset. The achieved accuracy measures could provide better risk prediction for listed companies.