Predicting the listing statuses of Chinese-listed companies (PLSCLC) is an important and complex problem for investors in China. There is a large quantity of information related to each company's listing status. We propose an improved filter feature selection method to select effective features for predicting the listing statuses of Chinese-listed companies. Due to the practical concerns of analysts in finance about the performance and interpretability of the prediction models, models based on decision trees C4.5 and C5.0 are employed and are compared with several other widely used models. To evaluate the models' robustness with time, the models are also tested under rolling time windows. The empirical results demonstrate the efficacy of the proposed feature selection method and decision tree C5.0 model. (C) 2017 Elsevier B.V. All rights reserved.