The prediction for listed companies financial distress by using multiple prediction methods with rough set and DempsterShafer evidence theory
It is critical to build an effective prediction model to improve the accuracy of financial distress prediction. Some existing literatures have demonstrated that single classifier has limitations and combination of multiple prediction methods has advantages in financial distress prediction. In this paper, we extend the research of multiple predictions to integrate with rough set and DempsterShafer evidence theory. We use rough set to determine the weight of each single prediction method and utilize DempsterShafer evidence theory method as the combination method. We discuss the research process for the financial distress prediction based on the proposed method. Finally, we provide an empirical experiment with Chinese listed companies real data to demonstrate the accuracy of the proposed method. We find that the performance of the proposed method is superior to those of single classifier and other multiple classifiers.
multiple prediction methods rough set dempstershafer evidence theory financial distress prediction weight