ENTERPRISE FINANCIAL DISTRESS PREDICTION BASED ON BACKWARD PROPAGATION NEURAL NETWORK: AN EMPIRICAL STUDY ON THE CHINESE LISTED EQUIPMENT MANUFACTURING ENTERPRISES

As a key part of the effective prevention of the enterprise financial distress, financial distress prediction has been the attention focus in the theory study and the practical business field. According to the principle of the indicator selection, 11 financial indicators are chosen for the financial distress prediction model. In order to reduce the information redundancy, factor analysis is used to extract five common factors and therefore, the comprehensive score of each sample is obtained. Unlike the traditional ST or non-ST criteria to classify the tested samples, the enterprises are divided into three categories: health, concern, and distress. Furthermore, the prediction model based on Backward Propagation Neural Network is built, trained and tested with the five common factors as input and the enterprise financial conditions of the Chinese Listed Equipment Manufacturing Enterprises a great help for the development of Chinese transportation industry.