The Classification of Financial Distress Prediction Patterns in Sinopec Corp. and Its Subsidiaries Based on Self-Organizing Map Neural Network

Abstract

Sinopec Corp. is one of the largest integrated energy and chemical company in China. It has more than 100 subsidiaries and branches including wholly owned, equity-holding and equity-sharing companies. The fast and accurate classification of financial distress prediction pattern in these companies is significantly important to the process of modeling financial distress prediction. The purpose of this paper is to use self-organizing map (SOM) neural network technique and the standardizing investigation method to effectively classify the different financial distress prediction patterns of Sinopec corp. and its nearly 100 subsidiaries and branches. The Case study of Sinopec Yizheng Chemical Fibre Company Limited is carried out at section 4. And the financial distress prediction pattern of Sinopec Yizheng Chemical Fibre Company Limited is classified into four categories in terms of different periods of financial data.