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

Abstract

The prediction of financial distress has received considerable attention in accounting and corporate financial literatures for decades. Various quantitative prediction methods based on financial ratios derived from financial statements have been proposed. This paper uses SOM neural network technology to quantitatively classify the financial distress prediction indexes in Sinopec Corp. and its subsidiaries, which is particularly important for the financial distress prediction modeling process. The Case study of Sinopec Yizheng Chemical Fibre Company Limited is carried out at section 4. And the statistics result shows that even for the same enterprise, the contents and the numbers of the selected evaluation indexes in financial distress prediction model are different during the different periods the enterprise enters.