Study On Prediction Model Of Financial Distress In Listed Companies

Listed companies'sustainable development is the cornerstone of the steady development of security market, prediction of listed company's financial distress timely and effective is a powerful weapon of listed companies'sustainable development. And prediction model of financial distress is the tool of prediction of financial distress, so we focuses on the research of prediction model of financial distress in listed companies, and systematically investigates the issues related listed companies'financial distress in view of China's national conditions in this paper.Firstly, we analysis the character and mechanism of Product of financial distress with financial cycle, and on this basis analysis the factors including internal factor (financial ratios and scale of enterprise) and external factors (business cycle and industry business cycle), which affect financial status in full range. Then, we compare the models on prediction of financial distress, and proposed Logistic-ANN combination model for financial distress prediction that is conform to listed companies in China. At last, mechanical manufacturing companies listed in a share market are selected as samples from 2006 to 2009. Then Logistic-ANN combination model is proposed to predict the financial distress by empirical data.(1) When selecting the information on prediction of financial distress, we should choose the information of financial reporting internal as the core, and we also choose the information of business cycle and industry business cycle as the external environmental information of Macroeconomic and Industrial Economy. Besides, we must take into account the impact of business cycle and industry business cycle on the information of financial reporting internal. (2) PMI (Purchasing Manager's Index) can reflect the trend on movement of financial position in China's manufacturing enterprises. This indicator should be taken more attention, when predicting manufacturing enterprises's financial distress. (3) Based on select indicators and samples, Logistic-ANN combination model is proved to be of great accuracy in prediction.