An Empirical Study Of Listed Companies' Financial Early Warning

Financial health is the basis of business, without a healthy financial position sound businesses at all times at a great risk and financial risk is relatively the easiest we have to guard against risk, because the relatively volatile economic environment in the industry changes in operating circumstances, the financial situation of enterprises financial information is our most accessible, and with the country accounting information to increase the quality of the attention, a listed company information disclosure system, the strengthening of the popularization of information technology networks, enterprises, especially listed companies, the relative real financial data is becoming increasingly vulnerable to the general public access, then the financial data through which we can also dialysis company's financial operations. Because the accounting information contained in the information is quite rich, through the data mining, we can get a lot of information of enterprises, and this work is to carry out more and more professionals, accountants and securities analysts economist from their respective needs mining point of view of financial information, the financial information as a corporate financial early-warning is a lot of people to explore a different direction.Financial early-warning is through the analysis of corporate financial data to predict the occurrence of financial crises, specifically the use of selected financial ratios, building models to determine the likelihood of financial crises enterprises. It is a predictive model can be used for credit analysis and so on. An effective early-warning model is considered the financial, monitoring, diagnosis, control, prevention and so on. To establish an effective financial early-warning model, can help enterprises to reduce operational risks and promote stable development of market discipline. At present, the use of corporate financial statements, data, application of statistical methods for analysis of each variable to predict the company's financial risk, in order to reveal the risks and timely and effective measures to guard against and defuse financial risks, has become the enterprise management authorities, investors, creditors, etc. stakeholders of common concern.In this paper, two methods have done a neural network prediction, the results of two methods closer to, and proves that the method used in this paper the correct rate of return than the logic of a relatively large improvement. In this paper, in the selection of indicators to strengthen cash flow targets in the whole composition of the proportion of indicators, selection of indicators detailed above optimization index and we use two methods to achieve a neural network model, which a few ways to optimize the accuracy of the financial projections. Stressed the cash flow target screen selection indicators, optimizing the composition of the indicators used to model only the financial and accounting point of view to enhance the credibility of the warning. The factor analysis used a combination of neural network model of Canadian early-warning capacity than the original, there has been relatively large increase, with the software, usability enhancements, the method can be a gradual transition to the practical field. The DPS software is proven to be a more appropriate use of neural networks to do the financial early-warning software, it is relatively simple and practical.Another path is deleted in the t test, after the election of indicators, using rough set of indicators for further screening, using the characteristics of rough set to select the indicator corresponds to the results of mapping is not sensitive indicator removed. This can greatly limit keep the original data. After the test comparison,this method came to the conclusion of the warning effect is to compare the three methods in the best.This major innovation is in three aspects, first of all using the latest available data from the entire Chinese stock market to do an analysis of the data more comprehensive. Secondly, using factor analysis and rough set two kinds of methods of dimensionality reduction and do neural network prediction results showed that both better than the logistic regression prediction. Third, the two kinds of software which using the neural network model, and we further make a discussion from theory to application of a simple problem.