Financial Distress Prediction Model Based On RS-SVM-data Mining Technology

Financial distress prediction is an important area of research of financial management and investment management because the financial status or whether financial difficulties are related to the state enterprises to develop their own strategies and adjustments, related to its creditors or the interests of investors. China's capital markets to flourish today, to judge the financial situation of enterprises and financial difficulties of forecasting has important theoretical and practical significance in particular. Financial distress prediction analysis through the open-to-business accounting statements issued by enterprises and a number of macroeconomic indicators issued by country, and use the scientific methods of forecasting and decision-making, to judge the enterprise's financial position, in order to predict the probability of financial distress over a period of time. The purpose of this article is to examine the general corporate financial distress prediction method to put restrictions on the size of a business, trade constraints, such as limited ownership structure can be widely used method of forecasting the financial difficulties. The paper believes that the financial difficulties of enterprises to improve their short-term forecast accuracy of the forecast called for the full index system. Papers on the basis of numbers of data mining indicators, extract of the largest short-term prediction index system and apply the RS-SVM forecasting methods, combined with Shanghai and Shenzhen listed company data through a purely mathematical analysis to verify the idea of forecasting, prediction index system and method of forecasting the feasibility of reconstruction and the results of data mining to enhance the availability of the method, that is, the application of meta-learning algorithm. The main outcome of the innovative work include the followings: First of all, in this paper, at home and abroad, there has been a comprehensive analysis of studies on the basis of that enterprise's financial plight of the basic connotation of the enterprise is an unhealthy financial state of its main performance is not due to fulfill the financial obligations of means or will not be able to fulfill their financial obligations due, enterprises can not afford to go the final or going bankrupt. Secondly, innovate the enterprise's financial plight of the forecast index system. This article believes that the low level of corporate governance and environmental factors such as changes led to the emergence of corporate financial difficulties, the different causes of the financial difficulties indicate the different lead time. So do financial projections on the plight of the inevitable impact of the introduction of all possible factors, such as the theory of corporate governance indicators, the nature and structure of the equity index system, macro-economic indicators and so on. Finally, the Rough-set (RS) and support vector machine (SVM) integrated forecasting method adopted by the reduction of input support vector machines to improve their properties out and the forecast accuracy. Based on benchmark test data sets statistical analysis shows that the proposed method of prediction accuracy is better than general support vector machines and other classification algorithms such as RBFNetwork, J48, ADTree. The empirical results of the short-term forecasts show that this method can achieve the best short-term forecast accuracy.