Based On Extension Detecting The Stock Market Risk Assessment System

It is already the end of night-mare for most China stock investors since 2001,when the ShangHai Index re-stand up at the point of 1300 and will touch the 1400 in short times, people getting more and more interested in stock investment. Equity investment returns and risks are often directly proportional, that is, higher investment income, the greater the risk. Therefore, the stock market risk assessment methods research is extremely important values and the application of theoretical significance. However, the complexity of the impact of stock price volatility of external factors determine the magnitude of the task, and traditional tools can not meet this challenge.This in-depth study could extension detection methods and methods of predicting stock prices based on the proposed establishment of the stock material and the use of neural network models to achieve matter-element translate method. The stock market is an extremely complex nonlinear dynamics systems, and neural network is highly nonlinear approaching capacity and self learning, adaptive attributes of this data shows that the integration of Extenics theory and neural network modelling of the stock market can be achieved relatively good short-term risk assessment results.In the traditional system to forecast trends in the stock correct rate forecast evaluation criteria deficiencies, the paper used to extension detection technology as the credibility of the assessment value and the actual value between evaluation criteria, In the data used, as a representative of the five sub-stock as a research goal for the certification results more convincing.In the final part of this paper, the function and the flaws of the system that design by myself are introduced, and the extension functions of the system are also described.This paper is supported by National Natural Science Foundation of China(6027089),and Guangdong Provincial Natural Science Foundation of China(980406).