The Analysis Of Investment Value Of Listed Companies Based On Aritificial Neural Network

With the comprehensive propulsion of Chinese financial reform, stock has became an important investment means of society investment, which raises a large number of capital for Chinese enterprise, promotes reasonable allocation of social resource, makes a great and profound influence on economic life. On the background of economic globalization and financial liberalization, Chinese stock market improves itself toward standard operation. Knowing the influence factors of stock investment value correctly could provide listed company's stock investment value with reliable and scientific basis, which benefits not only investor to decrease investment risks but also government to supervise and inspire enterprise.This thesis summarize research status and relating theories of domestic and foreign listed company's stock investment value evaluation method, from three aspects, macro, meso and micro, systematically analyzes and summarizes the influence factors of stock intrinsic value, basing on which, this thesis selects eleven evaluation indexes as the model's index system. Using the theories of BP neural network, this thesis selects 113 listed enterprises from Chinese stock markets of Shanghai and Shenzhen as empirical analysis. This thesis uses fixed growth model from free discounted cash flow model to evaluate the stock intrinsic value, and also does principal component processing of the numbers, reduces input variables, uses trail-and–error to ensure training function and hidden layer node number of neural network model, and then, builds a perfect BP network model, which is used to predict and comprehensive analysis. At last, according to the empirical results to evaluate the model, and gives some policy suggests.