Neuro-Genetic Structure to valuation of Initial Public Offering

Considering stock market history, major concerns in the first phase to enter the capital market is that what the right price for the initial public offering and could they convince investors to buy shares. Besides that, there are also investors concerns about the accuracy of the pricing stocks.
This study uses nonlinear method has resolved this issue.
Study provides a model pricing initial public offering of shares on the Tehran Stock Exchange. The research period between 1382 to 1393.
Research population 145 enterprises entering the Tehran Stock Exchange in this period of time and the sample of study is according to the condition of the Company and continuous investment of funds and access to company data, were reduced to 103 companies. The proposed network is a neural network optimized the genetic algorithm to determine the price of shares of new companies entering the stock exchange.With a choice of 12 variables affecting the price of initial public offerings and one dependent variable (Initial Public Offering price) suitable model to _ pricing than other linear models presented. The results of the fourth measure, RMSE, MAE, R-SQUARE, U-THEIL reflect the correct pricing proposed model, in most cases.