Document Type : Original Article
PhD Student in Finance, Rasht Branch, Islamic Azad University, Rasht, Iran
Associate Professor, Babol Branch, Islamic Azad University, Babol, Iran
Assistant Professor, Rasht Branch, Islamic Azad University, Rasht, Iran
Appropriate pricing of companies initials public offering shares entering the capital market for the first time is crucial for both business owners and investors. But the pricing of these stocks is influenced by many quantitative and qualitative factors. Nonlinear intelligent systems such as neural networks and genetic algorithms are good tools for accurately predicting initial public offering stock prices. Therefore, the purpose of this study is to predict the stock price of the initial public offering using a neural network based on genetic algorithm and compare the bid price of the model with Op. The statistical population of the study are 421 companies listed on the stock exchange that entered the Tehran Stock Exchange between 2009 and 2017. The statistical sample was reduced to 144 companies according to the mentioned filters. Leading neural network and genetic algorithm have been used to analyse the data. The results showed that the predicted research model for pricing the initial public offering shares is a desirable and optimal model. Comparing the bid price of the model with OP, also showed that the projected price of the model, while being close to OP and meeting the relative price, can fulfil the expectations of investors and company owners in proper pricing of initial public offering shares.