Predicting the initial public offering price using a neural network based on genetic algorithm and comparing the bid price of the model with Op

Document Type : Original Article


1 PhD Student in Finance, Rasht Branch, Islamic Azad University, Rasht, Iran

2 Associate Professor, Babol Branch, Islamic Azad University, Babol, Iran

3 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.


Main Subjects

Abdi, A .,  Malekian, E &  Basti, K .(2015). Reviewing factors affecting the pricing less than companies’ initial public offering of shares listed in Tehran Stock Exchange. Journal of Scientific Research and Development ,2 (3), 96-99.
Al Qaisi, F., Tahtamouni, A & AL-Qudah, M .(2016). Factors Affecting the Market Stock Price - The Case of the Insurance Companies Listed in Amman Stock Exchange. International Journal of Business and Social Science. 7(10), 81-90.
Agathee,U. S ., Brooks, C., & Sannassee, R. V (2012). Hot and Cold IPO Markets: The Case of the Stock Exchange of Mauritius. Journal of Multinational Financial Manangement, 22(4), 168-192.
Emamgholizade, S., Parsaeian, M., & Baradaran, M. (2015). Seed yield prediction of sesame using artificial neural network. European Journal of Agronomy. 68(3), 89–96.
Gupta, C. P. & Suri, S. (2017). Valuation  and Pricing of Indian IPOs. 21 (4), 375-385.
Kao, L., & Chen, A (2020). How a pre-IPO audit committee improves IPO pricing efficiency in an economy with little value uncertainty and information asymmetry. Journal of Banking and  Finance, 110(23), 1056 – 1088.
Malhotra, N., & Tandon, K. (2013). Determinants of Stock Prices: Empirical Evidence from NSE 100 Companies. International Journal of Research in Management & Technology. 3(3).
Mohammadi, M. R., Khaleghi, A., Moti Nasrabadi,A., Rafieivand,S., Bego,M., & Zarafshan,H. (2016). EEG Classification of ADHD and Normal Children Using Non-linear Features and Neural Network. Biomedical Engineering Letters, 6(11), 66-73.
Sharif, T., purohit, H., & Pillai, R. (2015). Analysis of Factors Affecting Share Prices: The Case of Bahrain Stock Exchange. International Journal of Economics and Finance, 7(3).
Verpoort, P.C., Macdonald,P., & Conduit,G. T. (2018). Materials data validation and imputation with an artificial neural network. Computational Materials Science, 147(4), 176-185.
Zheng, Z. & Zheng, Z. (2018). Towards an improved heuristic genetic algorithm for static content delivery in cloud storage. Computers and Electrical Engineering, 69(4), 422-434.