Forecasting Initial Public Offering Pricing Using Particle Swarm Optimization (PSO) Algorithm and Support Vector Machine (SVM) In Iran

Shaho Heidari Gandoman, Navab Kiamehr, Mahmood Hemetfar


The present study compares the ability of neural networks, support vector machine, and model derived from combining particles swarm optimization (PSO) algorithm and support vector machine (SVM) to forecast the initial public offering pricing. The purpose of this research is to design a model that helps investors recognize the validity of the initial public offering pricing and hunt profitable opportunities. The variables used in this study are selected among those variables which are in the disposal of investors who have limited access to information before the offering. On the other hand, these results can be useful for publishing companies, admissions consultant, underwriting and legislators of the stock exchange. We have considered the ninth day offering prices, since volatilities are gone and prices seem to be more realistic. The results show that the combination of particle swarm optimization (PSO) algorithm and support vector machine (SVM) markedly increases the forecasting power. As a result, support vector machine models can increase the accuracy of initial public offering pricing and provide significant economic benefits as reducing less than real pricing costs.

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Business and Economic Research  ISSN 2162-4860

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