Estimation of Net Present Value (NPV) in industrial and mine projects using General Regression Neural Network

Subject Areas : Journal of Investment Knowledge

Hossein Badiei ¹ , Mahyar Yousefi ² , Taleb Pargar ³

1 - Faculty member of Islamic Azad University, Garmsar Branch, Department of Accounting, Semnan, Iran & Ph.D Student of Accounting, Allameh Tabatabaee University, Tehran, Iran
2 - Assistant Professor of Malayer University, Department of Mining, Faculty of Engineering, Hamedan, Iran
3 - Graduated M.A. Student in Business Administration, Islamic Azad University, Qeshm International branch, Hormozgan, Iran. (Corresponding Author)

Received: 2015-01-10 Accepted : 2015-02-23 Published : 2015-02-23

Keywords: Net Present Value, Industrial and mine projects, General regression neural netw,

Abstract :

In economic studies of industrial and mine projects and estimation of their net present value (NPV) there are many factors as uncertain variables related to the future. Therefore, such studies should be carried out based on forecasting. To obtain reliable results in these situations, risk analyses methods under uncertainty are used. One of these uncertainty methods is to employ models to be simulated. In these models, some factors as random variables will be studied for the future. Future prediction of each random variable is assessed considering a probability distribution function. The aim of this research work is economic evaluation of Koohzar gold mine in Torbate-Heydariyeh, Iran for a seven years period using simulation and its application in risk and decision management, and estimating its NPV by applying General Regression Neural Network artificial intelligence method. For this purpose, first, probability distributions of the variables were obtained using information from the variables in previous years. Next, distribution functions of uncertain variables are replaced in appropriate cells of discounted cash flow (DCF) table. Then, random sampling was taken from the probability distributions of uncertain variables as an input of cash flow analysis. In the next stage, based on the simulation technique, probability distribution for NPV variations was obtained as the output in the form of graphs and a function. Considering the output, all of the NPV variations can be forecasted. Then, a general regression neural network was designed using simulated results for NPV prediction using input variables. The results show high reliable capability of general regression neural network in prediction of NPV. 

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