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 (Faculty member of Islamic Azad University, Garmsar Branch, Department of Accounting, Semnan, Iran & Ph.D Student of Accounting, Allameh Tabatabaee University, Tehran, Iran)
Mahyar Yousefi ² (Assistant Professor of Malayer University, Department of Mining, Faculty of Engineering, Hamedan, Iran)
Taleb Pargar 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. 

References:

یوسفی، م. و غلامی، ر. (1389). مدیریت ریسک و تصمیم گیری در سیستم­های اقتصادی، انتشارات جهاد دانشگاهی صنعتی امیرکبیر.

Adam PM, Nelis PM., 2005, Forecasting inflation with thick models and neural networks. Economic Modeling 22, 848–867.

Anderson, D. R., Sweeney, D. J. and Williams, T. A. (1998). An Introduction to Management Science: Quantitative Approaches to Decision Making. Thomson; South-Western Publication.

Aven, T. (2003). Foundations of RISK Analysis, John Wiley & Sons, 190 pp.

Liefly, F. (1997). Approaches to risk and uncertainty in the appraisal of new technology capital project, International Journal of Production Economics 53, 21-33.

Palisade corporation. (1994). Risk Analysis and Simulation Add-In for Microsoft Excel or Lotus 1- 2- 3'', Windows Version- Release 3, User’s Guide.

Ramasesh, R., Jayakumar, V. and Maliyakal, D. (1996). Inclusion of flexibility benefits in discounted cash flow analysis for investment evaluation: a simulation / optimization mode, European Journal of Operation Research 102, 124-14.

Samis, M., Graha, A. D., Laughton, D. and Poulin, R. (2006). Valuing uncertain asset cash flows when there are no options: A real options approach, Resources Policy 30, 285–298.

Sayadi A. R. , Tavassoli S. M. M., Monjezi M., M., Rezaei M., 2014, Application of neural networks to predict net present value in mining projects, Arabian Journal of Geosciences 7 (3), 1067-1072.

Slater, S F., Venkateshwar K. and Wirlein, J. Z. (1998). Evaluating Strategic Investments, Industrial Marketing Management 27, 447-458.

Specht, D. F. (1991).“A general regression neural network.” IEEE Transactions on Neural Networks., 2(6), 568–576.

Demuth, H., and Beale, M. (2002). Neural network toolbox for use with MATLAB, user’s guide version 4, MathWorks, Natick, MA