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The Comparison of Methods Artificial Neural Network with Linear Regression Using Specific Variables for Prediction Stock Price in Tehran Stock Exchange Reza Gharoie Ahangar, Master of Business Administration of Islamic Azad University – Babol branch &Membership of young researcher club, Iran. [email protected]

Mahmood Yahyazadehfar Associate Professor of Finance

University of Mazandaran Babolsar, Iran [email protected]

Abstract- In this paper, researchers estimated the stock price of activated companies in Tehran (Iran) stock exchange. It is used Linear Regression and Artificial Neural Network methods and compared these two methods. In Artificial Neural Network, of General Regression Neural Network method (GRNN) for architecture is used. In this paper, first, researchers considered 10 macro economic variables and 30 financial variables and then they obtained seven final variables including 3 macro economic variables and 4 financial variables to estimate the stock price using Independent components Analysis (ICA). So, we presented an equation for two methods and compared their results which shown that artificial neural network method is more efficient than linear regression method.

Hassan Pournaghshband Professor of Computer Science Department, Southern Polytechnic State University

in a problem, they can also model linear processes. For example, the capability of neural networks in modeling linear time series has been studied and confirmed by a number of researchers [8],[11],[26]. One of the major application areas of ANNs is forecasting. There is an increasing interest in forecasting using ANNs in recent years. Forecasting has a long history and the importance of this old subject is reflected by the diversity of its applications in different disciplines ranging from business to engineering. The ability to accurately predict the future is fundamental to many decision processes in planning, scheduling, purchasing, strategy formulation, policy making, and supply chain operations and stock price. As such, forecasting is an area where a lot of efforts have been invested in the past. Yet, it is still an important and active field of human activity at the present time and will continue to be in the future. A survey of research needs for forecasting has been provided by Armstrong [1]. Forecasting has been dominated by linear methods for many decades. Linear methods are easy to develop and implement and they are also relatively simple to understand and interpret. However, linear models have serious limitation in that they are not able to capture any nonlinear relationships in the data. The approximation of linear models to complicated nonlinear relationships is not always satisfactory. In the early 1980s, Makridakis (1982)organized a large-scale forecasting competition (often called M-competition) where a majority of commonly used linear methods were tested with more than 1,000 real time series. The mixed results show that no single linear model is globally the best, which may be interpreted as the failure of linear modeling in accounting for a varying degree of nonlinearity that is common in real world problems [10]. The financial forecasting or stock market prediction is one of the hottest fields of research lately due to its commercial applications owing to the high stakes and the kinds of

Key words: neural network, linear regression, Tehran stock exchange, GRNN

I. INTRODUCTION The recent upsurge in research activities into artificial neural networks (ANNs) has proven that neural networks have powerful pattern classification and prediction capabilities. ANNs have been successfully used for a variety of tasks in many fields of business, industry, and science [24] Interest in neural networks is evident from the growth in the number of papers published in journals of diverse scientific disciplines. A search of several major databases can easily result in hundreds or even thousands of “neural networks” articles published in one year. A neural network is able to work parallel with input variables and consequently handle large sets of data quickly. The principal strength with the network is its ability to find patterns [3] ANNs provide a promising alternative tool for fore asters. The inherently nonlinear structure of neural networks is particularly useful for capturing the complex underlying relationship in many real world problems. Neural networks are perhaps more versatile methods for forecasting applications in that not only can they find nonlinear structures

38 ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security, Vol. 7, No. 2, February 2010

attractive benefits that it has to offer [18]. Unfortunately, stock market is essentially dynamic, non-linear, complicated, nonparametric, and chaotic in nature [21]. The time series are multi-stationary, noisy, random, and has frequent structural breaks [13],[22]. In addition, stock market’s movements are affected by many macro-economical factors ([Miao et al., 2007] and [Wang, 2003]) such as poli