The Comparison of Methods Artificial Neural Network with Linear Regression Using Specific Variables for Prediction Stock Price in Tehran Stock Exchange

Reza Gharoie Ahangar · Mahmood Yahyazadehfar · Hassan Pournaghshband

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Stock Market Forecasting Methods Neural Networks and Applications Forecasting Techniques and Applications

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.

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References

1982The accuracy of extrapolation (time series) methods: Results of a forecasting competition 2001Principles of forecasting : a handbook for researchers and practitioners 2000Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index 2005The use of data mining and neural networks for forecasting stock market returns 2008Using neural networks and data mining techniques for the financial distress prediction model 2004A comparison between Fama and French's model and artificial neural networks in predicting the Chinese stock market 2006The development of a weighted evolving fuzzy neural network for PCB sales forecasting 2003Using neural networks for forecasting volatility of S&P 500 Index futures prices

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