A COMPARISION OF NEURAL NETWORK AND LINEAR REGRESSION FORECASTS OF THE ISE-100 INDEX

Authors : Murat Çinko, Emin Avci

Pages : 301-307

Doi:10.14783/maruoneri.684425

View : 22 | Download : 9

Publication Date : 2007-06-10

Article Type : Research

Abstract :In recent years the artificial neural network models have been successfully applied to solve many the real life problems. Especially for the last decade, the artificial neural network models have been applied to solve financial problems like bankruptcy prediction, portfolio construction, credit assessments and stock market forecasting. This study examines the comparison of artificial neural network models and stepwise linear regression forecasting the daily and sessional returns of the ISE-100 index. By using stepwise regression inputs is selected tlıen the same inputs is used in the neural network. Both methods are compared on the basis of mean squared error, normalized mean squared error and trend accuracy measures. Relying the findings of this study, it is concluded that the artificial neural network model is better than stepwise linear regression.
Keywords : Artificial Neural Network Models, Stock Market Forecasting