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 tlien 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.
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A COMPARISION OF NEURAL NETWORK AND LINEAR REGRESSION FORECASTS OF THE ISE-100 INDEX
Semantic Scholar · Computer Science · 2020
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 tlien 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.