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

Yıl 2007, , 301 - 307, 10.06.2007
https://doi.org/10.14783/maruoneri.684425

Öz

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.

Kaynakça

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Toplam 32 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Eski Sayılar
Yazarlar

Murat Çinko Bu kişi benim

Emin Avcı Bu kişi benim

Yayımlanma Tarihi 10 Haziran 2007
Yayımlandığı Sayı Yıl 2007

Kaynak Göster

APA Çinko, M., & Avcı, E. (2007). A COMPARISION OF NEURAL NETWORK AND LINEAR REGRESSION FORECASTS OF THE ISE-100 INDEX. Öneri Dergisi, 7(28), 301-307. https://doi.org/10.14783/maruoneri.684425

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