Yapay Sinir Ağı Performansına Etki Eden Faktörlerin Analizinde Taguchi Yöntemi: Hisse Senedi Fiyat Tahmini Uygulaması

İlgilenilen problemin yapısına bağlı olarak istatistiksel pek çok tahminleme yöntemi geliştirilmiştir. Bir yapay zeka tekniği olan Yapay Sinir Ağı (YSA); tanıma, sınıflandırma, tahminleme ve eniyileme konularında kullanılan etkili bir tekniktir. YSA’da model belirleme problemi literatürde önemli bir konu olarak ele alınmaktadır. Bu çalışmada, YSA performansını etkileyen faktörlerin analizi ve performansını iyileştiren uygun değerlerin belirlenmesinde taguchi yöntemi kullanılmıştır. Uygun faktör değerleri belirlenmiş YSA, İstanbul Menkul Kıymetler Borsası’nda (İMKB) işlem gören Koç Holding hisse senedinin fiyat tahmini problemine, Ocak 96- Aralık 01 dönemi için derlenen veriler dikkate alınarak uygulanmıştır. Yaklaşımın etkinliğini belirlemek ve YSA’da model seçiminin önemini vurgulamak amacıyla, elde edilen sonuç rassal olarak tasarlanmış bir YSA ve çoklu doğrusal regresyon modeli ile karşılaştırılmıştır.

Anahtar Kelimeler:

Taguchi Yöntemi, Yapay Sinir Ağı, Regresyon Analizi, Tahminleme, Hisse Senedi

Analyzing Performance of Artificial Neural Networks by Taguchi Methods: Forecasting Stock Market Prices

A wide variety of statistically based forecasting methods have been developed depending on the nature of the problem concerned. An Artificial Neural Network (ANN), one of the artificial intelligence techniques, is considered to be a powerful tool for recognition, classification, forecasting, and optimization. The design of an ANN architecture has been widely recognized as an important issue in the literature. In this paper, Taguchi method was used to analyze tha factors that affect the performance of an ANN and to determine appropriate values of the factors regarding to improving the performance of an ANN. A stock, namely Koç Holding, has been chosen from IMKB for application purpose and the optimized ANN has bee applied to forecast prices of the selected stock using data in the period of Jan96-Dec 01. In order to determine efficiency of the approach and to emphasize the necessity of performance optimization in ANN, the result obtained was compared with a randomly designed ANN and the multiple regression model.

Keywords:

Taguchi Method, Artificial Neural Network, Regression Analysis, Forecasting, Stock,

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