YAPAY SİNİR AĞLARI MODELLERİ İLE HİSSE SENEDİ GETİRİ TAHMİNLERİ

Abstract

İstanbul Menkul Kıymetler Borsası (İMKB) endekslerinin yapay sinir ağları modelleri ile tahmin edilebilirliği çeşitli çalışmalarda irdelenmiştir. Fakat söz konusu modellerle İMKB’de işlem gören hisse senetlerinin getirileri tahmin edilebilirliği üzerine bulgular bulunmamaktadır. Bu çalışmada yapay sinir ağları modellerinin, IMKB-30 endeksi içersinden seçilmiş hisse senetlerinin günlük getirilerini tahmin güçleri araştırılacaktır. Modellerin tahmin güçleri,işlem karlılık ölçütü doğrultusunda değerlendirilecektir. Çalışmanın sonuçları yapay sinir ağları modellerinin incelenen dönemlerin büyük çoğunluğunda al-ve-tut stratejisine üstünlük sağladıklarını göstermiştir.

Keywords

• Yapay Sinir Ağları Modelleri, Hisse Senedi Getirilerinin Tahmin Edilmesi, Çok katmanlı Algılama Modelleri

STOCK RETURN FORECASTS WITH ARTIFICIAL NEURAL NETWORK MODELS

Abstract

Although several studies have examined the power of the artificial neural network models in predicting Istanbul Stock Exchange (ISE) indexes, there is no evidence on the predictive power of these models for ISE traded stock returns. This paper intends to examine the power of neural network models in prediction of daily returns of the selected stocks from ISE-30 index. The performance of the neural network models are evaluated by trading profits. The results of the study presented that the neural network models could beat the buy-and-hold strategy for most of the periods under investigation.

Keywords

• Artificial Neural Network Models,Stock Return Prediction,Multilayer Perceptron Models

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