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YAPAY SİNİR AĞLARI İLE BORSA ENDEKSİ TAHMİNİ

STOCKMARKETINDEXPREDICTION WITH ARTIFICIAL NEURAL NETWORKS

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Abstract (2. Language): 
Currently, artificial neural networks are applied to many finance problems such as stock market index prediction, bankruptcy prediction or bond classification. Studies were performed for the prediction of stock index values as well as daily direction of change in the index. In some applications it has been specified that artificial neural networks have limitations for learning the data patterns or that they may perform inconsistently and unpredictable because of the complex financial data used. Continuous data and large scale of records require the removal of unnecessary properties which decreases the data volume, algorithm runtime and help to achieve more general results. In Turkey artificial neural networks are mostly used in predicting financial failures. There has been no specific research for prediction of Turkish stock market values. The aim of this paper is to use artificial neural networks to predict Istanbul Stock Exchange (ISE) market index value. The tests are performed using the data gathered for the period of July 2, 2001 through July 13, 2006 from the websites of Central Bank of Republic of Turkey and foreign stock markets. The results have shown that feed forward artificial neural networks can also be used to model ISE market index value successfully.
Abstract (Original Language): 
Günümüzde yapay sinir ağları popüler olarak borsa endeks tahmini, iflas tahmini ya da şirket bono sınıflaması gibi bir çok finans problemine uygulanmaktadır. Çalışmalar, hisse senedi endeks değeri tahmini üzerinde olduğu kadar günlük endeks değişim yönü üzerinde de durmaktadır. Bazı uygulamalarda yapay sinir ağlarının veri kalıplarını öğrenmede kısıtlamaları olduğu belirtilmektedir. Yapay sinir ağları seçkin öğrenme yeteneğini sunmakla birlikte karmaşık finansal veri nedeni ile tutarlı olmayan ve tahmin edilemeyen bir performans gösterebilmektedir. Buna ek olarak veri bazen o kadar hacimli olmaktadır ki öğrenme kalıpları çalışmayabilmektedir. Sürekli veri ve büyük çaptaki kayıtların varlığı nedeni ile gereksiz özelliklerin ayıklanması ve verinin boyutlarının azaltılması algoritmanın işlem süresini kısaltmakta ve daha genellenebilir sonuçlar verebilmektedir. Türkiye'deki yapay sinir ağları çalışmaları genelde finansal başarısızlık ve iflasların tahmini için kullanılmıştır. Yurtdışında borsa endeksi tahmini konusunda çalışmalar olduğu halde Türkiye'de bu tip çalışmaların eksikliği görülmektedir. Bu makaleye konu olan çalışma ile amaçlanan ileri beslemeli yapay sinir ağları yaklaşımı ile İMKB endeksinin tahmin edilebileceğinin gösterilmesidir. Türkiye Cumhuriyet Merkez Bankası ve diğer borsaların İnternet sitelerinden elde edilen 2 Temmuz 2001 ile 13 Temmuz 2006 tarihleri arasındaki veriler kullanılarak yapılan testler sonucunda İMKB endeks değerinin ileri beslemeli yapay sinir ağları ile de başarılı bir şekilde modellenebileceği görülmüştür.
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