Publication: Stock market forecasting with artificial of literature and an application on ISE-30 index
Abstract
Son yıllarda yapay sinir ağları modelleri gerçek hayata dair pek çok problemin çözümünde yaygın şekilde kullanılmakla beraber, bu modellerin geliştirilmesi aşamasında halen bazı problemler bulunmaktadır. Bu çalışma hisse senedi piyasası tahminlerinde yapay sinir ağları modelleri uygulamalarını kapsamlı şekilde incelemeyi amaçlamıştır. Çalışmanın temel hedefi literatüre geçmiş uygulamaları özellikle modelleme yönünden irdeleyerek, ileride yapılacak araştırmalar için faydalı ve güncel bilgiler sunmaktır. Bunu yanında, IMKB-30 endeksinin günlük getirilerini tahmin etmeye yönelik bir uygulama yapılmıştır.
Although the artificial neural network models have been increasingly applied to solve variety of real life problems in last few decades, there are still some modeling problems exists in development of these models. This paper intends to provide a comprehensive review of the artificial neural network applications in stock market forecasting. Our goal is to provide a useful and up-to-date analysis of the literature, which will guide the future studies, by placing a special emphasis on the modeling issues. Furthermore, an application of neural network models for predicting the daily returns of ISE-30 index is presented.
Although the artificial neural network models have been increasingly applied to solve variety of real life problems in last few decades, there are still some modeling problems exists in development of these models. This paper intends to provide a comprehensive review of the artificial neural network applications in stock market forecasting. Our goal is to provide a useful and up-to-date analysis of the literature, which will guide the future studies, by placing a special emphasis on the modeling issues. Furthermore, an application of neural network models for predicting the daily returns of ISE-30 index is presented.