Prediction of ISE 100 index returns by artificial neural network model based on principal component factor analysis

In this study, ISE 100 index return is estimated by using Artificial Neural Network model based on the Principal Component Factor Analysis. The data covers the period of 02.01.1997-30.12.2011 and consist of the daily returns of ISE 100 index, Dollar and Gold prices. The study consists of five sections. In the first section, we describe the purpose of study, method and about the importance of study. In the second section, we describe the basic elements of Artificial Neural Network model. In the third section, we describe the Principal Component Factor Analysis. In the fourth section, application are given, and finally in the fifth section we give the empirical results of the study and conclusion.