Abstract
This study aims to forecast the movement direction of Istanbul Stock Exchange National 100 Index (ISE-100) using Support Vector Machines (SVM). SVMs' classification performance was compared with Logistic Regression (LR), the other method used in this study, in order to forecast the movement direction of ISE-100 Index. Technical indicators that are among the devices useful for technical analysis in stock prediction were used. These indicators included in models were analysed with LR analysis and then, significant ones were used as independent variables. The analysis includes the data from 03.04.1995 to 19.03.2012. 4226 data were established as daily, weekly and monthly data sets. 4 models were built for each dataset and index movement direction forecasting performance of these methods was evaluated by applying different criteria for each model. The results of this study show that SVMs estimate the movement of ISE-100 Index best with weekly Model 1 (70.0%) among 12 models. Additionally, it is observed that this model has a high level of estimation and the closest increase (82.89%) and decrease (54.68%) direction of ISE 100 Index.
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