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Predicting the Istanbul Stock Exchange Index Return using Technical Indicators: A Comparative Study

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  • Senol Emir

    (Beykent University, Computer Programming, Istanbul, Turkey)

Abstract

The aim of this study to examine the performance of Support Vector Regression (SVR) which is a novel regression method based on Support Vector Machines (SVM) approach in predicting the Istanbul Stock Exchange (ISE)National 100 Indexdaily returns. For bechmarking,results given by SVR were compared to those given by classical Linear Regression (LR). Dataset contains 6 technical indicatorswhich were selected as model inputsfor 2005-2011 period. Grid search and cross valiadation is used for finding optimal model parameters and evaluating the models. Comparisons were made based on Root Mean Square (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Theil Inequality Coefficient (TIC) and Mean Mixed Error (MME) metrics. Results indicate that SVR outperforms the LR forall metrics.

Suggested Citation

  • Senol Emir, 2013. "Predicting the Istanbul Stock Exchange Index Return using Technical Indicators: A Comparative Study," International Journal of Finance & Banking Studies, Center for the Strategic Studies in Business and Finance, vol. 2(3), pages 111-117, July.
    • Handle: RePEc:rbs:ijfbss:v:2:y:2013:i:3:p:111-117
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