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Predicting the Istanbul Stock Exchange Index Return using Technical Indicators

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  • Senol Emir

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 Index daily returns. For bechmarking, results given by SVR were compared to those given by classical Linear Regression (LR). Dataset contains 6 technical indicators which were selected as model inputs for 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 for all metrics.

Suggested Citation

  • Senol Emir, 2013. "Predicting the Istanbul Stock Exchange Index Return using Technical Indicators," 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:id:425
    DOI: 10.20525/ijfbs.v2i3.158
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    References listed on IDEAS

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    1. Asa Ben-Hur & Cheng Soon Ong & Sören Sonnenburg & Bernhard Schölkopf & Gunnar Rätsch, 2008. "Support Vector Machines and Kernels for Computational Biology," PLOS Computational Biology, Public Library of Science, vol. 4(10), pages 1-10, October.
    2. Tay, Francis E. H. & Cao, Lijuan, 2001. "Application of support vector machines in financial time series forecasting," Omega, Elsevier, vol. 29(4), pages 309-317, August.
    3. Vladimir N. Vapnik, 1995. "The Nature of Statistical Learning Theory," Springer Books, Springer, number 978-1-4757-2440-0, December.
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