Random Regression Forest Model using Technical Analysis Variables: An application on Turkish Banking Sector in Borsa Istanbul (BIST)

Senol Emir, Hasan Dincer, Umit Hacioglu and Serhat Yuksel
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Senol Emir: Asst. Prof., Faculty of Economics, Istanbul University, 34126 Beyazit, Istanbul, Turkey
Hasan Dincer: Prof. of Finance, Istanbul Medipol University, School of Business and Management, Beykoz, 34810, Istanbul, Turkey
Serhat Yuksel: Asst.Prof. of Economics & Finance, Konya Food & Agriculture University, Faculty of Social Sciences and Humanities, Konya, Turkey

International Journal of Finance & Banking Studies, 2016, vol. 5, issue 3, 85-102

Abstract: The purpose of this study is to explore the importance and ranking of technical analysis variables in Turkish banking sector. Random Forest method is used for determining importance scores of inputs for eight banks in Borsa Istanbul. Then two predictive models utilizing Random Forest (RF) and Artificial Neural Networks (ANN) are built for predicting BIST-100 index and bank closing prices. Results of the models are compared by three metrics namely Mean Absolute Error (MAE), Mean Square Error (MSE), Median Absolute Error (MedAE). Findings show that moving average (MAV-100) is the most important variable for both BIST -100 index and bank closing prices. Therefore, investorsshould follow this technical indicator with respect to Turkish banks. In addition ANN shows better performance for all metrics.

Keywords: Random Forest Regression; Artificial Neural Networks; Technical Analysis; Banking Sector; Variable Importance (search for similar items in EconPapers)
Date: 2016
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