Random Regression Forest Model using Technical Analysis Variables
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Keywords

Random Forest Regression
Artificial Neural Networks
Technical Analysis
Banking Sector
Variable Importance

How to Cite

Emir, S., Dincer, H., Hacioglu, U., & Yuksel, S. (2016). Random Regression Forest Model using Technical Analysis Variables. International Journal of Finance & Banking Studies (2147-4486), 5(3), 85-102. https://doi.org/10.20525/ijfbs.v5i3.461

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, investors should follow this technical indicator with respect to Turkish banks. In addition ANN shows better performance for all metrics.

https://doi.org/10.20525/ijfbs.v5i3.461
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