Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange

Authors:
Yakup Kara;Melek Acar Boyacioglu;ÖMer Kaan Baykan
Affiliations:
Selçuk University, Faculty of Engineering and Architecture, Department of Industrial Engineering, Campus 42031, Konya, Turkey;Selçuk University, Faculty of Economics and Administrative Sciences, Department of Business Administration, Campus 42031, Konya, Turkey;Selçuk University, Faculty of Engineering and Architecture, Department of Computer Engineering, Campus 42031, Konya, Turkey
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

Prediction of stock price index movement is regarded as a challenging task of financial time series prediction. An accurate prediction of stock price movement may yield profits for investors. Due to the complexity of stock market data, development of efficient models for predicting is very difficult. This study attempted to develop two efficient models and compared their performances in predicting the direction of movement in the daily Istanbul Stock Exchange (ISE) National 100 Index. The models are based on two classification techniques, artificial neural networks (ANN) and support vector machines (SVM). Ten technical indicators were selected as inputs of the proposed models. Two comprehensive parameter setting experiments for both models were performed to improve their prediction performances. Experimental results showed that average performance of ANN model (75.74%) was found significantly better than that of SVM model (71.52%).