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

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...

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Bibliographic Details
Published in:Expert systems with applications 2011-05, Vol.38 (5), p.5311-5319
Main Authors: Kara, Yakup, Acar Boyacioglu, Melek, Baykan, Ömer Kaan
Format: Article
Language:English
Subjects:
Artificial neural networks
Learning theory
Mathematical models
Movement
Neural networks
Prediction
Raw materials
Stock exchanges
Stock price index
Support vector machines
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Summary: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%).
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2010.10.027