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Title |
Using A Trainable Neural Network Ensemble for Trend Prediction of Tehran Stock Exchange |
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Author |
Hossein Nikoo, Mahdi Azarpeikan, Mohammad Reza Yousefi, Reza Ebrahimpour, Abolfazl Shahrabadi |
| Citation |
Vol. 7 No. 12 pp. 287-293 |
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Abstract |
This paper represents a study of neural network ensembles for stock price trend prediction. The historical data available in this case study are from Kharg petrochemical company in TSE (Tehran stock Exchange). This company is a big producer of petrochemicals, including methanol, in Iran and its stock price is very much dependent on world methanol price. The results show how neural network ensembles can overcome just a Multi-layered Perceptrons (MLPs), as a Non-parametric combinatorial forecasting method. This study also demonstrates how we can bit the market without the use of extensive market data or knowledge. |
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Keywords |
Trainable neural network ensemble, Stock price trend prediction, Tehran Stock Exchange, Iran |
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