Using a Modified Trainable Neural Network Ensemble for Trend Prediction of Tehran Stock Exchange (Case Study: Kharg Petrochemical Company)

This paper represents a comparison between modified trainable neural network ensemble with other trainable and non-trainable ensembles. The historical data available in this case study are from kharg petrochemical company in Tehran stock exchange. This company is one of the biggest producers of petrochemicals, including methanol, in Iran and its stock price is very much dependent on world methanol price. Therefore Kharg stock price reflects its financial information more clearly than others with no products for global exportation. The reason of choosing Kharg is related to its large data history and high rate of stock free float1. The results show how a modified trainable neural network ensemble can overcome other trainable and non-trainable ensembles. This study also demonstrates how we can beat the market through our proposed model without the use of extensive market data or knowledge.