Mojtaba Zamani Noghabi - Department of Electrical Engineering Islamic Azad University Gonabad, Iran
Mohsen Farshad - Department of Electrical and Computer Engineering University of Birjand Birjand, Iran
Maryam Ashoori - Department of Engineering Higher Educational Complex of Saravan Sarvan, Iran

Ability to predict direction of stock/index price accurately is crucial for market dealers or investors to maximize their profits. Data mining techniques have been successfully shown to generate high forecasting accuracy of stock price movement. Nowadays, instead of a single method, traders need to use various appropriate techniques to gain more information about the future of the markets. In this paper, three different techniques of data mining are discussed and applied to predict price of Tehran stock market. The approaches include decision trees (CART and CHAID) and Neural Network that execute on Saipa, Iran Khodro, Telecommunication, Mapna and Saderat Bank datasets. The aim of this paper is generating an effective predicting model to forecasting future price in Tehran stock market. Price prediction in stock market helps investors for exact and quick identification and more investment on valuable portion. Finally it causes portion basket of investors optimized. Results show that the Neural Network is suitable for producing industries and CART is suitable for services industries. Next, many rule induction that achieved CART modeling, discussed.

Price Prediction; Stock Market; Neural Network; CHAID; CART; Rule Induction