Evaluating the Employment of Technical Indicators in Predicting Stock Price Index Variations Using Artificial Neural Networks (Case Study: Tehran Stock Exchange)

Stock price index is the initial significant factor influencing on investors' financial decision making. That's why predicting the exact movements of stock price index is considerably regarded. This study aims at evaluating the effectiveness of using technical indicators, such as Moving Average, RSI, CCI, MACD, etc in predicting movements of Tehran Exchange Price Index (TEPIX). An artificial neural network is employed for stock price index forecasting. The existing data are achieved from Tehran Stock Exchange. To capture the relationship between the technical indicators and the levels of the index in the market for the period under investigation, a back propagation neural network is used. The statistical and financial performance of this technique is evaluated and empirical results revealed that artificial neural networks are dominant tools for financial market predicting.