Investigating the performance of technical indicators in electrical industry in Tehran's Stock Exchange using hybrid methods of SRA, PCA and Neural Networks

According to high application of electrical industry in different industrial branches, generation and distribution of power energy is one of the most challenges of countries. The next step is its appropriate and qualified distribution after generation of electricity in powerhouses. Hence, this paper investigates the efficiency of cable companies in Tehrans Stock Exchange (TSE) according to key effects of providers in power distribution generation. Prediction price indicator movement has always been a challenging task in the exploitation of time series for forecasting. Exact prediction of price indicator movement may offer numerous privileges for investors. As a result of the complexity of stock market data, development of efficient models is often not simple. This research have combined a number of methods namely as Principal Component Analysis (PCA), Stepwise Regression Analysis (SRA) and Artificial Neural Networks (ANN) by technical analysis tools of financial markets. In proceeding, the efficiency of each set in predicting the indicator trend of stocks' total price, have been compared. Data used in this research have been collected from cable companies in the stock exchange between 2007 and 2013. Using empirical results, this research introduces an efficient set of technical indicators for forecasting total price indicator movement in cable companies in TSE. Other results of this research indicate more accuracy of SRA and neural networks in comparison with PCA and neural networks.