Forecasting Stock prices of banks using artificial neural networks (GMDH)

Abstract

According to the importance of shares and investment forecasts for those in the capital market and insufficient analysis of financial information regardless of other variables and parameters as well as the economic data, behavioral and social sciences od time series which are effective in banks stock price, investors are encouraged toward additional tools and available soft wares. In this study modeling and forecasting stock prices of banks using artificial neural networks (GMDH) is accomplished. Obtained results showed that performance of the models in processing simultaneous effects of turnover, value of transactions and number of transactions on bank stock price is excellent