Abstract: The purpose of this study is to provide a useful model for predicting the stock prices of companies listed in the Tehran Stock Exchange (TSE) during the Global Financial Crisis (GFC). Linear and exponential regression method and Artificial Neural Networks (ANNs) were used for this purpose. Then a comparison was done between the methods to determine the most effective of them for predicting the stock prices in the TSE. In this study, the stock prices were modelled by using the variables of the growth rate of industrial products, companies’ net assets, inflation, oil prices, earning per share and price ratio of dividends. The study uses a body of data from 250 companies in the years, 2008-2011. The results showed that the correlation coefficients for the linear and exponential regressions are equal to 30.5 and 35.1%, respectively and for ANNs, they are 86.041%. This shows the tangible superiority of ANNs for predicting stock prices compared to classical methods. This means that investors must use scientific methods to forecast stock prices instead of using traditional methods, especially during the GFC.