With the growing importance of the role of equities to institutional and individual investors, the selection of attractive stocks to reach a higher return performance is a complex challenge. A reliable tool that helps identify the top performing stocks with minimum effort in the selection process can be of great assistance to investors. This dissertation proposes a computation based forecasting system that integrates the generalized regression neural network and two supporting technologies, namely information gain and principal component analysis, to manage stock portfolios.