Stock selection using General Growing and Pruning Radial Basis Function (GGAP-RBF) neural network

Abstract

Stock Selection using GGAP-RBF (General Growing and Pruning Radial Basis Function) Neural Network In the stock picking or selection problem, the motivation is to design system such that it is able to systematically select stocks or equities that are empirically predicted to perform well in the future. Thus, it should achieve four objectives: 1) to achieve monetary returns in the future; 2) to beat the average market performance; 3) to avoid the bias in human-based stock selection due to psychological reasons; 4) automatically crunching the data to extract useful information based on past information, which can be hardly done manually. Hence, this dissertation presents methodologies to select equities based on soft-computing models and the information gathered from fundamental analysis of stock performance.