Utilizing Generalized Growing and Pruning Algorithm for Radial Basis Function (GGAP-RBF) Network in Predicting IPOs Performance

Finance and investing is the second most frequent business area of neural networks applications after production/operations. Although many research results show that neural networks can solve almost all problems more efficiently than traditional modeling and statistical methods, there are opposite research results showing that statistical methods in particular data samples outperform neural networks. Many papers on neural network applications on stock markets provide forecast only on existing stocks. However, many new stocks are being listed each year. Thus the aim of this study is to explore this relatively un-tapped region in the stock market and to investigate if neural networks can predict the returns of these initial public offerings (IPOs). As for the prediction model, this study uses a proposed sequential learning radial basis function (RBF) and this neural network aims to take advantage of the relationship between time series and firm specific information. Since IPOs have prior information of itself, the predicted values will be based on related time series and variables of the firm specific factors. Experimental results based on IPOs from the Singapore stock exchange are presented to evaluate the performance of the prediction.