Predicting IPOs performance using Huang’s network

Abstract

The focus of this work is to study the feasibility and practicality of using neural networks as a forecasting tool so as to determine if detectable trends are present in stock performance, particularly on initial public offers. Finance and investing is the second most frequent business area of neural networks applications after productiodoperations. 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 check if neural networks can make the prediction of returns of these IPOs.