| Abstract: | This article investigates the ability of neural network models to predict mispricing of initial public offerings (IPOs). The aim is to improve the modest explanatory power of existing models that are based on the theory of asymmetrically informed economic agents surrounding post‐issue market value of IPOs. This study develops and compares linear regression and neural network models. The results show that modelling variable interactions and non‐linearity allows a potentially fruitful approach for stagging in IPOs. Neural networks have been criticized for being a black box; however, this paper shows that, by using sensitivity analysis, neural networks can provide a reasonable explanation of their predictive behaviour and direction of association between variables. Copyright © 2005 John Wiley & Sons, Ltd. |
