Bharat A. Jain is Assistant Professor of Management at Towson State University. He received his Ph.D. in management science and MBA from The Pennsylvania State University. His research interests are in the applications of AI techniques, financial decision support systems, multicriteria decision making and new venture performance. He has published several articles in a number of journals including Journal of Finance, Managerial and Decision Economics, Quarterly Review of Economics and Finance, and the Journal of Small Business Management.
Artificial Neural Network Models for Pricing Initial Public Offerings
Article first published online: 7 JUN 2007
Volume 26, Issue 3, pages 283–302, May 1995
How to Cite
Jain, B. A. and Nag, B. N. (1995), Artificial Neural Network Models for Pricing Initial Public Offerings. Decision Sciences, 26: 283–302. doi: 10.1111/j.1540-5915.1995.tb01430.x
Barin N. Nag is an Associate Professor in Management at Towson State University. He received his BSEE and MSEE degrees from the Institute of Radiophysics and Electronics in India, and a Ph.D. in business from the University of Maryland at College Park. His research interests are in artificial intelligent methods, scheduling and optimization, model management and neural networks. He has published in a number of journals including European Journal of Operational Research, Annals of Operations Research, Journal of Information Systems and Technology, and Expert Systems with Applications.
- Issue published online: 7 JUN 2007
- Article first published online: 7 JUN 2007
- Received: October 5, 1994. Accepted: June 1, 1995.
- Initial Public Offerings;
- Neural Networks;
- and Statistical Models
In recent times, managerial applications of neural networks, especially in the area of financial services, has received considerable attention. In this paper, neural network models are developed for a new application: the pricing of Initial Public Offerings (IPOs). Previous empirical studies provide consistent evidence of considerable inefficiency in the pricing of new issues. Neural network models using publicly available financial data as inputs are developed to price IPOs. The pricing performance and the economic benefits of the neural network models are evaluated. Significant economic gains are documented with neural networks. Several tests to establish generalizability and robustness of the results are conducted.