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Artificial Neural Network Models for Pricing Initial Public Offerings
Author(s) -
Jain Bharat A.,
Nag Barin N.
Publication year - 1995
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1995.tb01430.x
Subject(s) - initial public offering , generalizability theory , robustness (evolution) , artificial neural network , inefficiency , computer science , empirical evidence , business , economics , finance , microeconomics , artificial intelligence , biochemistry , statistics , chemistry , philosophy , mathematics , epistemology , gene
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.