Artificial Neural Network Models for Pricing Initial Public Offerings

Bharat A. Jain · Barin N. Nag

79 citations0/0 files storedDecision Sciences

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Financial Markets and Investment Strategies Forecasting Techniques and Applications Stock Market Forecasting Methods

Abstract

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.

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References

1986Investment banking, reputation, and the underpricing of initial public offerings 1990Initial Public Offerings and Underwriter Reputation 1984The "Hot Issue" Market of 1980 1975Price performance of common stock new issues 1988INITIAL PUBLIC OFFERINGS 1992Managerial Applications of Neural Networks: The Case of Bank Failure Predictions 1994A Nonparametric Approach to Pricing and Hedging Derivative Securities Via Learning Networks 1987The costs of going public

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