Artificial Neural Network Models for Pricing Initial Public Offerings
Decision SciencesPublished 1 May 1995
Bharat A. Jain, Barin N. Nag
Citations81
SJR quartileQ1
SJR score1.62
SNIP1.46
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
In this paper, neural network models are developed for a new application: the pricing of Initial Public Offerings (IPOs), where previous empirical studies provide consistent evidence of considerable inefficiency in the Pricing of new issues.
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.
Keywords
Decision SciencesEconomics, Econometrics and FinanceBusiness, Management and Accounting
The MIT Press eBooksParallel Distributed Processing
15,227 Citations1986David E. Rumelhart, James L. McClelland +1 more
Psychological ReviewThe perceptron: A probabilistic model for information storage and organization in the brain.
11,746 Citations1958Frank F. Rosenblatt
This article will be concerned primarily with the second and third questions, which are still subject to a vast amount of speculation, and where the few relevant facts currently supplied by neurophysiology have not yet been integrated into an acceptable theory.
Journal of Financial EconomicsInvestment banking, reputation, and the underpricing of initial public offerings
2,601 Citations1986Randolph P. Beatty, Jay R. Ritter
The Journal of FinanceInitial Public Offerings and Underwriter Reputation
2,557 Citations1990Richard Carter, Steven Manaster
Journal of Financial EconomicsPrice performance of common stock new issues
1,363 Citations1975Roger G. Ibbotson
Management ScienceManagerial Applications of Neural Networks: The Case of Bank Failure Predictions
1,124 Citations1992Kar Yan Tam, Melody Y. Kiang
Empirical results show that neural nets is a promising method of evaluating bank conditions in terms of predictive accuracy, adaptability, and robustness.
The Journal of FinanceA Nonparametric Approach to Pricing and Hedging Derivative Securities Via Learning Networks
715 Citations1994James M. Hutchinson, Andrew W. Lo +1 more
Decision SciencesNeural Networks: A New Tool for Predicting Thrift Failures*
534 Citations1992Linda Salchenberger, E. Mine Çinar +1 more
A neural network model that processes input data consisting of financial ratios is developed to predict the financial health of thrift institutions and its ability to discriminate between healthy and failed institutions is compared to a traditional statistical model.
Information & ManagementForecasting with neural networks
432 Citations1993Desmond Fletcher, Ernie Goss
A back-propagation neural network methodology has been applied to a sample of bankrupt and non-bankrupt firms and results indicate that this technique more accurately predicts bankruptcy than the logit model.
Stock price pattern recognition-a recurrent neural network approach
254 Citations1990Keita Kamijo, T. Tanigawa
Recurrent neural networks were applied to the recognition of stock patterns, and a method for evaluating the networks was developed that is applicable to reducing mismatching patterns.
Journal of the Operational Research SocietyA Comparison of Discriminant Analysis versus Artificial Neural Networks
227 Citations1993Youngohc Yoon, George Swales +1 more
The purpose of this paper is to compare the ANN method with the Discriminant Analysis (DA) method in order to understand the merits of ANN that are responsible for the higher level of performance.
The Journal of FinanceRelationship‐Specific Assets and the Pricing of Underwriter Services
217 Citations1992Christopher M. James
IEEE International Conference on Neural NetworksBond rating: a nonconservative application of neural networks
216 Citations1988Dutta, Shekhar
The authors apply neural networks to a generalization problem of predicting the ratings of corporate bonds, where conventional mathematical modeling techniques have yielded poor results and it is difficult to build rule-based artificial-intelligence systems.
Handbooks in operations research and management scienceChapter 30 Initial public offerings
126 Citations1995Roger G. Ibbotson, Jay R. Ritter
Evidence is presented in the chapter on three anomalies associated with IPOs: new issue underprices, cycles in the extent of underpricing, and long-run underperformance.
IEEE International Conference on Neural NetworksAn application of a multiple neural network learning system to emulation of mortgage underwriting judgements
106 Citations1988Collins, Ghosh +1 more
It was found that when the MNNLS system and the underwriters disagree, the system's classifications are more consistent with the guidelines than the underwriter's judgement.
The Quarterly Review of Economics and FinanceThe underpricing of ‘unit’ initial public offerings
54 Citations1994Bharat A. Jain