A neural network model to predict long-run operating performance of new ventures

A neural network model to predict long-run operating performance of new ventures

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Article ID: iaor19982066
Country: Netherlands
Volume: 78
Issue: 1
Start Page Number: 83
End Page Number: 110
Publication Date: Mar 1998
Journal: Annals of Operations Research
Authors: ,
Keywords: finance & banking, artificial intelligence: expert systems, neural networks
Abstract:

The production of long-run operating performance of new ventures, known as Initial Public Offerings, represents a challenging decision problem. Factors adding to the complexity of the problem include asymmetrically informed agents, incentive problems, and inability to specify functional relationships between variables. Research literature identifying determinants of long-run performance of new issues is limited. This study uses a data driven, nonparametric, neural network based approach to predict the long-run operating performance of new ventures. The classification accuracy of the neural network model is compared with that of a logit model. Methodological issues such as sample design and estimation of optimal cutoff probabilities for classification are addressed. The results suggest that the neural networks generally outperform logit models.

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