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

Bharat Jain and Barin Nag

Annals of Operations Research, 1998, vol. 78, issue 0, 83-110

Abstract: The prediction of long-run operating performance of new ventures, known as Initial Public Offerings (IPOs), 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. Copyright Kluwer Academic Publishers 1998

Keywords: neural networks; initial public offerings; operating performance; logit models (search for similar items in EconPapers)
Date: 1998
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DOI: 10.1023/A:1018910402737

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