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

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  • Bharat Jain
  • Barin Nag

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

Suggested Citation

  • Bharat Jain & Barin Nag, 1998. "A neural network model to predict long-run operating performance of new ventures," Annals of Operations Research, Springer, vol. 78(0), pages 83-110, January.
    • Handle: RePEc:spr:annopr:v:78:y:1998:i:0:p:83-110:10.1023/a:1018910402737
    DOI: 10.1023/A:1018910402737
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    Cited by:

    1. Shabnam Sorkhi & Joseph C. Paradi, 2020. "Measuring short-term risk of initial public offering of equity securities: a hybrid Bayesian and Data-Envelopment-Analysis-based approach," Annals of Operations Research, Springer, vol. 288(2), pages 733-753, May.
    2. Madalina Ecaterina POPESCU & Marin ANDREICA & Ion-Petru POPESCU, 2017. "Decision Support Solution To Business Failure Prediction," Proceedings of the INTERNATIONAL MANAGEMENT CONFERENCE, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 11(1), pages 99-106, November.
    3. Beat Reber & Bob Berry & Steve Toms, 2005. "Predicting mispricing of initial public offerings," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 13(1), pages 41-59, March.
    4. Alina Mihaela Dima & Simona Vasilache, 2016. "Credit Risk modeling for Companies Default Prediction using Neural Networks," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 127-143, September.
    5. Libiao Bai & Kanyin Zheng & Zhiguo Wang & Jiale Liu, 2022. "Service provider portfolio selection for project management using a BP neural network," Annals of Operations Research, Springer, vol. 308(1), pages 41-62, January.

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