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Decision Support Solution To Business Failure Prediction

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  • Madalina Ecaterina POPESCU
  • Marin ANDREICA
  • Ion-Petru POPESCU

Abstract

This paper aims to develop a practical decision support solution to business failure prediction, as early warning signals of potential financial distress could become a true asset in the decision making process of a firm. Several prediction models, such as decision trees and neural networks are built on a sample of Romanian firms and tested for their prediction ability. In order to try to improve the prediction ability of the tree model, we propose a method based on principal component analysis. The high prediction accuracy of the models suggests that the proposed decision support solution can become a practical tool for any decision maker.

Suggested Citation

  • 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.
    • Handle: RePEc:rom:mancon:v:11:y:2017:i:1:p:99-106
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    References listed on IDEAS

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    1. Mãdãlina Ecaterina POPESCU, 2015. "Proposal for a Decision Support System to Predict Financial Distress," REVISTA DE MANAGEMENT COMPARAT INTERNATIONAL/REVIEW OF INTERNATIONAL COMPARATIVE MANAGEMENT, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 16(1), pages 112-118, March.
    2. 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.
    3. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    4. Liviu Tudor & Mădălina Ecaterina Popescu & Marin Andreica, 2015. "A Decision Support System to Predict Financial Distress. The Case Of Romania," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 170-179, December.
    5. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    6. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    7. Eisenbeis, Robert A, 1977. "Pitfalls in the Application of Discriminant Analysis in Business, Finance, and Economics," Journal of Finance, American Finance Association, vol. 32(3), pages 875-900, June.
    8. Ana-Maria Zamfir & Cristina Mocanu & Adriana Grigorescu, 2017. "Circular Economy and Decision Models among European SMEs," Sustainability, MDPI, vol. 9(9), pages 1-15, August.
    9. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    10. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, January.
    11. Juliana Yim & Heather Mitchell, 2005. "A comparison of corporate distress prediction models in Brazil: hybrid neural networks, logit models and discriminant analysis," Nova Economia, Economics Department, Universidade Federal de Minas Gerais (Brazil), vol. 15(1), pages 73-93, January-A.
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    Cited by:

    1. Romero Martínez, Mariano & Carmona Ibáñez, Pedro & Pozuelo Campillo, José, 2021. "Utilidad del Deep Learning en la predicción del fracaso empresarial en el ámbito europeo || The usefulness of Deep Learning in the prediction of business failure at the European level," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 32(1), pages 392-414, December.

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