Using non financial variables for business failure prediction: the Belgian context

• Autorzy: Daubie M. , Meskens N. , Zopounidis C.
• Języki publikacji: EN

Abstrakty

EN

Business failure prediction is a topic of outmost importance for a lot of people. Therefore, many prediction models have been developed but most of them are based solely on financial ratios constructed from published accounting data, which are generally easy to obtain. In Belgium, however, corporate financial data are not always available. This situation highlights the necessity of using non-financial information to predict bankruptcy. The objective of this study is to investigate the use and potential of non-financial information for bankruptcy prediction. Our models are constructed with the UTADIS method, logistic regression and decision trees.

Słowa kluczowe

EN

bankruptcy prediction; multicriteria decision aid; non-financial data; decision tree

• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Foundations of Computing and Decision Sciences
• Rocznik: 2008
• Tom: Vol. 33, No. 2
• Strony: 181--191
• Opis fizyczny: Bibliogr. 16 poz.

Twórcy

autor

Daubie M.

autor

Meskens N.

autor

Zopounidis C.

• Catholic University of Mons, Production and Operations Management Department, 151 Chaussee de Binche, B-7000 Mons, Belgium

Bibliografia

• [1]Dimitras, A., Zanakis, S., Zopounidis, C., A survey of business failures with an emphasis on prediction methods and industrial applications, European Journal of Operational Research, 90, 1996,487-513.
• [2]Altman, E., Financial ratios, discriminant analysis and the prediction of corporate bankruptcy, The Journal of Finance, 23, 4, 1968, 589-609.
• [3]Martin, D., Early warning of bank failure: A logit regression approach, Journal of Banking and Finance, 1, 1977, 249-276.
• [4]Izan, H., Corporate distress in Australia, Journal of Banking and Finance, 8, 1984, 303-320.
• [5]Tam, K., Kiang, M., Predicting bank failures: A neural network approach, Applied Artificial Intelligence, 4, 1990, 265-282.
• [6]Frydman, H., Altman, E., Kao, D., Introducing recursive partitioning for financial classification: The case of financial distress, The Journal of Finance, 40, 1, 1985, 269-291.
• [7]Zopounidis, C., L ’Evaluation du Risque de Défaillance: Méthodes et Cas d’Application, Economica, Paris, 1995.
• [8]Zopounidis, C., Doumpos, M., Business failure prediction using the UTADIS multicriteria analysis method, Journal of the Operational Research Society, 50, 1999, 1138-1148.
• [9]Shin, K.-S., Lee, T.S., Kim, H.-J., An application of support vector machines in bankruptcy prediction model, Expert Systems with Applications, 28, 2005, 127-135.
• [10]Daubie, M., Meskens, N., Business failure prediction: A review and analysis of the literature, in: C. Zopounidis (ed.), New Trends in Banking Management, Springer, Berlin, 2002, 71-86.
• [11]Pampel, F.C., Logistic Regression. A Primer, Sage Publications, Thousand Oaks, CA, 2000.
• [12]Quinlan, J.R., Induction of decision trees, Machine Learning, 1, 1986, 81-106.
• [13]Doumpos, M., Zopounidis, C. Multicriteria Decision Aid Classification Methods, Kluwer, Dordrecht, 2002.
• [14]Jacquet-Lagréze, E., Siskos, J., Preference disaggregation: Twenty years of MCDA experience, European Journal of Operational Research, 130, 2, 2001, 233-245.
• [15]Dimitras, A., Słowiński, R., Susmanga, R., Zopounidis, C., Business failure prediction using rough sets, European Journal of Operational Research, 114, 1999, 263-280.
• [16]Zopounidis, C., Dimitras, A., Le Rudelier, L., A multicriteria approach for the analysis and prediction of business failure in Greece, in: C. Zopounidis (ed.), Operational Tool in the Management of Financial Risks, Kluwer, Dordrecht, 1998, 107-119.