Abstract
Business risk identification is one of the most important components in business risk management. In this study, a knowledge ensemble methodology is proposed to design an intelligent business risk identification system, which is composed of two procedures. First of all, some data mining and knowledge discovery algorithms are used to explore the implied knowledge about business risk hidden in the business data. Then the implied knowledge generated from different mining algorithms is aggregated into an ensemble output using an evolutionary programming (EP) technique. For verification, the knowledge ensemble methodology is applied to a real-world business risk dataset. The experimental results reveal that the proposed intelligent knowledge ensemble methodology provides a promising solution to business risk identification.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Ahn, B.S., Cho, S.S., Kim, C.Y.: The integrated methodology of rough set theory and artificial neural network for business failure prediction. Expert Systems with Application 18, 65–74 (2000)
Altman, E.I.: Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance 23, 589–609 (1968)
Altman, E.I.: The success of business failure prediction models: An international survey. Journal of Banking and Finance 8, 171–198 (1984)
Altman, E.I.: Corporate Financial Distress and Bankruptcy. John Wiley, New York (1993)
Altman, E.I., Marco, G., Varetto, F.: Corporate distress diagnosis: Comparison using discriminant analysis and neural networks (the Italian experience). Journal of Banking and Finance 18, 505–529 (1994)
Beaver, W.H.: Financial ratios as predictors of failure. Journal of Accounting Research 4, 71–111 (1966)
Beynon, M.J., Peel, M.J.: Variable precision rough set theory and data discretisation: An application to corporate failure prediction. Omega 29, 561–576 (2001)
Cooper, D.R., Emory, C.W.: Business Research Methods. Irwin, Chicago (1995)
Courtis, J.K.: Modeling a financial ratios categoric framework. Journal of Business Finance and Accounting 5, 371–386 (1978)
Dimitras, A.I., Slowinski, R., Susmaga, R., Zopounidis, C.: Business failure prediction using rough sets. European Journal of Operational Research 114, 263–280 (1999)
Dimitras, A.I., Zanakis, S.H., Zopounidis, C.: A survey of business failures with an emphasis on prediction methods and industrial applications. European Journal of Operational Research 90, 487–513 (1996)
Dimitras, A.I., Zopounidis, C., Hurson, C.H.: A multicriteria decision aid method for the assessment of business failure risk. Foundations of Computing and Decision Sciences 20, 99–112 (1995)
Eisenbeis, R.A.: Pitfalls in the application of discriminant analysis in business and economics. The Journal of Finance 32, 875–900 (1977)
Fogel, D.B.: System Identification through Simulated Evolution: A Machine Learning Approach to Modeling. Ginn Press, Needham, MA (1991)
Frydman, H., Altman, E.I., Kao, D.L.: Introducing recursive partitioning for financial classification: The case of financial distress. The Journal of Finance 40, 269–291 (1985)
Gupta, Y.P., Rao, R.P., Bagghi, P.K.: Linear goal programming as an alternative to multivariate discriminant analysis: A note. Journal of Business Finance and Accounting 17, 593–598 (1990)
Hair, J.F., Anderson, R.E., Tatham, R.E., Black, W.C.: Multivariate Data Analysis with Readings. Prentice Hall, Englewood Cliffs, NJ (1998)
Hornik, K., Stinchocombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Networks 2, 359–366 (1989)
Jones, F.L.: Current techniques in bankruptcy prediction. Journal of Accounting Literature 6, 131–164 (1987)
Keasey, K., Watson, R.: Financial distress prediction models: a review of their usefulness. British Journal of Management 2, 89–102 (1991)
Kim, K.J.: Financial time series forecasting using support vector machines. Neurocomputing 55, 307–319 (2003)
Lai, K.K., Yu, L., Huang, W., Wang, S.Y.: A novel support vector machine metamodel for business risk identification. In: Yang, Q., Webb, G. (eds.) PRICAI 2006. LNCS (LNAI), vol. 4099, pp. 980–984. Springer, Heidelberg (2006a)
Lai, K.K., Yu, L., Wang, S.Y., Zhou, L.G.: Neural network metalearning for credit scoring. In: Huang, D.-S., Li, K., Irwin, G.W. (eds.) ICIC 2006. LNCS, vol. 4113, pp. 403–408. Springer, Heidelberg (2006b)
Lai, K.K., Yu, L., Wang, S.Y., Zhou, L.G.: Credit risk analysis using a reliability-based neural network ensemble model. In: Kollias, S., Stafylopatis, A., Duch, W., Oja, E. (eds.) ICANN 2006. LNCS, vol. 4132, pp. 682–690. Springer, Heidelberg (2006c)
Lai, K.K., Yu, L., Zhou, L.G., Wang, S.Y.: Credit risk evaluation with least square support vector machine. In: Wang, G.-Y., Peters, J.F., Skowron, A., Yao, Y. (eds.) RSKT 2006. LNCS (LNAI), vol. 4062, pp. 490–495. Springer, Heidelberg (2006d)
Lee, K., Booth, D., Alam, P.: A comparison of supervised and unsupervised neural networks in predicting bankruptcy of Korean firms. Expert Systems with Applications 29, 1–16 (2005)
Luoma, M., Laitinen, E.K.: Survival analysis as a tool for company failure prediction. Omega 19, 673–678 (1991)
Messier, W.F., Hansen, J.V.: Including rules for expert system development: An example using default and bankruptcy data. Management Science 34, 1403–1415 (1988)
Ohlson, J.A.: Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research 3, 109–131 (1980)
Olmeda, I., Fernandez, E.: Hybrid classifiers for financial multicriteria decision making: The case of bankruptcy prediction. Computational Economics 10, 317–335 (1997)
Piramuthu, S.: Financial credit-risk evaluation with neural and neurofuzzy systems. European Journal of Operational Research 112, 310–321 (1999)
Scott, J.: The probability of bankruptcy: A comparison of empirical predictions and theoretical models. Journal of Banking and Finance 5, 317–344 (1981)
Shin, K.S., Lee, T.S., Kim, H.J.: An application of support vector machines in bankruptcy prediction model. Expert Systems with Applications 28, 127–135 (2005)
Siskos, Y., Zopounidis, C., Pouliezos, A.: An integrated DSS for financing firms by an industrial development bank in Greece. Decision Support Systems 12, 151–168 (1994)
Tay, F.E.H., Cao, L.J.: Application of support vector machines in financial time series forecasting. Omega 29, 309–317 (2001)
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1998)
Varetto, F.: Genetic algorithms applications in the analysis of insolvency risk. Journal of Banking and Finance 2, 1421–1439 (1998)
Vermeulen, E.M., Spronk, J., van der Wijst, N.: The application of the multi-factor model in the analysis of corporate failure. In: Zopounidis, C. (ed.) Operational Tools in the Management of Financial Risks, pp. 59–73. Kluwer Academic Publisher, Dordrecht (1998)
White, H.: Connectionist nonparametric regression: Multilayer feedforward networks can learn arbitrary mappings. Neural Networks 3, 535–549 (1990)
Yang, S., Browne, A.: Neural network ensembles: Combining multiple models for enhanced performance using a multistage approach. Expert Systems 21, 279–288 (2004)
Yu, L., Wang, S.Y., Lai, K.K.: A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates. Computers and Operations Research 32, 2523–2541 (2005)
Zavgren, C.V.: The prediction of corporate failure: The state of the art. Journal of Financial Literature 2, 1–37 (1983)
Zavgren, C.V.: Assessing the vulnerability to failure of American industrial firms: A logistic analysis. Journal of Business Finance and Accounting 12, 19–45 (1985)
Zmijewski, M.E.: Methodological issues related to the estimation of financial distress prediction models. Studies on Current Econometric Issues in Accounting Research, 59–82 (1984)
Zopounidis, C.: A multicriteria decision making methodology for the evaluation of the risk of failure and an application. Foundations of Control Engineering 12, 45–67 (1987)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Yu, L., Lai, K.K., Wang, S. (2008). An Evolutionary Programming Based Knowledge Ensemble Model for Business Risk Identification. In: Prasad, B. (eds) Soft Computing Applications in Business. Studies in Fuzziness and Soft Computing, vol 230. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79005-1_4
Download citation
DOI: https://doi.org/10.1007/978-3-540-79005-1_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-79004-4
Online ISBN: 978-3-540-79005-1
eBook Packages: EngineeringEngineering (R0)