Open Access Open Access  Restricted Access Subscription or Fee Access

To Find Best Bankruptcy Model using Genetic Algorithm

A. Martin, J. Madhusudhnan, T. Miranda Lakshmi, V. Prasanna Venkatesan

Abstract


In the globalized stiff business environment for the survival of any organization an effective technology is required to take right decisions at right time by right people. One such a prime technology is business intelligence. Bankruptcy prediction is one of the business intelligence techniques. Among so many challenges bankruptcy is very important for a financial institution or any business. Prediction of bankruptcy is crucial for the smooth running of business. Many bankruptcy models are available. Each bankruptcy model is described by quantity equation, which is based on the non linear relationship between various financial ratios used in that model. The Genetic process is applied to find the non linear relationship between financial ratios which are having more impact on bankruptcy model. In this research three bankruptcy models Altman, Edmister and Deakin model were chosen. Genetic algorithm is applied in these three bankruptcy models to find most impacted ratios. Altman model is has more impact on its financial ratios compare to other bankruptcy models. The impacted threshold value is 98% matches with the original threshold value of Altman.


Keywords


Genetic Algorithm, Bankruptcy Models, Deakin Model, Altman Model, Edmister Model, Financial Ratios, Business Intelligence.

Full Text:

PDF

References


A.Martin, T Miranda Lakshmi ,Dr.V. Prasanna Venkatesan, ―To find most impact financial features on bankruptcy models using Genetic Algorithm‖, Proceedings of International conference on Advances in Engineering and Technology [ICAET-2011], E.G.S. Pillay Engineering College, Nagapattinma, India. 27-28, May, 2011.

Altman EI. Financial ratios discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance 1968;23:589–609.

Ohlson JA. Financial ratios and probabilistic prediction of bankruptcy. Journal of Accounting Research 1980;18:109–31.

Zavgren CV. Assessing the vulnerability to failure of American industrial firm: a logistic analysis. Journal of Business Finance & Accounting 1985;12(1):19–45.

Frydman H, Altman EI, Kao D. Introducing recursive partitioning for financial classification: the case of financial distress. Journal of Finance 1985;40(1):269–91.

Sun J, Li H. Listed companies’ financial distress prediction based on weighted majority voting combination of multiple classifiers. Expert Systems with Applications 2007.

McKee TE, Greenstein M. Predicting bankruptcy using recursive partitioning and a realistically proportioned data set. Journal of Forecasting 2000;19:219–30.

Jo H, Han I. Integration of case-based forecasting, neural network, and discriminant analysis for bankruptcy prediction. Expert Systems with Applications 1996;11(4):415–22.

Jo H, Han I, Lee H. Bankruptcy prediction using case-based reasoning, neural networks, and discriminant analysis. Expert Systems with Applications 1997;13(2):97–108.

Sun J, Hui X-F. Financial distress prediction based on similarity weighted voting CBR. Lecture notes in artificial intelligence, vol. 4093, Berlin:Springer; 2006. p. 947–58.

Odom M, Sharda R. A neural networks model for bankruptcy prediction. In: Proceedings of the IEEE international conference on neural network, vol. 2, 1990. p. 163–8.

Shin K-S, Lee Y-J. A genetic algorithm application in bankruptcy prediction modeling. Expert Systems with Applications 2002;23:321–8.

Shin K-S, Lee TS, Kim H-J. An application of support vector machines in bankruptcy prediction model. Expert Systems with Applications 2005;28(1):127–35.

Min JH, Lee Y-C. Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Systems with Applications 2005;28(4):128–34.

Hui X-F, Sun J. An application of support vector machine to companies’ financial distress prediction. Lecture notes in artificial intelligence,vol. 3885, Berlin: Springer; 2006. p. 274–282.

Back B, Laitinen T, Sere K. Neural networks and genetic algorithms for bankruptcy predictions. Expert Systems with Applications 1996;11(4):407–13.

Anandarajan M, Lee P, Anandarajan A. Bankruptcy prediction of financially stressed firms: an examination of the predictive accuracy of artificial neural networks. International Journal of Intelligent Systems in Accounting, Finance & Management 2001;10:69–81.

Kumar PR, RaviV. Bankruptcy prediction in banks and firms via statistical and intelligent techniques—a review. European Journal of Operational Research 2007;180(4):1–28.

Chih-Fong Tsai, ―Feature selection in bankruptcy prediction‖, International Journal of Knowledge-Based Systems-Elsevier, 2009,p.120-127

Shih-Wei Lin, Yeou-Ren Shiue , Shih-Chi Chen, Hui-Miao Cheng, ―Applying enhanced data mining approaches in predicting bank performance: A case of Taiwanese commercial banks‖, Elsevier Journal of Expert Systems with Applications 36 (2009) 11543–115

Huimin Zha, Atish P. Sinha,Wei Ge,‖ Effects of feature construction on classification performance: An empirical study in bank failure prediction‖, Elsevier Journal of Expert Systems with Applications 36 (2009) 2633–2644.

Jae H. Min, Chulwoo Jeong, ―A binary classification method for bankruptcy prediction,Elsevier Journal of Expert Systems with Applications 36 (2009) 5256–5263

Hyunchul Ahn, Kyoung-jae Kim, ―Bankruptcy prediction modeling with hybrid case-based reasoning and genetic algorithms approach‖, Elsevier Journal of Applied Soft Computing 9 (2009) 599–607.

Xiaoyan Xu, Yu Wang, ―Financial failure prediction using efficiency as a predictor‖, Elsevier Journal of Expert Systems with Applications 36 (2009) 366–373.

Haupt, R. L., & Haupt, S. E, ―Practical genetic algorithms‖, Wiley Interscience Publication, 1998.

Chih-Hung Wu, Gwo-Hshiung Tzeng , Yeong-Jia Goo , Wen-Chang Fang,‖ A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy‖, Elsevier Journal of Expert Systems with Applications 32 (2007) 397–408.

Jhen-Jia Hu, Tzuu-Hseng S. Li,‖ Genetic regulatory network-based symbiotic evolution‖,Elsevier Journal of Expert Systems with Applications (2010)

Myoung-Jong Kim, Ingoo Han, ―The discovery of experts’ decision rules from qualitative bankruptcy data using genetic algorithms‖, Elsevier Journal of Expert Systems with Applications 25 (2003) 637–646

A.Martin, M.Manjula and Dr.V.Prasanna Venkatesan,‖ A Business Intelligence Model to Predict Bankruptcy using Financial Domain Ontology with Association Rule Mining Algorithm‖, IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 3, No. 2, May 2011

A. Martin, D.Maladhy & Dr.V.Prasanna Venkatesan,‖ A framework for business Intelligence application Using ontological Classification‖, International Journal of Engineering Science and Technology, Vol. 3 No. 2 Feb 2011

A.Martin,Dr.V.Prasanna Venkatesan,‖ A hybrid model for bankruptcy Prediction using genetic Algorithm, fuzzy c-means and mars‖, International Journal on Soft Computing Vol.2, No.1, February 2011

Xingsen Li,Hongliang Qu,Zhengxiang Zhu,Yongsheng Han ―A System Information Collection Method For Business Intelligence ‖,Proceedings of International Conference on Electronic Commerce and Business Intelligence,2009.

Milena Tvrdikova ―Support of Decision Making by Business Intelligence Tools‖, Proceedings of the 6th International Conference on Computer Information system and Industrial management applications (CISIM’07)

C. Cardie, Using decision trees to improve case-based learning, in: Proceedings of the Tenth International Conference on Machine Learning, San Francisco, CA, (1993), pp. 25–32.

D.B. Skalak, Prototype and feature selection by sampling and random mutation hill climbing algorithms, in: Proceedings of the Eleventh International Conferenceon Machine Learning, New Brunswick, NJ, (1994), pp. 293–301.

P. Domingos, Context-sensitive feature selection for lazy learners, Artificial Intelligence Review 11 (1–5) (1997) 227–253.

C. Cardie, N. Howe, Improving minority class prediction using case-specific feature weights, in: Proceedings of the Fourteenth International Conference on Machine Learning, San Francisco, CA, (1997), pp. 57–65.

J.D.J. Kelly, L. Davis, ―Hybridizing the genetic algorithm and the k nearest neighbours classification algorithm‖ Proceedings of the 0Fourth International Conference on Genetic Algorithms, San Diego, CA, (1991), pp. 377–383.

Kyung-Shik Shin, Yong-Joo Lee, ―A genetic algorithm application in bankruptcy prediction modelling‖, Elsevier Journal of Expert Systems with Applications (2002)

D. Wettschereck, D.W. Aha, T. Mohri, A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms, Artificial Intelligence Review 11 (1–5) (1997) 273–314.

Deakin, E. B. (1972), ―A Discriminant Analysis of Predictors of Failure‖, Journal of Accounting Research, vol. 9, pp.167-179.

Hill, N.T., Perry, S., Andes, S., 1996. Evaluating firms in financial distress: An event history analysis. Journal of Applied Business Research 12 (3), 60–70.

McKee, T.E., 1999a. A mathematically derived rough set model for bankruptcy prediction. In: Bonson, Vasarhelyi (Eds.), Emerging Technologies in Accounting and Finance, University of Heulva, Huelva, Spain, pp. 81–99.

T.E. McKee, T. Lensberg, "Genetic programming and rough sets: A hybrid approach to bankruptcy classification", European Journal of Operational Research 138 (2002) 436–451.

Amir F. Atiya, ―Bankruptcy prediction for Credit Risk Using Neural Networks: A Survey and New Results‖, IEEE Transactions on Neural Networks, Vol. 12,No. 4, July 2001.

P. Ravisankar,V. Ravi,I. Bose,"Failure prediction of dotcom companies using neural network–genetic programming hybrids",Elsevier Journal of Information Sciences 180 (2010) 1257–1267

Hui Li,Jie Sun,"Empirical research of hybridizing principal component analysis with multivariate discriminant analysis and logistic regression for business failure prediction",Elsevier Journal of Expert Systems with Applications,2010.

Huimin Zhao, Atish P. Sinha , Wei Ge,"Effects of feature construction on classification performance: An empirical study in bank failure prediction",Elsevier journal of Expert Systems with Applications 36 (2009) 2633–2644

Messier, W., & Hansen, J. (1988). Inducing rules for expert system development: An example using default and bankruptcy data. Management Science, 34(12), 1403–1415.

Edmister, R.O. (1972). An empirical test of financial ratio analysis for small business failure prediction. Journal of Finance and Quantitative Analysis, 7(2): 1477-1493.


Refbacks

  • There are currently no refbacks.