Abstract
This paper presents a study on credit risk evaluation modeling using linear Support Vector Machines (SVM) classifiers, combined with feature selection and “sliding window” testing approach. Discriminant analysis based evaluator was applied for dynamic evaluation and formation of bankruptcy classes. The research demonstrates a possibility to develop and apply an intelligent classifier based on original discriminant analysis method evaluation and shows that it might perform bankruptcy identification even better than original model.
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
Altman, E.: Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance 23(4), 589–609 (1968)
Zmijewski, M.: Methodological Issues Related to the Estimation of Financial Distress Prediction Models. Journal of Accounting Research 22, 59–82 (1984)
Springate, G.L.V.: Predicting the Possibility of Failure in a Canadian Firm. Unpublished M.B.A. Research Project, Simon Fraser University (1978)
Sands, E.G., Springate, G.L.V., Var, V.: Predicting Business Failures. CGA Magazine, 24–27 (May 1983)
Ohlson, J.A.: Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research 18(1), 109 (1980)
Yang, Z.R.: Support Vector Machines for Company Failure Prediction. In: Proceedings of 2003 IEEE International Conference on Computational Intelligence for Financial Engineering, pp. 47–54 (2003)
Wang, X.: Corporate Financial Warning Model Based on PSO and SVM. In: 2nd International Conference on Information Engineering and Computer Science (ICIECS), pp. 1–5 (2010)
Bose, I., Pal, R.: Using Support Vector Machines to Evaluate Financial Fate of Dotcoms. In: Proceedings on Pacific Asia Conference on Information Systems (PACIS), Paper 42 (2005)
Hardle, W.K., Moro, R., Schafer, D.: Rating Companies with Support Vector Machines. DIW Berlin, Diskussionspapier, 416 (2004)
Hardle, W., Moro, R., Schafer, D.: Estimating Probabilities of Default With Support Vector Machines. Discussion Paper Series 2: Banking and Financial Studies. Deutsche Bundesbank, Research Centre (2008)
Chen, W.-H., Shih, J.-Y.: A study of Taiwan’s issuer credit rating systems using support vector machines. Expert Systems with Applications 30, 427–435 (2006)
Chong, W., Yingjian, G., Dong, W.: Study on Capital Risk Assessment Model of Real Estate Enterprises Based on Support Vector Machines and Fuzzy Integral. In: Control and Decision Conference, pp. 2317–2320 (2008)
Lai, K.K., Yu, L., Wang, S.-Y., Huang, W.: An Intelligent CRM System for Identifying High-Risk Customers: An Ensemble Data Mining Approach. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2007. LNCS, vol. 4488, pp. 486–489. Springer, Heidelberg (2007)
Lai, K.K., Zhou, L., Yu, L.: A Two-Phase Model Based on SVM and Conjoint Analysis for Credit Scoring. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2007, Part II. LNCS, vol. 4488, pp. 494–498. Springer, Heidelberg (2007)
Zhou, L., Lai, K.K.: Weighted LS-SVM Credit Scoring Models with AUC Maximization by Direct Search. In: Proceedings of 2009 International Joint Conference on Computational Sciences and Optimization, pp. 7–11 (2009)
Zhou, L., Lai, K.K.: Multi-Agent Ensemble Models Based on Weighted Least Square SVM for Credit Risk Assessment. In: Proceedings of 2009 WRI Global Congress on Intelligent Systems, pp. 559–563 (2009)
Zhou, L., Lai, K.K., Yu, L.: Least squares support vector machines ensemble models for credit scoring. Expert Systems with Applications 37, 127–133 (2010)
Van Gestel, T., Baesens, B., Garcia, I.J.: A support vector machine approach to credit scoring (2003), http://www.defaultrisk.com/pp_score_25.htm
Van Gestel, T., Baesens, B., Suykens, J.A.K., Espinoza, M., Baestaens, D.E., Vanthienen, J., De Moor, B.: Bankruptcy prediction with least squares support vector machine classifiers. In: Proceedings of the International Conference on Computational Intelligence for Financial Engineering, CIFER, pp. 1–8 (2003)
Van Gestel, T., Baesens, B., Suykens, J.A.K., Van Den Poel, D., Baestaens, D.E., Willekens, M.: Bayesian kernel based classification for financial distress detection. European Journal of Operational Research 172, 979–1003 (2006)
Merkevičius, E., Garšva, G., Simutis, R.: Neuro-discriminate Model for the Forecasting of Changes of Companies Financial Standings on the Basis of Self-organizing Maps. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2007. LNCS, vol. 4488, pp. 439–446. Springer, Heidelberg (2007)
Danenas, P., Garsva, G.: Support Vector Machines and their Application In Credit Risk Evaluation Process. Transformations in Business & Economics 8(3(18)), 46–58 (2009)
Danenas, P., Garsva, G.: Credit Risk Evaluation Using SVM-Based Classifier. In: Abramowicz, W., Tolksdorf, R., Węcel, K. (eds.) BIS 2010, Part 1. LNBIP, vol. 57, pp. 7–12. Springer, Heidelberg (2010)
Danenas, P., Garsva, G., Gudas, S.: Credit Risk Evaluation Model Development Using Support Vector Based Classifiers. In: Proceedings of the International Conference on Computational Science (ICCS 2011), Procedia Computer Science, vol. 4, pp. 1699–1707 (2011)
Fan, R., Chang, K., Hsieh, C., Wang, X., Lin, C.: LIBLINEAR: A library for large linear classification. The Journal of Machine Learning Research 9, 1871–1874 (2008)
Zmijewski, M.: Methodological Issues Related to the Estimation of Financial Distress Prediction Models. Journal of Accounting Research 22, 59–82 (1984)
Danenas, P., Garsva, G., Simutis, R.: Development of Discriminant Analysis and Majority-Voting Based Credit Risk Assessment Classifier. In: Proceedings of the 2011 International Conference on Artificial Intelligence, ICAI 2011, Las Vegas, Nevada, JAV, July 23-27, vol. 1, pp. 204–209 (2011)
Weka 3: Data Mining Software in Java, http://www.cs.waikato.ac.nz/ml/weka/
Hall, M.A.: Correlation-based Feature Subset Selection for Machine Learning, Hamilton, New Zealand (1998)
UCLA-LoPucki Bankruptcy Research Database, http://lopucki.law.ucla.edu/index.htm
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Danenas, P., Garsva, G. (2012). Credit Risk Modeling of USA Manufacturing Companies Using Linear SVM and Sliding Window Testing Approach. In: Abramowicz, W., Kriksciuniene, D., Sakalauskas, V. (eds) Business Information Systems. BIS 2012. Lecture Notes in Business Information Processing, vol 117. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30359-3_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-30359-3_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30358-6
Online ISBN: 978-3-642-30359-3
eBook Packages: Computer ScienceComputer Science (R0)