Skip to main content
SpringerLink
Log in

Credit Risk Modeling of USA Manufacturing Companies Using Linear SVM and Sliding Window Testing Approach

  • Conference paper
Business Information Systems (BIS 2012)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 117))

Included in the following conference series:

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Altman, E.: Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance 23(4), 589–609 (1968)

    Google Scholar 

  2. Zmijewski, M.: Methodological Issues Related to the Estimation of Financial Distress Prediction Models. Journal of Accounting Research 22, 59–82 (1984)

    Article  Google Scholar 

  3. Springate, G.L.V.: Predicting the Possibility of Failure in a Canadian Firm. Unpublished M.B.A. Research Project, Simon Fraser University (1978)

    Google Scholar 

  4. Sands, E.G., Springate, G.L.V., Var, V.: Predicting Business Failures. CGA Magazine, 24–27 (May 1983)

    Google Scholar 

  5. Ohlson, J.A.: Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research 18(1), 109 (1980)

    Article  Google Scholar 

  6. 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)

    Google Scholar 

  7. 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)

    Google Scholar 

  8. 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)

    Google Scholar 

  9. Hardle, W.K., Moro, R., Schafer, D.: Rating Companies with Support Vector Machines. DIW Berlin, Diskussionspapier, 416 (2004)

    Google Scholar 

  10. 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)

    Google Scholar 

  11. 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)

    Article  Google Scholar 

  12. 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)

    Google Scholar 

  13. 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)

    Chapter  Google Scholar 

  14. 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)

    Chapter  Google Scholar 

  15. 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)

    Google Scholar 

  16. 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)

    Google Scholar 

  17. 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)

    Article  Google Scholar 

  18. 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

  19. 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)

    Google Scholar 

  20. 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)

    Article  Google Scholar 

  21. 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)

    Chapter  Google Scholar 

  22. 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)

    Google Scholar 

  23. 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)

    Chapter  Google Scholar 

  24. 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)

    Google Scholar 

  25. 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)

    Google Scholar 

  26. Zmijewski, M.: Methodological Issues Related to the Estimation of Financial Distress Prediction Models. Journal of Accounting Research 22, 59–82 (1984)

    Article  Google Scholar 

  27. 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)

    Google Scholar 

  28. Weka 3: Data Mining Software in Java, http://www.cs.waikato.ac.nz/ml/weka/

  29. Hall, M.A.: Correlation-based Feature Subset Selection for Machine Learning, Hamilton, New Zealand (1998)

    Google Scholar 

  30. UCLA-LoPucki Bankruptcy Research Database, http://lopucki.law.ucla.edu/index.htm

Download references

Author information

Authors and Affiliations

  1. Department of Informatics, Kaunas Faculty, Vilnius University, Muitines St. 8, LT- 44280, Kaunas, Lithuania

    Paulius Danenas & Gintautas Garsva

Authors
  1. Paulius Danenas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gintautas Garsva
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Poznań University of Economics, Poznań, Poland

    Witold Abramowicz

  2. Vilnius University, Kaunas, Lithuania

    Dalia Kriksciuniene  & Virgilijus Sakalauskas  & 

Rights and permissions

Reprints 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)

Publish with us

Policies and ethics

Search

Navigation