IDEAS home Printed from https://ideas.repec.org/p/zbw/sfb649/sfb649dp2005-009.html

Predicting bankruptcy with support vector machines

Author

Listed:
  • Härdle, Wolfgang Karl
  • Moro, Rouslan A.
  • Schäfer, Dorothea

Abstract

No abstract is available for this item.

Suggested Citation

  • Härdle, Wolfgang Karl & Moro, Rouslan A. & Schäfer, Dorothea, 2005. "Predicting bankruptcy with support vector machines," SFB 649 Discussion Papers 2005-009, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    • Handle: RePEc:zbw:sfb649:sfb649dp2005-009
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/25028/1/495997935.PDF
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Wiginton, John C., 1980. "A Note on the Comparison of Logit and Discriminant Models of Consumer Credit Behavior," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 15(3), pages 757-770, September.
    2. Wilcox, Jw, 1971. "Simple Theory Of Financial Ratios As Predictors Of Failure," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 9(2), pages 389-345.
    3. Zmijewski, Me, 1984. "Methodological Issues Related To The Estimation Of Financial Distress Prediction Models," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 22, pages 59-82.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yu, Lean & Yao, Xiao & Zhang, Xiaoming & Yin, Hang & Liu, Jia, 2020. "A novel dual-weighted fuzzy proximal support vector machine with application to credit risk analysis," International Review of Financial Analysis, Elsevier, vol. 71(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Moro, Russ & Härdle, Wolfgang Karl & Aliakbari, Saeideh & Hoffmann, Linda, 2011. "Forecasting corporate distress in the Asian and Pacific region," SFB 649 Discussion Papers 2011-023, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    2. Petr Jakubík & Petr Teplý, 2011. "The JT Index as an Indicator of Financial Stability of Corporate Sector," Prague Economic Papers, Prague University of Economics and Business, vol. 2011(2), pages 157-176.
    3. repec:hum:wpaper:sfb649dp2005-009 is not listed on IDEAS
    4. Goriunov Dmytro & Venzhyk Katerina, 2013. "Loan Default Prediction in Ukrainian Retail Banking," EERC Working Paper Series 13/07e, EERC Research Network, Russia and CIS.
    5. Zeineb Affes & Rania Hentati-Kaffel, 2016. "Forecast bankruptcy using a blend of clustering and MARS model - Case of US banks," Post-Print halshs-01314553, HAL.
    6. Balcaen, Sofie & Ooghe, Hubert, 2006. "35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems," The British Accounting Review, Elsevier, vol. 38(1), pages 63-93.
    7. repec:diw:diwwpp:dp416 is not listed on IDEAS
    8. Alnoor Bhimani & Mohamed Azzim Gulamhussen & Samuel Rocha Lopes, 2014. "Owner liability and financial reporting information as predictors of firm default in bank loans," Review of Accounting Studies, Springer, vol. 19(2), pages 769-804, June.
    9. Sumaira Ashraf & Elisabete G. S. Félix & Zélia Serrasqueiro, 2019. "Do Traditional Financial Distress Prediction Models Predict the Early Warning Signs of Financial Distress?," JRFM, MDPI, vol. 12(2), pages 1-17, April.
    10. Rubén Mosqueda, 2010. "Fallibility Of The Rough Set Method In The Formulation Of The Failure Prediction Index Model Of Dynamic Risk," Journal of Economics, Finance and Administrative Science, Universidad ESAN, vol. 15(28), pages 65-88.
    11. Maurice Peat, 2001. "Bankruptcy Probability: A Theoretical and Empirical Examination," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 1-2001, January-A.
    12. Paweł Zając & Piotr Gurgul, 2012. "Forecasting of migration matrices in business demography," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 13(2), pages 387-404, June.
    13. Dimitras, A. I. & Zanakis, S. H. & Zopounidis, C., 1996. "A survey of business failures with an emphasis on prediction methods and industrial applications," European Journal of Operational Research, Elsevier, vol. 90(3), pages 487-513, May.
    14. Ying Zhou & Xia Lin & Guotai Chi & Peng Jin & Mengtong Li, 2024. "EWT‐SMOTE to improve default prediction performance in imbalanced data: Analysis of Chinese data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(3), pages 615-643, April.
    15. Christine V. Zavgren & Michael T. Dugan & James M. Reeve, 1988. "The Association Between Probabilities of Bankruptcy and Market Responses—A Test of Market Anticipation," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 15(1), pages 27-45, March.
    16. Kurt M. Fanning & Kenneth O. Cogger, 1994. "A Comparative Analysis of Artificial Neural Networks Using Financial Distress Prediction," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 3(4), pages 241-252, December.
    17. Cakir, Murat, 2005. "Firma Başarısızlığının Dinamiklerinin Belirlenmesinde Makina Öğrenmesi Teknikleri: Ampirik Uygulamalar ve Karşılaştırmalı Analiz [Machine Learning Techniques in Determining the Dynamics of Corporat," MPRA Paper 55975, University Library of Munich, Germany.
    18. Maurice Peat, 2001. "Bankruptcy Probability: A Theoretical and Empirical Examination," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 20, July-Dece.
    19. Şaban Çelik, 2013. "Micro Credit Risk Metrics: A Comprehensive Review," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 20(4), pages 233-272, October.
    20. Zeineb Affes & Rania Hentati-Kaffel, 2016. "Forecast bankruptcy using a blend of clustering and MARS model - Case of US banks," Documents de travail du Centre d'Economie de la Sorbonne 16026, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    21. Marco Muscettola, 2019. "Distinctiveness of Highly Risky Italian Firms That are Saved-A Logistic Approach," Applied Economics and Finance, Redfame publishing, vol. 6(1), pages 64-73, January.
    22. Antonio David Somoza Lopez & Josep Vallverdu Calafell, 2003. "Una comparacion de la seleccion de los ratios contables en los modelos contable-financieros de prediccion de la insolvencia empresarial," Working Papers in Economics 94, Universitat de Barcelona. Espai de Recerca en Economia.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:sfb649:sfb649dp2005-009. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/sohubde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.