IDEAS home Printed from https://ideas.repec.org/a/ibn/ijefaa/v12y2020i6p45.html
   My bibliography  Save this article

A Simplified Variable Analysis of Credit Ratings for Small Chinese Enterprises Based on Support Vector Machine

Author

Listed:
  • Ying Chen
  • Yangkai Guo
  • Maoguo Wu

Abstract

Small enterprises are an important component of the national economy and valuable customers of commercial banks. Commercial banks use credit ratings, including financial and nonfinancial indices, to analyze small enterprises before committing to long-term collaborations, including loans. This paper uses a support vector machine algorithm to establish an imbalanced multi-classification model and compares the results to those of other methods. Commercial banks need simplified variable analysis credit ratings that use minimal information to rapidly and accurately obtain credit ratings and improve the efficiency of the process. Accordingly, we perform multiple tests of simplified rating systems using fewer variables.

Suggested Citation

  • Ying Chen & Yangkai Guo & Maoguo Wu, 2020. "A Simplified Variable Analysis of Credit Ratings for Small Chinese Enterprises Based on Support Vector Machine," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 12(6), pages 1-45, June.
    • Handle: RePEc:ibn:ijefaa:v:12:y:2020:i:6:p:45
    as

    Download full text from publisher

    File URL: http://www.ccsenet.org/journal/index.php/ijef/article/download/0/0/42714/44625
    Download Restriction: no
    File URL: http://www.ccsenet.org/journal/index.php/ijef/article/view/0/42714
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Martin, Daniel, 1977. "Early warning of bank failure : A logit regression approach," Journal of Banking & Finance, Elsevier, vol. 1(3), pages 249-276, November.
    2. Carey, Mark, 2002. "A guide to choosing absolute bank capital requirements," Journal of Banking & Finance, Elsevier, vol. 26(5), pages 929-951, May.
    3. Zhang, Junni L. & Härdle, Wolfgang K., 2010. "The Bayesian Additive Classification Tree applied to credit risk modelling," Computational Statistics & Data Analysis, Elsevier, vol. 54(5), pages 1197-1205, May.
    Full references (including those not matched with items on IDEAS)

    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. Wolfgang Karl Härdle & Dedy Dwi Prastyo, 2013. "Default Risk Calculation based on Predictor Selection for the Southeast Asian Industry," SFB 649 Discussion Papers SFB649DP2013-037, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    2. Wolfgang Karl Härdle & Dedy Dwi Prastyo & Christian Hafner, 2012. "Support Vector Machines with Evolutionary Feature Selection for Default Prediction," SFB 649 Discussion Papers SFB649DP2012-030, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    3. Suzan Hol, 2006. "The influence of the business cycle on bankruptcy probability," Discussion Papers 466, Statistics Norway, Research Department.
    4. Pesaran, M. Hashem & Schuermann, Til & Treutler, Bjorn-Jakob & Weiner, Scott M., 2006. "Macroeconomic Dynamics and Credit Risk: A Global Perspective," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(5), pages 1211-1261, August.
    5. Zhou, Fanyin & Fu, Lijun & Li, Zhiyong & Xu, Jiawei, 2022. "The recurrence of financial distress: A survival analysis," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1100-1115.
    6. Suarez, Javier & Sánchez Serrano, Antonio, 2018. "Approaching non-performing loans from a macroprudential angle," Report of the Advisory Scientific Committee 7, European Systemic Risk Board.
    7. Matthew Plosser & João A. C. Santos, 2014. "Banks' incentives and the quality of internal risk models," Staff Reports 704, Federal Reserve Bank of New York.
    8. José Eduardo Gómez González & Ines Paola Orozco Hinojosa, 2010. "Un modelo de alerta temprana para el sistema financiero colombiano," Revista ESPE - Ensayos sobre Política Económica, Banco de la Republica de Colombia, vol. 28(62), pages 124-147, June.
    9. Philip Swicegood & Jeffrey A. Clark, 2001. "Off‐site monitoring systems for predicting bank underperformance: a comparison of neural networks, discriminant analysis, and professional human judgment," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 10(3), pages 169-186, September.
    10. Le, Hong Hanh & Viviani, Jean-Laurent, 2018. "Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios," Research in International Business and Finance, Elsevier, vol. 44(C), pages 16-25.
    11. Michael Halling & Evelyn Hayden, 2008. "Bank failure prediction: a two-step survival time approach," IFC Bulletins chapters, in: Bank for International Settlements (ed.), The IFC's contribution to the 56th ISI Session, Lisbon, August 2007, volume 28, pages 48-73, Bank for International Settlements.
    12. V L Miguéis & D F Benoit & D Van den Poel, 2013. "Enhanced decision support in credit scoring using Bayesian binary quantile regression," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(9), pages 1374-1383, September.
    13. 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.
    14. Li, Hui & Sun, Jie, 2009. "Hybridizing principles of the Electre method with case-based reasoning for data mining: Electre-CBR-I and Electre-CBR-II," European Journal of Operational Research, Elsevier, vol. 197(1), pages 214-224, August.
    15. Daniel Porath, 2006. "Estimating probabilities of default for German savings banks and credit cooperatives," Schmalenbach Business Review (sbr), LMU Munich School of Management, vol. 58(3), pages 214-233, July.
    16. Pablo de Llano Monelos & Manuel Rodríguez López & Carlos Piñeiro Sánchez, 2013. "Bankruptcy Prediction Models in Galician companies. Application of Parametric Methodologies and Artificial Intelligence," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(1), pages 117-136.
    17. Fatima Alali & Silvia Romero, 2013. "Characteristics of failed U.S. commercial banks: an exploratory study," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 53(4), pages 1149-1174, December.
    18. Paola Bongini & Stijn Claessens & Giovanni Ferri, 2001. "The Political Economy of Distress in East Asian Financial Institutions," Journal of Financial Services Research, Springer;Western Finance Association, vol. 19(1), pages 5-25, February.
    19. Allen N. Berger & Björn Imbierowicz & Christian Rauch, 2016. "The Roles of Corporate Governance in Bank Failures during the Recent Financial Crisis," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(4), pages 729-770, June.
    20. Nijskens, Rob & Mokas, Dimitris, 2019. "Credit Risk in Commercial Real Estate Bank Loans : The Role of Idiosyncratic versus Macro-Economic Factors," Other publications TiSEM ea4f2f0e-dc50-4987-91d3-6, Tilburg University, School of Economics and Management.

    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

    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:ibn:ijefaa:v:12:y:2020:i:6:p:45. 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: Canadian Center of Science and Education (email available below). General contact details of provider: https://edirc.repec.org/data/cepflch.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.