IDEAS home Printed from https://ideas.repec.org/a/ibn/ijefaa/v10y2018i7p56.html
   My bibliography  Save this article

Measuring and Managing Credit Risk for Chinese Microfinance Institutions

Author

Listed:
  • Jie Li
  • Zhenyu Sheng

Abstract

Chinese microfinance institutions need to measure and manage credit risk in a quantitative way in order to improve competitiveness. To establish a credit scoring model (CSM) with sound predictive power, they should examine various models carefully, identify variables, assign values to variables and reduce variable dimensions in an appropriate way. Microfinance institutions could employ both CSM and loan officer¡¯s subjective appraisals to improve risk management level gradually. The paper sets up a CSM based on the data of a microfinance company running from October 2009 to June 2014 in Jiangsu province. As for establishing the model, the paper uses Linear Discriminant Analysis (LDA) method, selects 16 initial variables, employs direct method to assign variables and adopts all the variables into the model. Ten samples are constructed by randomly selecting records. Based on the samples, the coefficients are determined and the final none-standardized discriminant function is established. It is found that Bank credit, Education, Old client and Rate variables have the greatest impact on the discriminant effect. Compared with the same international models, this model¡¯s classification effect is fine. The paper displays the key technical points to build a credit scoring model based on a practical application, which provides help and references for Chinese microfinance institutions to measure and manage credit risk quantitatively.

Suggested Citation

  • Jie Li & Zhenyu Sheng, 2018. "Measuring and Managing Credit Risk for Chinese Microfinance Institutions," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 10(7), pages 1-56, July.
  • Handle: RePEc:ibn:ijefaa:v:10:y:2018:i:7:p:56
    as

    Download full text from publisher

    File URL: http://www.ccsenet.org/journal/index.php/ijef/article/download/74846/41989
    Download Restriction: no

    File URL: http://www.ccsenet.org/journal/index.php/ijef/article/view/74846
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. M. Rusydi & Sardar M. N. Islam, 2007. "Market Models and Applications," Palgrave Macmillan Books, in: Quantitative Exchange Rate Economics in Developing Countries, chapter 4, pages 45-62, Palgrave Macmillan.
    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. Mazni Asrida Abdullah & Azlina Ahmad & Nor Azam Mat Nayan & Zubir Azhar & Abd-Razak Ahmad, 2020. "Credit Risk Assessment Models of Retail Microfinancing: The Case of a Malaysian National Savings Bank¡¯s Branch," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 11(3), pages 73-83, June.
    2. Medina-Olivares, Victor & Calabrese, Raffaella & Dong, Yizhe & Shi, Baofeng, 2022. "Spatial dependence in microfinance credit default," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1071-1085.

    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. Christensen, Kim & Kinnebrock, Silja & Podolskij, Mark, 2010. "Pre-averaging estimators of the ex-post covariance matrix in noisy diffusion models with non-synchronous data," Journal of Econometrics, Elsevier, vol. 159(1), pages 116-133, November.
    2. Xingang Wang & Sholeh A. Maani, 2021. "Ethnic regional networks and immigrants' earnings: A spatial autoregressive network approach," Papers in Regional Science, Wiley Blackwell, vol. 100(1), pages 141-168, February.
    3. Baum, Matthias & Schwens, Christian & Kabst, Ruediger, 2015. "A latent class analysis of small firms’ internationalization patterns," Journal of World Business, Elsevier, vol. 50(4), pages 754-768.
    4. Andrey Sokolov & Andrew Melatos & Tien Kieu, 2010. "Laplace transform analysis of a multiplicative asset transfer model," Papers 1004.5169, arXiv.org.
    5. de Mattos Neto, Paulo S.G. & Silva, David A. & Ferreira, Tiago A.E. & Cavalcanti, George D.C., 2011. "Market volatility modeling for short time window," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(20), pages 3444-3453.
    6. Sokolov, Andrey & Melatos, Andrew & Kieu, Tien, 2010. "Laplace transform analysis of a multiplicative asset transfer model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(14), pages 2782-2792.
    7. Francesca Bassi & Fulvia Pennoni & Luca Rossetto, 2020. "The Italian market of sparkling wines: Latent variable models for brand positioning, customer loyalty, and transitions across brands' preferences," Agribusiness, John Wiley & Sons, Ltd., vol. 36(4), pages 542-567, October.
    8. Saerom Park & Jaewook Lee & Youngdoo Son, 2016. "Predicting Market Impact Costs Using Nonparametric Machine Learning Models," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-13, February.
    9. Desjana Grymshi & Eva Crespo‐Cebada & Ahmed Elghannam & Francisco J. Mesías & Carlos Díaz‐Caro, 2022. "Understanding consumer attitudes towards ecolabeled food products: A latent class analysis regarding their purchasing motivations," Agribusiness, John Wiley & Sons, Ltd., vol. 38(1), pages 93-107, January.
    10. Sven Lindmark, 2009. "Web 2.0: Where does Europe stand?," JRC Research Reports JRC53035, Joint Research Centre.
    11. Alfaro, Martin & Lander, David, 2019. "A Unified Explanation of Trade Liberalization Effects Across Models of Imperfect Competition," Working Papers 2019-16, University of Alberta, Department of Economics.
    12. Abayomi Oredegbe, 2021. "Cost Efficiency Determinants: Evidence from the Canadian Banking Industry," International Journal of Business and Management, Canadian Center of Science and Education, vol. 15(1), pages 1-86, July.
    13. Ritika Chopra & Gagan Deep Sharma, 2021. "Application of Artificial Intelligence in Stock Market Forecasting: A Critique, Review, and Research Agenda," JRFM, MDPI, vol. 14(11), pages 1-34, November.
    14. Shujian Liao & Jian Chen & Hao Ni, 2021. "Forex Trading Volatility Prediction using Neural Network Models," Papers 2112.01166, arXiv.org, revised Dec 2021.
    15. Yusaku Nishimura & Yoshiro Tsutsui & Kenjiro Hirayama, 2012. "Return and Volatility Spillovers between Japanese and Chinese Stock Markets FAn Analysis of Overlapping Trading Hours with High-frequency Data," Discussion Papers in Economics and Business 12-01, Osaka University, Graduate School of Economics.

    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:10:y:2018:i:7:p:56. 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.