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How the small and medium-sized enterprises’ owners’ credit features affect the enterprises’credit default behavior?

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  • Wanting Wang

    (School of Finance, Central University of Finance and Economics, Beijing, China, E-mail:wangwanting123@163.com; Tel.1-864-986-8588; Address: No.557 Somerville Avenue, MA, Boston, USA 02143.)

Abstract

Identifying and measuring credit risk of small and medium-sized enterprises should be different from that of large firms, for SMEs appear to be influenced by their owners more directly and significantly. This paper attempts to testify the relationship between default behaviors of SMEs and the credit features of their owners, so that a more appropriate and effective way of credit management of SMEs could be applied in practice. After segregating the owners’ characteristics data into variables of basic features, credit capacity features and credit will features, this paper implemented an empirical study of logistic regression analysis with repeat sampling data. The result demonstrates that, compared with the owners’ credit will variables, credit capacity features share more significant relationship with the SMEs’ credit default behavior. Specifically, the age of owner, the inquiry frequency of owners’ credit information for post-loan risk management and pro-loan approval purpose, and the proportion of overdue loans are the extreme significant variables which are valuable indicators in default risk estimate model.

Suggested Citation

  • Wanting Wang, 2012. "How the small and medium-sized enterprises’ owners’ credit features affect the enterprises’credit default behavior?," E3 Journal of Business Management and Economics., E3 Journals, vol. 3(2), pages 090-095.
    • Handle: RePEc:etr:series:v:3:y:2012:i:2:p:090-095
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    References listed on IDEAS

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    1. Avery, Robert B. & Bostic, Raphael W. & Samolyk, Katherine A., 1998. "The role of personal wealth in small business finance," Journal of Banking & Finance, Elsevier, vol. 22(6-8), pages 1019-1061, August.
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    Cited by:

    1. Dumitrescu, Elena & Hué, Sullivan & Hurlin, Christophe & Tokpavi, Sessi, 2022. "Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1178-1192.
    2. Elena Ivona DUMITRESCU & Sullivan HUE & Christophe HURLIN & Sessi TOKPAVI, 2020. "Machine Learning or Econometrics for Credit Scoring: Let’s Get the Best of Both Worlds," LEO Working Papers / DR LEO 2839, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.

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