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A Meta Path Based Evaluation Method for Enterprise Credit Risk

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  • Marui Du
  • Yue Ma
  • Zuoquan Zhang

Abstract

Nowadays small and medium-sized enterprises have become an essential part of the national economy. With the increasing number of such enterprises, how to evaluate their credit risk becomes a hot issue. Unlike big enterprises with massive data to analyze, it is hard to find enough information of small enterprises to assess their financial status. Limited by the lack of primary data, how to inference small enterprises' credit risk from secondary data, like information of their upstream, downstream, parent, and subsidiary enterprises attracts big attention from industry and academy. Targeting on accurately evaluating the credit risk of the small and medium-sized enterprise (SME), in this paper, we exploit the representative power of Information Network on various kinds of SME entities and SME relationships to solve the problem. A novel feature named meta path feature proposed to measure the credit risk, which makes us able to evaluate the financial status of SMEs from various perspectives. Experiments show that our method is effective to identify SMEs with credit risks.

Suggested Citation

  • Marui Du & Yue Ma & Zuoquan Zhang, 2021. "A Meta Path Based Evaluation Method for Enterprise Credit Risk," Papers 2110.11594, arXiv.org, revised May 2022.
  • Handle: RePEc:arx:papers:2110.11594
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    References listed on IDEAS

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    1. Psillaki, Maria & Tsolas, Ioannis E. & Margaritis, Dimitris, 2010. "Evaluation of credit risk based on firm performance," European Journal of Operational Research, Elsevier, vol. 201(3), pages 873-881, March.
    2. Loredana Cultrera & Xavier Brédart, 2016. "Bankruptcy prediction: the case of Belgian SMEs," Review of Accounting and Finance, Emerald Group Publishing Limited, vol. 15(1), pages 101-119, February.
    3. Moro, Andrea & Fink, Matthias, 2013. "Loan managers’ trust and credit access for SMEs," Journal of Banking & Finance, Elsevier, vol. 37(3), pages 927-936.
    4. Bauer, Julian & Agarwal, Vineet, 2014. "Are hazard models superior to traditional bankruptcy prediction approaches? A comprehensive test," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 432-442.
    5. Edmister, Robert O., 1972. "An Empirical Test of Financial Ratio Analysis for Small Business Failure Prediction," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 7(2), pages 1477-1493, March.
    6. Aneta Ptak-Chmielewska, 2019. "Predicting Micro-Enterprise Failures Using Data Mining Techniques," JRFM, MDPI, vol. 12(1), pages 1-17, February.
    7. Sermpinis, Georgios & Tsoukas, Serafeim & Zhang, Ping, 2018. "Modelling market implied ratings using LASSO variable selection techniques," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 19-35.
    8. Tian, Shaonan & Yu, Yan & Guo, Hui, 2015. "Variable selection and corporate bankruptcy forecasts," Journal of Banking & Finance, Elsevier, vol. 52(C), pages 89-100.
    9. Tsai, Ming-Feng & Wang, Chuan-Ju, 2017. "On the risk prediction and analysis of soft information in finance reports," European Journal of Operational Research, Elsevier, vol. 257(1), pages 243-250.
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