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Research on the Measurement of Subject Credit Risk of Chinese Port Enterprises by Constrained Logistic Regression

Author

Listed:
  • Ming Wu

    (School of Management, University of Science and Technology of China, Hefei 230026, P. R. China)

  • Gang Cheng

    (School of Statistics, University of Minnesota, MN 55112, USA)

  • Jiajing Gao

    (School of Management, Shanghai University, Shanghai 200444, P. R. China)

Abstract

This paper studies the subject credit risk of Chinese port enterprises. Since the impact of cash flow ability on credit risk measurement will be increased under extreme case, ordinary logistic regression methods may lack explanatory power for port enterprise default under extreme cases. Considering the characteristics of cash flow in port industry, we introduce the constrained logistic regression method to establish a default probability model which can describe the credit risk level of the industry with higher accuracy in the extreme case where an enterprise’s quick ratio is lower than a cutoff point, For empirical study, we leverage the data of more than 900 companies in port and transportation industry in 2016–2018. The constrained logistic regression splits the data into two subspaces based on quick ratios with the cutoff of 1.8. Then logistic regression is built on the two subspaces, respectively. The recall ratios show that the constrained logistic regression method performs better than the ordinary logistic regression on the study of corporate default probability in port and transportation industry.

Suggested Citation

  • Ming Wu & Gang Cheng & Jiajing Gao, 2021. "Research on the Measurement of Subject Credit Risk of Chinese Port Enterprises by Constrained Logistic Regression," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 38(03), pages 1-22, June.
    • Handle: RePEc:wsi:apjorx:v:38:y:2021:i:03:n:s0217595920400163
    DOI: 10.1142/S0217595920400163
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    Cited by:

    1. Zhang, Yuanyuan & Zhao, Huiru & Li, Bingkang & Zhao, Yihang & Qi, Ze, 2022. "Research on credit rating and risk measurement of electricity retailers based on Bayesian Best Worst Method-Cloud Model and improved Credit Metrics model in China's power market," Energy, Elsevier, vol. 252(C).

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