Research on Credit Risk Evaluation of shipping Enterprises based on Fuzzy Comprehensive Evaluation Method

Mingyue Gao

doi:10.54691/fsd.v3i3.4510

Authors

Mingyue Gao

DOI:

https://doi.org/10.54691/fsd.v3i3.4510

Keywords:

Credit Evaluation; Shipping Enterprises; Credit Risk.

Abstract

The outbreak of the novel coronavirus has dealt a huge blow to all walks of life in the world, and the shipping industry has also suffered a heavy impact. In the post-COVID-19 era, shipping enterprises should base on the current situation, face risks squarely and actively face the impact of the epidemic. Therefore, it is urgent to establish a set of credit risk evaluation model suitable for shipping enterprises to help them recover better. Based on this background, this paper starts from the current situation of shipping enterprises, combines qualitative and quantitative indicators, analyzes the existing risks and risk causes of shipping enterprises, and builds a multi-level credit risk evaluation system for shipping enterprises. Then, A representative shipping company of China A is selected as a case through the example analysis, which indicates that the multi-level fuzzy comprehensive evaluation method can comprehensively and effectively evaluate the credit risk of shipping enterprises.

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References

H. Tong. The future development and restructuring of the international shipping industry: Conference report, Marine policy, Vol. 137 (2022), p. 104956-104956.

M. Y. Tian, W. Y. He. Small and Medium-sized Enterprise Credit Risk Assessment in the SCF Financing Mode-Empirical Research in Auto Industry as an Example, Journal of Industrial Technological & Economics, Vol. 35(6) (2016), No.6, p. 154-160.

L. Yu, Y. Xiao, S. Wang, et al. Credit risk evaluation using a weighted least squares SVM classifier with design of experiment for parameter selection, Expert Systems with Applications,Vol.38(12) (2011), No.12, p. 15392-15399.

Pang, S. L. , WANG, and LI. Credit-Risk Evaluation Model Based on Back-Propagation Algorithm, Mathematics In Practice and Theory, (2003),No.8, p. 48-55.

W. Gang, J. Ma. A hybrid ensemble approach for enterprise credit risk assessment based on Support Vector Machine, Expert Systems with Application, Vol. 39 (2012),No.5, p. 5325-5331.

X. Zhou, W. Jiang, S. Yong. Credit risk evaluation by using nearest subspace method, Procedia Computer Science, Vol. 1 (2010),No.1, p. 2449-2455.

Y Wu, X Li, Q Liu, et al. The analysis of credit risks in agricultural supply chain finance assessment model based on genetic algorithm and backpropagation neural network, Computational Economics, Vol. 5 (2021), No.1, p.1-24.

S. Wang, P. T. Wang, Q. W. Huang. Credit risk assessment of small and medium-sized enterprises based on multi-level fuzzy comprehensive evaluation, Financial theory and practice, Vol. 35 (2014), No.5, p.13-17.

J Xu, L Yu, R Gupta. Evaluating the performance of the government venture capital guiding fund using the intuitionistic fuzzy analytic hierarchy process, Sustainability, Vol. 17 (2020), No.12, p.6908.

X. F. Zhu, Y. J. Wang, L. I. Da-Jun. The Effectiveness Test of the Maximum Membership Principle in Fuzzy Comprehensive Evaluation, Geomatics & Spatial Information Technology, Vol.36 (2016), No.5, p135-137.

Ywa B, Qiang M B. Optimizing freight rate of spot market containers with uncertainties in shipping demand and available ship capacity, Transportation Research Part B: Methodological, Vol. 146 (2021), No. C, p.314-332.

D. Jovii, L. Jeremi, L. Milievi, et al. Warehouse Receipts Functioning To Reduce Market Risk, Economics of Agriculture, Vol. 61 (2014), No.2, p.347-365.