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The Evaluation Model of Enterprise Credit Default Based on Logistic Regression

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DOI: 10.23977/ferm.2020.030118 | Downloads: 13 | Views: 639

Author(s)

Guoqing Wang 1, Xialing Wu 2, Chenxi Xu 3

Affiliation(s)

1 School of Finance, Anhui University of Finance and Economics, Bengbu, Anhui, 233000
2 School of Management Science and Engineering, Anhui University of Finance and Economics, Bengbu, Anhui, 233000
3 School of International Economics and Trade, Anhui University of Finance and Economics, Bengbu, Anhui, 233000

Corresponding Author

Guoqing Wang

ABSTRACT

Since entering the new economic normal, my country's small, medium and micro enterprises have continued to reform and develop and become an important part of the real economy. The bank will conduct an overall assessment of SMEs and formulate corresponding credit strategies based on the business strength, development potential, creditworthiness, and default risks of the enterprise. While providing financial support to SMEs as much as possible, the bank will achieve interest rates on bank credit. Maximize. This paper establishes a Logistic regression model to quantify the default records and credit ratings of small and medium-sized enterprises, and builds a credit default evaluation model for enterprises; and then uses structural equations to reflect the relationship between corporate strength, default risk, bank credit lines, and bank credit interest rates. Formulate specific credit strategies. Use AHP improved by the cloud model to screen the industry to which the company belongs; then establish a change-point model to analyze the volatility of the company's operating conditions and default risks when affected by emergencies, and to fit the evolution path of different companies in the future ; Finally, through the establishment of a neural network model under the co-integration theory, the credit default risk is predicted, and the degree of impact of different industries is ranked, thereby adjusting the bank's credit strategy.

KEYWORDS

Logistic regression, structural equation, decision tree, AHP under cloud model, cointegration neural network

CITE THIS PAPER

Guoqing Wang, Xialing Wu, Chenxi Xu, The Evaluation Model of Enterprise Credit Default Based on Logistic Regression. Financial Engineering and Risk Management (2020) 3: 121-126. DOI: http://dx.doi.org/10.23977/ferm.2020.030118.

REFERENCES

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