Identifying and Predicting the Credit Risk of Small and Medium-Sized Enterprises in Sustainable Supply Chain Finance: Evidence from China
Abstract
1. Introduction
2. Literature Review
2.1. Sustainable Supply Chain Finance
2.2. Influencing Factors and Prediction Models of Credit Risk in SCF
3. Research Methodology
3.1. Lasso Model
3.2. Lasso-Logistic Model
3.3. Model Evaluation
4. Credit Risk Triggering Mechanism and Variable Definition
4.1. Credit Risk Triggering Mechanism
4.2. Variable Definitions
4.3. Data Sources and Pre-Processing
5. Simulations
6. Experimental Results and Analysis
6.1. Identification of the Factors Influencing the Credit Risk of SMEs
6.2. Evaluation of the Lasso-Logistic Model
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Actual Value | Predicted Value | Total | |
---|---|---|---|
0 (Non-Default) | 1 (Default) | ||
0 (Non-default) | a | b | a + b |
1 (Default) | c | d | c + d |
Total | a + c | b + d | a + b + c + d |
First Level Variables | Second Level Variables | Third Level Variables | Indexes | Variables Description |
---|---|---|---|---|
Corporate Credit | Non-financial factors of SMEs | Registered capital | X1 | Registered capital of SME |
Number of employees | X2 | Number of employees | ||
Time of establishment | X3 | Time of establishment | ||
Financial factors of SMEs | Return on equity | X4 | Net margin divided by total net assets | |
Operating profit ratio | X5 | Operating profit divided by sales revenue | ||
Current assets turnover ratio | X6 | Net income from main business divided by average total current assets | ||
Operating growth rate | X7 | Growth of current operating income divided by operating income of last year | ||
Debt asset ratio | X8 | Total l debt divided by total assets | ||
Transaction Credit | The authenticity of transaction | Matching degree of order data | X9 | Matching degree of order flow, tax flow and capital flow for cross validation, which is divided into 5 grades |
The performance of transaction | Ratio of on-time delivery | X10 | Ratio of on-time delivery according to the contract | |
Ratio of contract enforcement | X11 | Actual delivery divided by contractual delivery | ||
Number of contract defaults | X12 | Number of contract defaults | ||
The sustainability of transaction | Degree of business concentration | X13 | Degree of business concentration, which is divided into 5 grades. | |
The social relations of SMEs | X14 | Number of managers holding positions in other enterprises | ||
Reputation and Supervision Behavior | Records of rewards and punishments from government departments | History of litigation | X15 | The value of 1 indicates that there is a history of litigation; The value of 0 means no litigation history |
Number of being listed on credit blacklists | X16 | Number of being listed on credit blacklists | ||
Tax incentive record | X17 | Number of tax rating as A | ||
Number of administrative penalties | X18 | Number of administrative penalties | ||
Number of abnormal operations | X19 | Number of abnormal operations | ||
Business reputation | Industry recognition | X20 | Dynamic evaluation from the relevant website, which is divided into five grades. |
Model | Sample Size | The Number of Non-Zero Coefficients Estimated | The Number of Correctly Estimated Zero Coefficients | The Number of Correctly Estimated Non-Zero Coefficients | Prediction Accuracy | AUC | ||
---|---|---|---|---|---|---|---|---|
The Training Set | The Test Set | The Training Set | The Test Set | |||||
Lasso- logistic model | 100 | 4.050 | 3.950 | 3.000 | 0.996 | 0.978 | 0.999 | 0.998 |
200 | 4.250 | 3.700 | 3.000 | 0.989 | 0.979 | 0.999 | 0.999 | |
500 | 4.800 | 3.200 | 3.000 | 0.994 | 0.982 | 0.999 | 0.999 | |
Ridge regression model | 100 | 3.800 | 4.200 | 3.000 | 0.964 | 0.930 | 0.996 | 0.989 |
200 | 4.150 | 3.850 | 3.000 | 0.980 | 0.961 | 0.998 | 0.995 | |
500 | 3.750 | 4.150 | 3.000 | 0.968 | 0.952 | 0.997 | 0.993 | |
Logistic regression model | 100 | 2.000 | 5.000 | 2.000 | 0.936 | 0.925 | 0.983 | 0.973 |
200 | 2.000 | 5.000 | 2.000 | 0.920 | 0.892 | 0.977 | 0.960 | |
500 | 2.000 | 5.000 | 2.000 | 0.901 | 0.900 | 0.973 | 0.974 |
Explanatory Variables | βj | VIF | Explanatory Variables | βj | VIF |
---|---|---|---|---|---|
X9 | −1.355 | 1.805 | X13 | 0.809 | 1.416 |
X11 | −0.063 | 3.865 | X18 | −0.018 | 1.060 |
X12 | 2.351 | 4.556 | / | / | / |
Models | Prediction Accuracy | Type I Error | Type II Error | AUC | |
---|---|---|---|---|---|
Ridge regression model | 0.958 | 0.033 | 0.049 | 0.959 | |
The train set | Lasso-logistic model | 0.965 | 0.033 | 0.037 | 0.965 |
BP neural network | 0.923 | 0.033 | 0.111 | 0.928 | |
Ridge regression model | 0.934 | 0.048 | 0.084 | 0.934 | |
The test set | Lasso-logistic model | 0.964 | 0.044 | 0.032 | 0.962 |
BP neural network | 0.903 | 0.049 | 0.148 | 0.903 |
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Yang, Y.; Chu, X.; Pang, R.; Liu, F.; Yang, P. Identifying and Predicting the Credit Risk of Small and Medium-Sized Enterprises in Sustainable Supply Chain Finance: Evidence from China. Sustainability 2021, 13, 5714. https://doi.org/10.3390/su13105714
Yang Y, Chu X, Pang R, Liu F, Yang P. Identifying and Predicting the Credit Risk of Small and Medium-Sized Enterprises in Sustainable Supply Chain Finance: Evidence from China. Sustainability. 2021; 13(10):5714. https://doi.org/10.3390/su13105714
Chicago/Turabian StyleYang, Yubin, Xuejian Chu, Ruiqi Pang, Feng Liu, and Peifang Yang. 2021. "Identifying and Predicting the Credit Risk of Small and Medium-Sized Enterprises in Sustainable Supply Chain Finance: Evidence from China" Sustainability 13, no. 10: 5714. https://doi.org/10.3390/su13105714
APA StyleYang, Y., Chu, X., Pang, R., Liu, F., & Yang, P. (2021). Identifying and Predicting the Credit Risk of Small and Medium-Sized Enterprises in Sustainable Supply Chain Finance: Evidence from China. Sustainability, 13(10), 5714. https://doi.org/10.3390/su13105714