Abstract
Exploring the value of multi-source information fusion to predict small and medium-sized enterprises’ (SMEs) credit risk in supply chain finance (SCF) is a popular yet challenging task, as two issues of key variable selection and imbalanced class must be addressed simultaneously. To this end, we develop new forecast models adopting an imbalance sampling strategy based on machine learning techniques and apply these new models to predict credit risk of SMEs in China, using financial information, operation information, innovation information, and negative events as predictors. The empirical results show that the financial-based information, such as TOC, NIR, is most useful in predicting SMEs’ credit risk in SCF, and multi-source information fusion is meaningful in better predicting the credit risk. In addition, based on the preferred CSL-RF model, which extends cost-sensitive learning to a random forest, we also present the varying mechanisms of key predictors for SMEs’ credit risk by using partial dependency analysis. The strategic insights obtained may be helpful for market participants, such as SMEs’ managers, investors, and market regulators.
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Acknowledgements
The authors gratefully acknowledge financial support from the National Natural Science Foundation of China (71902159, 72171070, 71729001, 72025101), the Humanity and Social Science Foundation of Ministry of Education of China (20YJA630024), and the Fundamental Research Funds for the Central Universities (No. FRF-DF-20-11) the China Postdoctoral Science Foundation under Grant 2021M700380.
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Appendices
Appendix A: The pseudocode of SVM-based classifier
Appendix B: The pseudocode of ANN-based classifier
Appendix C: The pseudocode of C4.5 DT
Appendix D: The pseudocode of RF
Appedix E: The pseudocode of bagging
Appendix F: The pseudocode of GB
Appendix G: Variables and definition
Abbreviated | Attribute | Definition |
|---|---|---|
Supply chain capabilities | ||
TPS | CON | TPS is total purchases amount of enterprise from the top five suppliers |
PAS | CON | PAS is the proportion of the total purchase amount from the top five suppliers |
PRP | CON | PRP is the number of listed enterprises in the top five suppliers |
TFR | CON | TFR is total revenue of enterprise from the top five customers |
PTC | CON | PTC is the proportion of the total revenue from the top five customers |
PSP | CON | PSP is the number of listed enterprises in the top five customers |
Capital capabilities | ||
SCR | CON | SCR is the ratio of the net cash flows to the enterprise’s sales income |
CIR | CON | CIR is the ratio of cash flows to capital expenditure and cash dividends |
COI | CON | COI is the ratio of operating cash flows to operating cash |
CRA | CON | CRA is the ratio of net operating cash flows to total assets |
Management capabilities | ||
INT | CON | INT is the average times for an enterprise sold its total during a year |
TRA | CON | TRA is a measure for receivables by clients |
TRC | CON | TRC is the times of the current assets are turned over in a year |
ROA | CON | ROA compares the sales of an enterprise to its asset base |
APT | CON | APT refers to the liquidity of accounts payable of an enterprise |
ITD | CON | ITD is the number of days that an enterprise sells its inventory during a given year |
Profit capabilities | ||
NPT | CON | NPT is the ratio of net profit to total profits |
NIR | CON | NIR is ratio of the sum of net profit and shareholders’ profit to main business revenue |
TOC | CON | TOC is the proportion of operating costs to operating revenue |
MER | CON | MER is the management fee to revenue of main business |
CTP | CON | CTP is the ratio of cash to total profits |
Growth capabilities | ||
GTR | CON | GTR is the growth rate of total operating revenue for an enterprise |
NPR | CON | NPR is the year-on-year growth rate of net profit for an enterprise |
GTL | CON | GTL is the growth rate of total liabilities for an enterprise |
GRA | CON | GRA is the growth rate of total assets for an enterprise |
Solvency capabilities | ||
CUR | CON | GUR is a liquidity ratio that a firm has resources to meet its short-term obligations |
QUR | CON | QUR is the ratio that quick assets to extinguish its current liabilities immediately |
CAS | CON | CAS is the ratio of the sum of monetary to total current |
RBA | CON | RBA is the ratio of long-term borrowing to total assets |
Innovation capabilities | ||
NPA | CON | NPA is the total number of invention patent applications |
NPO | CON | NPO is the total number of invention patent granted |
RDP | CON | RDP is the ratio of R&D personnel to total employees |
RDR | CON | RDR is the ratio of R&D investment to total revenue |
Negative events | ||
NLD | BIN | NLD = 1 if the enterprise is involved in litigation disputes, else 0 |
CPR | BIN | CPR = 1 if the enterprise is punished by the regulator, else 0 |
EPR | BIN | EPR = 1 if the executives is punished by the regulator, else 0 |
MNN | BIN | MNN = 1 if the enterprise disclosed by the media with negative news, else 0 |
Related transactions | ||
TRT | MUL | TRT = 1 is loans; TRT = 2 is sell products; TRT = 3 is purchase assets; TRT = 4 is accept services; TRT = 5 is purchase goods; TRT = 6 is equity; TRT = 7 is provision of goods or services; TRT = 8 is rents; TRT = 9 is payment of expenses; TRT = 10 is asset transaction; TRT is fee; TRT = 12 is financial dealings; TRT = 13 is investment; TRT = 14 is proxy; TRT = 15 is construction; TRT = 16 is technical services; TRT = 17 is hydropower supply; TRT = 18 is receivable and payable; TRT = 19 is consulting services; TRT = 20 is transfer of assets; TRT = 21 is others |
MRT | CON | MRT is the total currency amount of related transactions |
CRR | BIN | CRR = 1 if the control relationship occurred between related enterprise and the enterprise, else 0 |
Appendix H: Relative importance for each predictor (%)
Variable | SVM | NN | DT | RF | BA | BO | Variable | SVM | NN | DT | RF | BA | BO |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
TPS | 0.00 | 0.00 | 1.86 | 2.05 | 3.63 | 1.55 | CTP | 0.00 | 0.00 | 2.11 | 2.01 | 1.38 | 1.51 |
PAS | 2.44 | 2.41 | 0.00 | 2.52 | 1.24 | 1.87 | GTR | 9.80 | 9.79 | 5.05 | 8.71 | 5.64 | 9.14 |
PRP | 0.07 | 0.07 | 0.00 | 0.60 | 0.21 | 0.01 | NPR | 0.28 | 0.00 | 7.78 | 1.93 | 1.39 | 4.34 |
TFR | 0.87 | 0.10 | 3.20 | 1.73 | 1.76 | 1.42 | GTL | 0.00 | 0.00 | 3.20 | 2.86 | 1.78 | 2.72 |
PTC | 2.64 | 1.60 | 4.90 | 3.80 | 2.34 | 1.57 | GRA | 4.02 | 5.61 | 7.12 | 4.85 | 3.57 | 6.08 |
PSR | 0.03 | 0.03 | 0.00 | 0.43 | 0.41 | 0.03 | CUR | 1.35 | 1.79 | 0.00 | 1.99 | 1.43 | 1.44 |
SCR | 2.79 | 3.82 | 0.00 | 4.09 | 2.75 | 5.26 | QUR | 0.80 | 1.08 | 0.00 | 2.16 | 4.55 | 1.51 |
CIR | 0.00 | 0.00 | 3.74 | 1.64 | 2.66 | 1.64 | CAS | 5.17 | 6.13 | 8.21 | 4.38 | 5.57 | 3.76 |
COI | 2.88 | 2.66 | 0.00 | 2.08 | 2.13 | 2.45 | RBA | 1.72 | 1.67 | 0.00 | 2.53 | 2.09 | 2.97 |
CRA | 5.58 | 5.80 | 1.87 | 1.53 | 1.55 | 2.04 | NPA | 0.00 | 0.04 | 1.97 | 1.98 | 1.83 | 0.86 |
INT | 0.00 | 0.00 | 0.00 | 1.74 | 4.21 | 2.69 | NPO | 0.18 | 0.18 | 0.00 | 1.29 | 1.58 | 0.93 |
TRA | 0.00 | 0.03 | 4.84 | 1.83 | 2.88 | 0.84 | RDP | 2.00 | 1.43 | 0.00 | 2.46 | 4.39 | 2.99 |
TRC | 3.19 | 3.16 | 2.22 | 2.58 | 3.43 | 3.42 | RDR | 2.53 | 2.53 | 0.00 | 2.87 | 3.53 | 3.21 |
ROA | 4.96 | 5.81 | 1.85 | 2.72 | 2.76 | 3.63 | NLD | 0.31 | 0.00 | 0.00 | 0.25 | 0.00 | 0.08 |
APT | 1.31 | 1.30 | 2.36 | 2.27 | 1.26 | 3.89 | CPR | 4.58 | 4.14 | 2.52 | 0.72 | 3.01 | 1.06 |
ITD | 4.63 | 3.77 | 4.84 | 3.70 | 3.78 | 2.14 | EPR | 1.44 | 1.75 | 2.79 | 0.57 | 0.30 | 0.18 |
NPT | 0.00 | 0.00 | 0.00 | 3.14 | 2.11 | 2.91 | MNN | 2.28 | 2.28 | 0.00 | 0.90 | 0.32 | 0.11 |
NIR | 13.25 | 12.27 | 14.106 | 5.17 | 6.13 | 5.51 | TRT | 0.48 | 0.47 | 0.00 | 1.12 | 1.93 | 0.69 |
TOC | 14.66 | 15.65 | 13.53 | 6.36 | 4.40 | 8.82 | MRT | 2.83 | 0.74 | 0.00 | 3.83 | 3.44 | 3.39 |
MER | 0.82 | 0.81 | 0.00 | 2.20 | 1.54 | 1.22 | CRR | 0.15 | 0.27 | 0.00 | 0.39 | 0.00 | 0.17 |
Appendix I: The selected key predictor via ensemble learning
Models | TPS | PAS | PRP | TFR | PTC | PSR | SCR | CIR | COI | CRA | INT | TRA | TRC | ROA |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
SVM | X | X | X | X | √ | X | √ | X | √ | √ | X | X | √ | √ |
NN | X | X | X | X | X | X | √ | X | √ | √ | X | X | √ | √ |
DT | X | X | X | √ | √ | X | √ | √ | X | X | X | √ | X | X |
RF | X | √ | X | X | √ | X | X | X | X | X | X | X | √ | √ |
BA | √ | X | X | X | √ | X | √ | √ | X | X | √ | √ | √ | √ |
BO | X | X | X | X | X | X | √ | X | X | X | √ | X | √ | √ |
EM | X | X | X | X | √ | X | √ | X | X | X | X | X | √ | √ |
Models | APT | ITD | NPT | NIR | TOC | MER | CTP | GTR | NPR | GTL | GRA | CUR | QUR | CAS |
SVM | X | √ | X | √ | √ | X | X | √ | X | X | √ | X | X | √ |
NN | X | √ | X | √ | √ | X | X | √ | X | X | √ | X | X | √ |
DT | X | √ | X | √ | √ | X | X | √ | √ | √ | √ | X | X | √ |
RF | X | √ | √ | √ | √ | X | X | √ | X | X | √ | X | X | √ |
BA | X | √ | X | √ | √ | X | X | √ | X | X | √ | √ | √ | √ |
BO | √ | X | √ | √ | √ | X | X | √ | √ | √ | √ | X | X | √ |
EM | X | √ | X | √ | √ | X | X | √ | X | X | √ | X | X | √ |
Models | RBA | NPA | NPO | RDP | RDR | NLD | CPR | EPR | MNN | TRT | MRT | CRR | ||
SVM | X | X | X | X | √ | X | √ | X | X | X | √ | X | ||
NN | X | X | X | X | √ | X | √ | X | X | X | X | X | ||
DT | X | X | X | X | X | X | √ | √ | X | X | X | X | ||
RF | √ | X | X | X | √ | X | X | X | X | X | √ | X | ||
BA | X | X | X | √ | √ | X | √ | X | X | X | √ | X | ||
BO | √ | X | X | √ | √ | X | X | X | X | X | √ | X | ||
EM | X | X | X | X | √ | X | √ | X | X | X | √ | X |
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Wang, L., Jia, F., Chen, L. et al. Forecasting SMEs’ credit risk in supply chain finance with a sampling strategy based on machine learning techniques. Ann Oper Res 331, 1–33 (2023). https://doi.org/10.1007/s10479-022-04518-5
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DOI: https://doi.org/10.1007/s10479-022-04518-5