An Empirical Study of Credit Risk of Supply Chain Finance——Based on MF-Logistic Model
Bibliographic record
Abstract
Supply chain finance, a transition from the traditional credit mode in commercial banks, has brought the participants into a win-win situation. In China, supply chain finance is still in the initial stage. Aiming to gain profits, the commercial banks actively develop their supply chain finance businesses, but their credit risk management of the supply chain finance is relatively backward. This paper chooses macroeconomic indicators and credit risk assessment indicators of enterprise at the micro level, and uses econometrics methods to build an MF-Logistic model containing macroeconomic factors and reflecting the financial index of enterprise credit capacity. And then we conduct an empirical study on 27 groups of sample data from the first quarter of 2007 to the third quarter of 2013, and come to conclusions by the empirical study and normative analysis. Through measurement and prediction of credit risk of supply chain finance, this paper offers pre-warning against credit risk of supply chain finance for commercial banks, and provides reference for formulating corresponding measures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".