The Impact of Business Scale of “Shadow Banking” on Credit Risk of Commercial Banks --Take Ten Domestic Listed Commercial Banks as Examples
Bibliographic record
Abstract
In this article, according to search for the definition of shadow banking, we can make sure the business kinds of “shadow banking”, discuss the influence of business in “shadow banking” on credit risk of commercial banks, and study the elements which may increase the credit risk of commercial banks by using the semi-annual panel data during 2011-2016 of 10 listed banks. Then we can come to some primary conclusions: The credit risk of commercial banks is related to the shadow banking business. All the survival scale increment of financial products increasing, the size of entrusted loans increasing in increment, and the increasing in the size of guarantee commitments will increase the credit risk of commercial banks. There is no obvious relationship between trust loan business and bank credit risk. Our study is of great significance for the government to supervise the off-balance-sheet business of commercial banks. At the same time, it also fills the vacancy of domestic commercial banking “shadow banking” business empirical research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".