Issuance of Wealth Management Products and Expected Yields; A Shadow Banking Perspective
Bibliographic record
Abstract
In the last decade, shadow banking in China has expanded rapidly, driven predominantly by banking regulations and credit restrictions on specific industries. Wealth management products are considered the largest contributors to the overall shadow banking sector in China. The majority of these products are off the balance sheet and offer much higher yields than conventional deposit rates. This study aims to examine how commercial banks, more specifically small and medium-sized banks (SMBs), utilize wealth management products to offer higher yields on new products. This study comprises the top 30 Chinese banks from the first quarter of 2013 to the last quarter of 2019. A fixed-effects approach was adopted by implementing the panel corrected standard errors (PCSE) and Driscoll and Kraay standard errors (DKSE) models. This study found that for SMBs, the issuance of WMPs has a positive and significant impact on the yields of new products, but there is no such significant relationship exists for large four banks.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".