Influences of Different Underwriting Mechanisms on Offering Prices and the Case of China
Bibliographic record
Abstract
There seem to be little discussions on the influences of different underwriting mechanisms on offering prices. Underdifferent underwriting mechanisms, the issuer, the investment bank and the investor will consider the offering prices basedon his own benefit. As a result, the offering price is affected by different underwriting mechanisms. In China, most of thestock IPO takes the form of stand-by underwriting. Because of the huge demand on the new share issue from Chineseinvestors, the investment bank almost has not unsold stocks to underwrite. Therefore, there is no big difference in the riskthat the investment bank faces between the firm commitment underwriting and best-effort underwriting. As a result, whenthe Chinese investment bank is allowed to participate in the procedure of pricing offering price, the pricing behavior of theinvestment bank has no big difference in the two risk- similar mechanisms. Considering different advantage and suitabilityof different mechanisms, China needs to change the single underwriting mechanism situation and encourage the practice ofthe firm commitment underwriting mechanism.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".