An Equilibrium Analysis of the IPO Risk Information Supply and Demand
Bibliographic record
Abstract
The efficiency of the securities market depends on the degree of the completeness of information. The efficiency is affected by the supply as well as the demand of the information. But there are few studies on the risk information demand of investors, and much fewer studies on the equilibrium of the IPO risk information supply and demand. Because of the frequent occurrences of information fraud behavior in the IPO market of China, Chinese investors pay a lot of attention to the risk information disclosure. This paper discussed the development of the Chinese IPO system and the current situation of the risk information disclosure. And then according to the problems, this paper gave a theoretical analysis of the equilibrium and disequilibrium conditions of the risk information supply and demand in the IPO market by examining the dynamic process between information supply and demand. Through the analysis, this paper found that there exist many information supply and demand deficiencies in the Chinese IPO market. The “make-up” phenomenon in IPO information disclosure has been very serious. The investors’ demand for valuable risk information disclosure is largely ignored. More attention needs urgently to be paid to the information demands of Chinese IPO investors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".