Bibliographic record
Abstract
In this paper we conduct a comprehensive study on China’s second board, ChiNext. We compare ChiNext against other major second boards in the world to assess its performance in attracting new listings and facilitating capital raising. We find that it has fared very well in these aspects. We examine the firm characteristics, pre-IPO operating performance, and governance practices and ownership structures for 355 ChiNext firms. We find that these are young and small firms that are profitable and experiencing high growth. They generally adopt good governance practices. Their ownership remains to be highly concentrated after their IPOs. We explore the determinants of IPO underpricing and find that IPOs of larger and more profitable firms are less underpriced, while those of firms with high volatility are more underpriced. IPOs conducted in a hot IPO market are less underpriced. In addition, investors may perceive the length of the time interval between the IPO issue date and the listing date on ChiNext as a signal of firm quality: the longer it takes a firm to list its IPO shares on ChiNext, the more its shares are underpriced. Our paper contributes to the IPO literature, provides insight into Chinese private enterprises, and sheds light on factors affecting the success of a second board.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".