Understanding the Dynamics of IPO Underpricing and Its Effect on Bond Issuance Strategies
Bibliographic record
Abstract
Important milestones in a company's life cycle, IPOs are often marked by underpricing. The equities of high-tech enterprises on the China Scientific and Technological Innovation Board (STAR Markets) are significantly underpriced during IPOs. In this paper, we use the Two-tier Stochastic Frontier Models to break IPO underpricing down into its component parts—the pricing effect of the primary market while the transaction effect of the secondary market—and then we examine how these two markets differ in their effects on IPO underpricing. We do this from an investor behavior perspective in order to understand why STAR Market has such high IPO underpricing. Furthermore, company size has little bearing on the IPO underpricing. As a result, the STAR Market's IPO underpricing has historically been mostly influenced by secondary market investor activity. To investigate this, we used Ordinary Least Squares regression modeling to look at how underpricing affected the long-term success of IPOs. In China's STAR market, underpriced IPOs were associated with better long-term success, according to the regression results. We provide an evolving framework of a market for IPOs, where companies seek investment funds by becoming public. In making decisions about going public, raising and investing funds, and pricing the IPO, the initial shareholders have access to confidential information about the quality of their company's investment prospects. There are two categories of outside investors: those who are privy to the original shareholders' hidden financial motivations and those who learn about the IPO market only from publicly available IPO market data.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".