A review of IPO selling methods: Is there a clear winner?
Bibliographic record
Abstract
After the hot IPO market of 1999/2000, numerous U.S. underwriters have been sued in connection with unfair IPO allocation schemes. In these lawsuits, plaintiffs contend that the underwriters engaged in illegal tactics by soliciting and receiving kickbacks in exchange for allocations of portions of a company’s IPO, required tie-in purchases creating an artificial demand for the stock, and artificially inflated the price of the stock through “laddering” (requiring purchases of additional stock in the aftermarket at escalating prices). The proliferation of these laddering schemes has inspired several government agencies and regulatory bodies to seek alternatives for a fairer way to sell IPO shares to the public. While auctions such as that used by Google alleviate issues related to unfair share allocation, they are associated with other problems which make them unattractive for many issuers. Our study discusses the advantages and disadvantages of the existing selling methods. While there is no clear-cut answer as to what constitutes the bestselling method, our study should provide corporate managers with the necessary insights that are needed to choose the method that best meets their objectives. In addition, our study aims to open the door for further academic discussion that is required to address a number of questions that to date remain unanswered in this area.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".