Bibliographic record
Abstract
With regard to purchasing Tunisian IPOs shares, the current paper aims at considering two types of investors: a non-institutional investor and an institutional one.Each is concerned with placing a purchase order at the offer price during the subscription period.In line with the literature on IPOs, we attempted to determine the minimum price required by an investor allowing for recovering the initial investment, information costs, transaction costs, and the offsetting of shortfall.We expect that the initial return of an IPO share in the Tunisian market is positively related to the following factors: the number of non-institutional investors participating during the subscription period, the subscription ratio of institutional investors, the expected rate of return by investors, the gap between the closing date of the subscription period and the day following the announcement of the subscription result, the gap between the announcement of the subscription result and the first listing day, the number of trading days, the cost of information and the transaction costs.However, it is negatively related to other determinants, such as the discount level, the number of shares allocated for a non-institutional investor and the number of offered shares, which are allocated to non-institutional investors.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".