Bibliographic record
Abstract
• In this short paper, the general characteristics surrounding the voluntary release of prospectus earnings forecasts are examined for a sample of recently organised initial public offerings in Hong Kong. In terms of forecast accuracy, results, relative to earlier Hong Kong-based studies [Chan et al. (1996), Jaggi (1997), Cheng and Firth (2000) and Chen, Firth and Krishnan (2001)], point to an even tighter distribution of forecast errors than was the case for IPOs in the early to mid 1990s. A more sophisticated primary market- in which dual-tranche offerings, many with variable offer prices and over-allotment options, dominate- allied to improved corporate governance practice is the likely reason for this outcome. • Post-listing deviations from prospectus forecasts are also noted to be positively related to the percentage of equity retained in the newly listed firms by pre-listing stakeholders as well as to the utilisation of over-allotment options by issue underwriters. Finally, the importance of underpricing to the disclosure of IPO earnings forecasts- as signalled in the work of Jog and McConomy (2003) for Canadian IPOs- is partially reflected through the over-allotment option exercise decision variable. The latter is strongly and positively related to both the incidence of a prospectus earnings forecast and the magnitude of IPO underpricing. 1 This paper is for pure fact-finding and research purposes, and is not an attempt to comment on the developments of any markets/ companies or interpret the policies concerned. The views expressed in this paper do not represent those of the SFC. The author would like to thank Kevin Keasey, Michael Ferguson and an anonymous reviewer for their comments on an earlier draft of this
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.633 | 0.503 |
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".