Bibliographic record
Abstract
Earlier evidence has shown that there are substantial time-varying fluctuations in the issuance activity of IPOs and SEOs. Utilizing a comprehensive dataset of 3054 IPOs and 2853 rights issues launched on the London Stock Exchange (LSE) during the time periods (1987-2007) and (1975-2007) respectively, this study aims to conduct a comprehensive analysis of the main alternative determinants of timing of IPOs and rights issues in a unified framework. Three main theories are tested: (i) favorable business and economic conditions; (ii) stock market conditions: bull market timing versus behavioral timing, and (iii) decreasing adverse selection costs and information spill-over. This study explicitly deals with the methodological and econometric challenges associated with modeling the IPOs and rights issues as time-series non-negative count variables via using auto-regressive Poisson model. The findings are significantly consistent with the adverse selection story; firms tend to make more equity offerings during periods of reduced asymmetric information and market uncertainty, robust to the data frequency and variables tested. These findings stand in line with Gerbich (1996) for UK IPOs and Lowry and Schwert (2002) for US IPOs. For rights issues, the findings exhibit that UK seasoned firms tend to time their offerings mostly during periods of bull stock prices, which is consistent with Michailides (2000) for UK rights issues.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".