Firm Characteristics and Long-Run Abnormal Returns after IPOs: A Jordanian Financial Market Experience
Bibliographic record
Abstract
This study aims to detect the long run performance of the Jordanian initial public offerings (IPOs) listed in Amman stock exchange during the period from (1st January, 1993 until 31st December, 2011). In order to achieve the study’s objectives, the researcher applied “The Event Study” approach on the study sample which is consisted of all the Jordanian initial public offerings that are listed in Amman stock exchange during the study period, which were (119) companies. We calculated the monthly returns of these companies for 60 months (5 years) after listing. Also, a simple linear regression model applied to explore the relationship between the companies’ characteristics such as (company age, size, the sector in which the company belongs, and the offer size), and the abnormal return (AR) by using the three benchmarks that are employed in the study. The results of the analysis showed that the study corresponds to most of the previous studies with regard to the long run underperformance phenomenon for the initial public offerings (IPOs), but the level of this underperformance was different based on the benchmark employed to measure the long run performance. This conclusion was also confirmed by some previous studies. This study showed that there are statistically significant differences in the abnormal returns (AR) after applying the three benchmarks by using the parametric ‘‘One Sample T-test”. Finally, by running simple linear regressions, the study showed that there is statistically significant positive relationship between the characteristics of the firm (size, age, sector, and offer size) and the abnormal return (AR) after applying various benchmarks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".