The Influence of Growth Opportunities on IPO Initial Aftermarket Performance
Bibliographic record
Abstract
This study examined the influence of growth opportunities of firms on the immediate aftermarket performance of IPOs. The growth opportunities were defined as the amount of proceeds received during IPOs to activities that support the growth of a firm, such as assets acquisition, and research and development (R&D). Acknowledging that not much information about a firm is possibly received by investors prior to its listing in a stock exchange, investors will rely mostly on the information supplied in the “Prospectus” as a reliable channel of their participation evaluation in the IPO firm. One crucial piece of information is on the allocation amount of IPO proceeds as it should signal the directions of a firm in the aftermarket. This study proposes that an IPO firm would have a larger potential to grow if it allocates a bigger amount of proceeds to growth activities, which will encourage higher demand on and subscription of shares of the IPO firm. Eventually, the higher demand would lead to a higher share price of the firm and a higher return for investors in the aftermarket. Leveraging this proposition, a total sample of 436 IPOs listed on Bursa Malaysia from 2000 to 2017 were tested using the multiple regression analysis. This study reveals that the amount of proceeds allocated to growth activities are positively and significantly related to the return of IPOs in the initial aftermarket.
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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 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".