The relationship between IPOs and long-term industry performance: Evidence from Tehran Stock Exchange
Bibliographic record
Abstract
Every year, the companies with favorable perspective are entered into stock exchange through Initial public offering (IPO). This issue is a treat for share of competitors in market and usually, IPO is offered while industry is in top of its valuation, which could be corrected after offering price. In this regard, we study the relationship between IPO and long-term performance of industry, identification and explaining the effective factors. In addition, the relationship of IPS has compared with portfolios of corresponding competitors. In this study, we conclude that the portfolios of competitors had an undesirable performance three years after initial offering. Six effective variables are verified on performance including industry concentration, industry valuation and homogeneity, size of offering, industry operating leverage and industry financial leverage. Undesirable effects of industry is specified when the industry valuation is rather high, industry is in top of valuation or concentration is high in industry, also, when the homogeneity of industry is more, undesirable effect is less.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".