Effects of IPO Offer Price Ranges on Initial Subscription, Initial Turnover and Ownership Structure—Evidence from Indian IPO Market
Bibliographic record
Abstract
In this paper, we establish the significance and effects of initial public offer (IPO) offer price ranges on subscription, initial trading, and post-IPO ownership structures. The primary market in India provides a unique setting for estimating the effect of various initial public offer (IPO) price ranges and IPO issue factors on the initial demand for an IPO among investors, measured by full IPO subscription/oversubscription, initial turnover (liquidity), and the post-IPO listing ownership structure among investors (ownership). For the IPO pre-listing stage, this study uses firth logistic regression to estimate the effect of various IPO offer price ranges (low to high) and various IPO issue factors on the full subscription/oversubscription of an IPO in each investor category. For the post-IPO listing stage, the study uses OLS regression to estimate the effect of various IPO offer price ranges (low to high) and various IPO issue factors on the initial trading ratio (IPO listing day trading) and the ownership percentage between institutional and individual investors. We find that all investor categories show a lesser likelihood for full subscription or oversubscription of an IPO issue at the lowest range of IPO offer prices. At the post-listing stage, the results indicate a diverse IPO offer price range in which individuals and institutions maximize their respective ownership holdings after the IPO listing. The results further show that lower promoter holdings diffuse higher ownership among individual shareholders by targeting lower IPO offer prices, thus increasing control.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".