Underpricing, share retention, and the IPO aftermarket liquidity
Bibliographic record
Abstract
Purpose To test the effects of underpricing and share retention (i.e. the proportion of shares retained by the pre‐initial‐public‐offering (IPO) owners) on IPO aftermarket liquidity. Design/methodology/approach Uses both percentage spread and turnover ratio to measure liquidity. The percentage spread is the quoted bid‐ask spread divided by the quoted midpoint and measures the trading cost relative to share price. Turnover ratio is the daily trading volume divided by the number of shares offered and measures the speed of transaction. Both non‐parametric analyses and multiple regressions are conducted to investigate the effects of underpricing and share retention on liquidity. Findings Results indicate that initial return is positively related to turnover ratio and negatively related to percentage spread. These relations are significant even after controlling for other factors. Also finds that the pre‐IPO owners’ retention rate is positively related to turnover ratio and negatively related to percentage spread. High retention rates attract more trades, provide quality assurance, and improve IPO aftermarket liquidity. Originality/value This paper investigates the theoretical links between underpricing and liquidity and provides direct evidence on Booth and Chua's liquidity theory. In addition, this is one of the first empirical studies to analyze the effect of share retention on aftermarket liquidity.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".