Dynamic relationships and technological innovation in hot and cold issue markets
Bibliographic record
Abstract
Purpose The puzzle of hot and cold issue markets has attracted substantial interest in the academic community. The behavior of IPO volume and initial returns over time is well documented. Few studies, however, investigate the dynamic interrelationship between these two variables. This paper aims to fill this gap. In addition, the technological innovations hypothesis of hot issue markets is tested. Welch and Hoffmann‐Burchardi suggest that the clustering of new issues is caused by IPO volume spikes in industries that have recently experienced technological innovations or favorable productivity shocks. Design/methodology/approach This paper employs a sample of 8,160 initial public offerings filed in the USA between January 1972 and December 2001. A simultaneous equation approach is used to examine the endogenous relationship between IPO volume and initial returns. In addition, the paper analyzes the industry correlation matrix of new issue activity and estimates a fixed‐effects model based on industry‐level data to examine the impact of technological innovations on new issue activity. Findings It is found that higher IPO volume causes higher initial returns, but not vice versa. In addition, evidence is found against the technological innovations hypothesis. The findings suggest that economy‐wide rather than industry‐specific factors are responsible for the observed variations in IPO volume. Research limitations/implications As with any empirical study, the results may be sample‐specific. Originality/value The paper extends the prior literature on the relationship between IPO volume and initial returns by applying two‐stage and three‐stage least squares models that go beyond prior methodological approaches used in the extant literature. In addition, the paper provides some of the first empirical evidence on the effect of technological innovations and productivity shocks on IPO activity.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".