Exit disruption and matching in venture capital markets: Evidence based on IPO suspensions in China
Bibliographic record
Abstract
This study investigates the impact of IPO suspensions on the matching relationship between venture capital firms (VCs) and startups in China’s venture capital market. We find that IPO suspensions significantly improve the degree of matching between VCs and startups. This effect is particularly pronounced for lower-quality VCs and startups with limited growth potential. Moreover, the positive impact of IPO suspensions on matching is stronger when VCs are domestic or state-backed, when VCs and startups are located in the same region, or when startups are in the middle or late stages of development. Our analysis reveals that IPO suspensions reduce VCs’ risk-taking behavior and facilitate information exchange in the venture capital market, thereby enhancing the matching process. These findings provide novel evidence on the role of government policy interventions, such as IPO suspensions, in shaping the investment and financing activities of VCs and startups in China’s venture capital market.
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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 | 0.000 | 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 | 0.000 | 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".