Static and dynamic impacts of venture capital shareholding on stock price crash risk
Bibliographic record
Abstract
With the rapid development of venture capital industry in China, venture capital firms are playing an increasingly active role in funding startups. As more and more companies with venture capital shareholders go public, venture capital firms become an important group of institutional investors that can bring significant influence on companies? stock price. In the past decade, extremely high exit returns from IPO of venture capital firms raised the public and Chinese scholars? attention. However, few researches inspected the venture capital?s post-IPO influence. In this paper, we examine whether venture capital shareholding will lead to companies? higher stock crash risk, and whether different venture capital shareholders? different behavior of holding and selling after lockup period will have different impacts on companies? crash risk. The research used descriptive statistics and regression analysis to analyze the quarterly data of Chinese A-share companies from 2005-2016. We found that: (1) Companies with venture capital shareholders in a specific quarter will have greater incentive of earnings management (exaggerating revenue and profit) and greater stock price crash risk in the next quarter then companies without venture capital shareholders. (2) Venture capital shareholders continuing to hold non-restricted shares after lockup period is considered as a positive signal, thus reduce the crash risk. Venture capital shareholders selling shares is considered as a negative signal, thus increase the crash risk.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".