Study on the Influence of PE Shareholding on the Profit of GEM Listed Enterprises
Bibliographic record
Abstract
This paper takes 91 companies listed on the GEM from 2014 to 30 April 2015 as the research object. The financial data of the firms as of 31 December 2022 are obtained from the Rexis database to analyze whether PEs play the role of certification and monitoring and whether there are adverse selection and grandstanding hypotheses in China's GEM market. Through empirical analysis, it is concluded that there is no significant difference in earnings between listed companies with and without PE background, that is, there is no adverse selection, certification effect and monitoring effect in the GEM market. Then, this paper extracts PE reputation and PE holding time as PE characteristics, studies the impact of these two on corporate profitability, and concludes that ROE is higher for firms supported by PE with good reputations. PE with long investment periods and high participation in corporate governance have higher ROE of invested enterprises.
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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 | 0.004 | 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 | 0.000 |
| 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".