Study of the Influence of Corporate Governance Level on Investors' Confidence
Bibliographic record
Abstract
Stock market investment has the Sheep-Flock Effect, so investors’ confidence relates to the stability and healthy development of the stock market. The functional mechanism of investors’ confidence is complicated with many influential factors. This paper selects the factor of corporate governance level to investigate and study the great effect of corporate governance level evaluation on maintaining and increasing investors’ confidence from the perspective of investors. In this paper, the method to measure investors’ confidence and corporate governance level is improved, and the data of A-share companies listed in Shanghai Stock Exchange of China in 2011-2013 is selected as the sample to analyze the panel data. The results show that, the higher the corporate governance level is, the stronger investors’ confidence is; investors’ confidence is also influenced by the macro level of the market and the nature of various industries is different, so significances of influences of corporate governance level in different industries on investors’ confidence are not the same. At the same time, the empirical results show that investors’ confidence has a positive lag effect.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".