Research and Analysis on Market Value Management in China Based on Method of Rank-Sum Ratio and Principal Component Analysis
Why this work is in the frame
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Bibliographic record
Abstract
<p>Since 2005, China has implemented the split-share reform. After entering the full-circulation era of stock equity, the pursuit for maximize the company value has turned into the primary goal of listed companies in the course of their management and development. Thus, they attach great importance to the concept of market value management. The management of stockholders in listed companies began to pay attention to the inner values and the performances in the stock market of their enterprises, and thereby the concept of market value management is established. However, the weak efficiency of China’s capital market has resulted in the deviation between market values and inner values of companies. Thus, companies need to implement market value management and devise corresponding solutions so that two kinds of values can be well-matched.</p>This paper presents the definition of market value management at first. Next, it studies the background of the emergence of market value management as well as its development status in China, which are also compared with the overseas value management. And then, it makes a literature review and analyzes Economic Value Added Evaluation System (EVA), a performance evaluation system of market value management. It adopts the method of Rank Sum Ratio (RSR)and Principal Component Analysis to make empirical analyses,which evaluates the level of market value management of listed companies in China and discovers the weak links existing in the process of market value management .This paper eventually puts forward corresponding countermeasures and suggestions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 it