Research and Analysis on Market Value Management in China Based on Method of Rank-Sum Ratio and Principal Component Analysis
Bibliographic record
Abstract
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. 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".