The Panorama of CEO Turnover: An Empirical Study on Chinese Listed Companies/PANORAMA DU ROULEMENT DE CEO : UNE ÉTUDE EMPIRIQUE DES SOCIÉTÉS COTÉES CHINOISES
Bibliographic record
Abstract
Abstract: In this paper, we present a panorama of the Chinese listed companies' CEO turnover. We can see that from 2001 to 2004, the top three industries about the number of CEO turnover are Manufacturing, Mixed and Telecommunications industries; the top three industries about relative turnover rate are Media and culture, Telecommunications and Mixed industries; job re-arrangement, resignation and expiry of tenure are three mainly publish reasons; the difference between inside and outside succession is not significant. A deep analysis showed that age, education and tenure will influence CEO turnover too. Key words: Chinese listed company, CEO turnover, inside succession, outside succession Resume: Dans le present article, nous presentons un panorama du roulement de CEO des societes cotees chinoises. Nous constatons que, de 2001 a 2004, les trois top industries sur le nombre du roulement de CEO sont l'industrie de fabrication, l'industrie mixte et les telecommunications. Les trois top industries sur le taux de roulement relatif sont les medias et culture, les telecommunications et l'industrie mixte. Le changement de travail, la demission et l'expiration du mandat sont les trois raisons essentielles. La difference entre la succession interne et la succession externe n'est pas importante. Une analyse profonde indique que l'âge, l'education et la duree de mandat peuvent aussi influencer le roulement de CEO. Mots-Cles: societe cotee chinoise, roulement de CEO, succession interne, succession externe 1. INTRODUCTION CEO turnover is one of the most important decisions for the development of one corporation, and such issue is widely researched by accounting and finance researchers overseas. CEO turnover can influence a lot of aspects of the corporation, Inside of the organization, it is the most influential management change. Outside of the organization, it is a signal of the development of the corporation for stakeholders, such as stokeholders, customers, suppliers, public and government. As a result, CEO turnover can influence economic, political environment and corporation performance. The structure of the paper is as follows. First, related background in the area of corporate CEO turnover is discussed. Next the data and the applied methodology are presented, followed by the empirical results of CEO turnovers. Finally, the last section concludes. 2. METHODOLOGY We constructed a data set by gathering information of CEO turnovers from announcements submitted to the Shanghai and Shenzhen Stock Exchange and record in CSMAR System (www.gtarsc.com) during the period from 2001 to 2004. The total sample consists of CEO turnovers from firms that meet the following three criteria: (1) the firm is listed on the Shanghai and Shenzhen Stock Exchange; (2) an announcement of resignation is submitted to the Shanghai and Shenzhen Stock Exchange that identifies the reason and the date of the actual turnover; (3) the manager is the CEO. 3. EMPIRICAL RESULTS 3.1 Industrial distribution of CEO turnover Table 1 provides the statistical results for the numbers of CEO turnovers, and the percentages of industrial CEO turnover on all CEO turnover. The results show that the top three industries about the number of CEO turnover are Manufacturing, Mixed and Telecommunications industries, the CEO turnover rate is 56.66 percent, 9.38 percent and 7.33 percent, respectively. But we cannot suggest that the highest industry about CEO turnover rate is Manufacturing. Because the number of Manufacturing is very large. For example, the percentage of the number of Manufacturing companies on all Chinese listed companies is 57.32 percent in 2004. 1. Average number of CEO turnover means the average number of CEO turnover per year during the period 2001 to 2004; 2. The number of listed companies in 2004 means the number of listed companies in each industry in 2004; 3. …
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".