The Impact of Overseas M&A on Performance Sensitivity of CEO Turnover: Evidence From Chinese Listed Companies
Bibliographic record
Abstract
In recent years, with China’s growing economic strength driven by the reform of market economic system and the implementation of “going out” strategy, overseas M&A has become an important channel for many Chinese listed companies to optimize the resource allocation and realize the leap of global value chain. Overseas M&A not only garners the advanced technology from overseas companies, but also improves the corporate governance of the parent company. With the data of Chinese listed companies from 2003 to 2019, this paper empirically examines how overseas M&A of listed companies influences the performance sensitivity of CEO turnover, and the PSM method and placebo tests are adopted to discuss the endogeneity. Finally, we proceed the heterogeneity tests by the nature of property rights, CEO power and the market competitiveness of product, respectively. The empirical results show that overseas M&A of Chinese listed companies will significantly improve the performance sensitivity of CEO turnover, and the phenomenon is more significant in SOEs and companies with lower management shareholding ratio, older CEO age, longer tenure of CEO, and lower product market competition. The conclusion of this paper not only offers a unique theoretical perspective for China's listed companies to improve their governance efficiency through overseas M&A, but also provides policy references for improving China's overseas M&A behavior in practice.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".