The correlation between corporate governance and market value: regime or signal?
Bibliographic record
Abstract
Purpose – As literatures argue that managers’ personalities will affect both corporate governance structures and corporate performance, the correlation between them is a mixed result. The purpose of this paper is to separate different routes leading to the mixed correlation, and name the separated routes as regime effect and signal effect. Design/methodology/approach – By theoretical analysis, the authors list three routes leading to the correlation between corporate governance and corporate performance. Routes 1 and 2 show that governance can directly and indirectly change the performance; while route 3 shows that both the governance and performance are results of managers’ personalities, and the governance has no influence onto the performance, which means the correlation led by route 3 is fake. By design a new econometric methodology, this paper separates the mixed correlation between corporate governance and performance, and names the correlation led by routes 1 and 2 as the regime effect and the correlation led by route 3 as signal effect. Findings – By an empirical research on Chinese listed corporates, the authors find that the correlations between Chinese listed corporates’ market value and main corporate governance factors can be separated into regime effects and signal effects; and the authors also find that some factors (Share of Institutional Investors, Share of Real Controller and the Squared, Dummy of Identical CEO and Chairman, Ownership Concentration) only show regime effects, some factors (Separating Extent of Ownership and Controlling Right, Dummy of Provincial State-Owned Firms) only show signal effects, and some factors (Dummy of Republic State-Owned Firms, Scale of Board) show both. What’s more, the authors find out an interesting result that the state-owning has no negative regime effect on China SOEs’ performance but very significantly negative signal effect; in this paper, the authors suggest that this means the key negative factors of Chinese SOEs is not state-owning ownership structure but the managers’ corruption. Practical implications – As only the factors with regime effects can directly and indirectly affect corporates’ performance and the factors with signal effects show that there’re some managers’ personalities affecting both the governance and performance, the separation method in this paper can help shareholders knowing which governance factors will be helpful to improve the performance and which others will show managers’ hard-working or corruption intention. Originality/value – Separate the regime effect and the signal effect from the correlation between corporate governance and performance.
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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 | 0.006 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".