The impact of the classified voting system on corporate investment and equity value
Bibliographic record
Abstract
Abstract Granting decision rights to minority shareholders protects them from expropriation by controlling shareholders, but it simultaneously fosters a mismatch between decision rights and decision‐relevant information. Using the setting of China's classified voting system (CVS), which requires minority shareholder approval for managerial proposals, this study investigates the effect of such a regulation on investment responsiveness to profitability and equity value attributable to growth options. Following the real‐options‐based valuation model, we document that the adoption of CVS diminishes both investment responsiveness and equity value. This reduction is attributed to heightened financial constraints following the CVS implementation. Further analyses show the negative impacts are more pronounced for firms experiencing greater information asymmetry, lower mutual fund holdings, and severe agency conflicts. Our evidence indicates that the efficacy of the regulation is contingent on the alignment between decision rights of minority shareholders and decision‐relevant information available to them. Our findings thus provide insights to the regulators regarding the advantages and disadvantages of allowing minority shareholders direct influence over corporate decision‐making.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".