Institutional Investors’ Distraction and Executive Compensation Stickiness Based on Multiple Regression Analysis
Bibliographic record
Abstract
Based on the impact of industry extreme return on the attention of institutional investors, taking Chinese A-share listed companies from 2011 to 2020 as a sample, this paper empirically tests the relationship between institutional investors’ distraction and executive compensation stickiness based on multiple regression analysis. The study finds that institutional investors’ distraction promotes the executive compensation stickiness, which is more significant in the group of pressure-resistant institutional investors. The mechanism test finds that based on the governance effect, information effect and psychological effect, corporate external governance, stock price information content and management anxiety play a partial intermediary role between institutional investors’ distraction and executive compensation stickiness. The moderating effect finds that the level of corporate internal governance and managerial overconfidence will weaken the impact of institutional investors’ distraction on executive compensation stickiness. In addition, the distraction behavior in non-state-owned and western companies has a more significant economic impact.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".