Common Institutional Ownership and Earnings Management*
Bibliographic record
Abstract
ABSTRACT This study examines the relation between earnings management and block ownership of same‐industry peer firms by a common set of institutional investors (common institutional ownership). This relation is important given the tremendous growth of common institutional ownership and the significant influence of blockholders on financial reporting. We hypothesize that common institutional ownership mitigates earnings management by enhancing institutions' monitoring efficiency and by encouraging institutions to internalize the negative externality of a firm's earnings management on peer firms' investments. Consistent with our hypothesis, we find that higher common institutional ownership is related to less earnings management. Analyses of a quasi‐natural experiment based on financial institution mergers show that this negative relation is unlikely to be driven by the endogeneity of common institutional ownership. Cross‐sectional tests provide evidence that the negative relation is stronger among firms for which common institutional ownership is likely to generate a greater reduction in institutions' information acquisition and processing costs, and among firms whose severe financial misstatements are more likely to distort co‐owned peer firms' investments, supporting both mechanisms underlying our hypothesis. Our findings inform the ongoing debate on the costs and benefits of common institutional ownership by highlighting an important benefit: the enhanced monitoring of financial reporting.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".