Large Shareholder Portfolio Diversification and Voluntary Disclosure*
Bibliographic record
Abstract
ABSTRACT Although large shareholders have sufficient influence to engage privately with management, extant literature provides inconclusive evidence on the relation between large equity positions and corporate disclosure. This study examines whether large shareholders' portfolio diversification affects voluntary corporate disclosure. We define diversification as the extent to which investors spread investments among portfolio stocks. We predict that holding a diversified portfolio deters large shareholders from incurring the costs of private information gathering about a portfolio firm. We document that firms provide more voluntary disclosure when their large shareholders hold a more diversified portfolio, consistent with investors relying more on public disclosure about portfolio firms when their portfolio diversification is higher. Evidence from cross‐sectional analyses suggests that, as predicted, the positive relation between portfolio diversification and voluntary disclosure is weaker as the net benefit of acquiring private information increases for large shareholders (i.e., when portfolio firms are more connected, have alternative information channels, or are more complex). Overall, our results suggest that diversified large shareholders' preference for a richer public disclosure environment creates a positive externality of lowering the information costs for external stakeholders without private access to management.
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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 | 0.025 |
| 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".