The Emergence of Concentrated Ownership and the Rebalancing of Portfolios due to Shareholder Activism in a Financial Market Equilibrium
Bibliographic record
Abstract
Consider a financial market equilibrium with correlated firms and risk averse investors holding diversified portfolios. When an activist investor has the ability to perform value-enhancing activities in a single firm, and these activities increase with ownership, we show that optimizing behavior by all investors leads to a concentration of shares in the hands of this activist. This concentration arises in the presence of complete information and is a consequence of Walrasian equilibrium mechanisms that include all investors and give no special powers to any of them in the equilibrium process. By yielding more ownership to the activist, all investors alter the risk profiles of their holdings, ending with less balanced portfolios. This rebalancing effect is accompanied by an increase in the price of the security that the activist can affect, as well as in total value of the market. When the activist can affect more than one firm, rebalancing of all portfolios again occurs. Although the activist may not acquire increased concentration in all the firms she might affect, prices change for all those firms, and we give conditions under which at least one price must increase. We find that equilibrium results in a sharing of the costs and benefits of activism among all market participants, mitigating the free-rider problem. When we study multiple activists in many firms, we show that concentration can occur for several activists, and rebalancing occurs for all investors. Predictions on investor-specific concentration are difficult and excessive portfolio churning is present. The introduction of asymmetric information concerning activism again results in rebalancing and in concentration of ownership, but not necessarily in the hands of the activist.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".