Bibliographic record
Abstract
The current market for proxy advice arises out of an agency problem, but not the one usually assumed. Investment fund managers have relatively few economic incentives to invest effort on corporate governance and so they tend to organize around picking the best stocks and trading those stocks at the optimal time. This creates a market for third party proxy advisors, but both investment managers and proxy firms bear few of the costs of poor governance and operate under incentives to keep proxy advice as inexpensive as possible.Empirical evidence drawn from the academic studies performed on this market, along with trends revealed by submissions to the SEC and CSA, show significant problems with the content of proxy advice (including mistakes in what produces good corporate governance and frequent errors in voting recommendations) along with problems in the process by which the advice is delivered (including insufficient information for advisors to comply with their own voting guidelines, conflicts of interest, opacity, and an apparent inability to correct errors.)The case for regulatory intervention in the market for proxy advice can be stated quite simply: (1) there is empirical evidence of significant, repeated informational failures produced by the market for third party proxy voting advice; (2) there is evidence these failures arise systemically as a logical consequence of the conflicts of interest of the agents that make up the market; and (3) there is evidence of significant externalities in the market for proxy advice, suggesting the value of good proxy advice is not captured by the agents that participate in the market and that high-quality advice is therefore underproduced. This is precisely the type of market failure securities regulation is designed to fix. The paper concludes by recommending the modest application of traditional disclosure tools to the market for proxy advice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.021 | 0.011 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".