Delegating Benchmarks: Aligning Incentives for Better Total Fund Performance
Bibliographic record
Abstract
Benchmarks are essential to institutional investing, but their dual role—as both strategic references and measures of execution—can create structural tensions. When applied at the asset-class level, benchmarks may inadvertently encourage behaviors such as index tracking, beta tilts, or localized outperformance that do not always align with total fund objectives. This article explores an alternative governance model in which the board anchors the total fund benchmark, while the investment executive (IE)—the CEO, CIO, and their team—designs mandate-level benchmarks. A stylized, game-theoretic model shows that delegation reduces benchmark gaming, supports the generation of uncorrelated alpha, and enables more efficient capital allocation. The expected outcome is stronger risk-adjusted returns, more diversified sources of alpha, and improved retention of top investment talent. With appropriate guardrails and oversight, delegated benchmarking restores benchmarks to their intended role: providing clear accountability for execution in service of long-term total fund success.
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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 | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".