Relative Performance Contracts and Fund Managers’ choice of Investment Strategies
Bibliographic record
Abstract
This paper has constructed a programming model used to solve the optimal relative performance contracts. Under the contract that the constraints of the model can be satisfied, the strategy combination that the investors wish to see must form a Nash equilibrium in the managers’ subgame. It is further indicated by the numerical example that more often than not this equilibrium constitutes a dominant strategic equilibrium as long as the managers do not collaborate with each other. Key words: Relative Performance, Compensation Fee, Fund Manager, Investment Strategies Resume: Ce texte a concu un modele programme pour resoudre les contrats de performance relative . Sous le contrat que les contraites de modele peuvent etre satisfaites , la combinaison strategique que souhaitent les investisseurs doit former un equilibre Nash dans les sub-jeux des directeurs . L’exemple numerique montre davantage que cet equilibre constitue l’equilibre de strategie dominante , pourvu que les directeurs ne se collaborent pas . Mots-cles: performance relative, renumeration compensatoire, directeur de fonds, strategie d’investissement
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".