The Implications of VaR and Short-Selling Restrictions on the Portfolio Manager Performance
Bibliographic record
Abstract
The ability of a portfolio manager to deliver higher returns with relatively low risk is a fundamental issue in finance. We analyze here the performance of a portfolio manager under two different types of constraints. For a manager with private information, we compare the effect of value at risk (VaR) and short-selling constraints on the relation between the expected portfolio return and the market return. We find that in more volatile market, the VaR restriction will have a stronger effect on the manager performance compared to the short-selling restriction effect. The VaR constraint also strongly affects a manager with good quality of information while the short-selling restriction moderately affects manager with any level of information quality. For the manager attitude toward the risk, a too aggressive manager will find his overall performance more affected by the VaR constraint. Therefore, financial institutions such as large investment banks and hedge-funds with a strong ability to obtain superior information could be more affected by a very strong VaR restriction than by a short-selling restriction.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".