Robust Portfolio Allocation with Systematic Risk Contribution Restrictions
Bibliographic record
Abstract
The standard mean-variance approach can imply extreme weights in some assets in the optimal allocation and a lack of stability of this allocation over time. To improve the robustness of the portfolio allocation, but also to better control for the portfolio turnover and the sensitivity of the portfolio to systematic risk, it is proposed in this paper to introduce additional constraints on both the total systematic risk contribution of the portfolio and its turnover. Our paper extends the existing literature on risk parity in three directions: i) we consider other risk criteria than the variance, such as the Value-at-Risk (VaR), or the Expected Shortfall; ii) we manage separately the systematic and idiosyncratic components of the portfolio risk; iii) we introduce a set of portfolio management approaches which control for the degree of market neutrality of the portfolio, for the strength of the constraint on systematic risk contribution and for the turnover
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".