Unconstrained strategies and the variance-kurtosis trade-off
Why this work is in the frame
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Bibliographic record
Abstract
In this article, we study unconstrained strategies through a respecification of classic mean-variance utility and, as a reference implementation, a long-only strategy based on Canadian and US bond markets. First, we capture the underlying economic forces that drive benchmark indices in the two economies as orthogonal components of yields. We find that bond indices in the two markets are sensitive to components that account for lesser total yield variability. Next, we develop a new polynomial utility function that captures the kurtosis effects found in the sensitivities to lower-eigenvector components. In our unconstrained strategy, excess kurtosis triggers portfolio adjustments and the resulting returns outperform those of traditional mean-variance optimization. The respecified utility function introduces iso-risk contour lines that account for abrupt adjustments of portfolios to eigenvectors of hidden influence.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it