Practical Applications of Portfolio Tilts Using Views on Macroeconomic Regimes
Bibliographic record
Abstract
In <ext-link><bold><italic>Portfolio Tilts Using Views on Macroeconomic Regimes</italic></bold></ext-link>, from the February 2023 issue of <bold><italic>The Journal of Portfolio Management</italic></bold>, <bold>Redouane Elkamhi</bold>, of the <bold>University of Toronto</bold>, and <bold>Jacky S. H. Lee</bold> and <bold>Marco Salerno</bold>, both of the <bold>Healthcare of Ontario Pension Plan Trust Fund</bold>, develop and illustrate an approach for enhancing investment decisions by incorporating investor views on the likelihood of economic regimes. This contrasts with the literature incorporating investor views on specific asset returns and the covariances among returns on different assets. The authors assert that their approach outperforms others that require investors to have views on specific asset returns. Additionally, it is easier to apply because it is more likely that an investor will have views about the likelihood of different macroeconomic regimes than about the expected performance of many different assets.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.004 |
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".