Portfolio Selection Subject to Vague Experts' Judgments
Bibliographic record
Abstract
This paper is written with two purposes in mind. First, it brings together some recent results in the area of mean-variance theory model validation for fuzzy systems in the existence of subjective measures suggested by experts. The central idea of the methods presented here is to map random uncertainty given a portfolio-selection model into fuzzy random uncertainty description, which is useful from an application and analysis point of view. The main contributions of the paper are (i) to explore the implications of fuzzy return indeterminacy on mean-variance optimal portfolio choice and (ii) to use bid-ask spread as a proxy measure of the indeterminacy or fuzzy nature of random returns. Second, this paper also presents a brief self-contained glimpse of empirical representations to practitioners unfamiliar with the field of fuzzy modeling. It is hoped that expositions such as this one will open new collaborations between other branches of fuzzy mathematics and asset-pricing theories.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".