Benchmarking Performance Measures With Perfect-Foresight AssetAllocation Strategies
Bibliographic record
Abstract
Waterloo. The comments of Reo Audette, Mark Kamstra, and other participants are greatly appreciated. I thank the Social Sciences Research Council of Canada for financial support, as well as Chris Fong and, especially, Poh Chung Fong for most capable research assistance. Benchmarking Performance Measures With Perfect-Foresight Asset-Allocation Strategies Popular measures of investment performance do not agree on the relative performance of passive portfolios, mutual funds, or even the seemingly obvious relative abnormal performance of assetallocation strategies generated from portfolio selection models. Moreover, the measures suffer from a number of conceptual and empirical shortcomings. Therefore, in order to better appreciate the ability of the performance measures to detect abnormal returns, this paper investigates whether they correctly recognize the truly amazing abnormal performance of perfectforesight asset-allocation strategies. Unfortunately, although each of the measures recognizes abnormal performance, some of them do not rank the strategies correctly, and others confound Many studies benchmark performance measures using passive portfolios, as passive
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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 | 0.021 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".