Exploiting Industry Momentum with Sector Funds: the Case of the European Market
Bibliographic record
Abstract
GREG N. GREGORIOU is Associate Professor of Finance and coordinator of faculty research in the School of Business and Economics at State University of New York (Plattsburgh). He obtained his PhD (Finance) from the University of Quebec at Montreal and is the hedge fund editor for the peer-reviewed journal Derivatives Use, Trading and Regulation published by Palgrave-MacMillan based in the U.K. He has authored over 50 articles on hedge funds, and managed futures in various U.S. and U.K. peer-reviewed publications, including the Journal of Portfolio Management, Journal of Futures Markets, European Journal of Finance, Journal of Asset Management, European Journal of Operational Research, and Annals of Operations Research. He has edited twelve books on hedge funds, funds of hedge funds, CTAs, IPOs, Corporate Governance and investment management.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".