Luck Versus Skill in the Cross-Section of Ethical Mutual Funds
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The risk-adjusted selection and timing performance (alphas and gammas) of a comprehensive and survivorship-free sample of Canadian equity SRI funds after (before) management-related costs is negative (positive) and is sensitive to the choice of the return-generating process. It is not statistically different from that of non-SRI funds. Examination of funds in the tails of the performance distribution using the block-bootstrap method suggests that “bad luck” causes the underperformance of extreme left-tail funds and almost no fund possesses truly superior management and timing skills. Resume Ce papier etudie la performance liee a la selection des titres et a la synchronisation des mouvements de marche (alphas et gammas) d’un echantillon de fonds mutuels socialement responsables (SRI) au Canada. Les resultats montrent que la performance ajustee au risque de ce fonds apres (avant) les couts de gestion est negative (positive) et est sensible au choix du processus generateur des rendements. Elle n’est pas statistiquement differente de celle des fonds non-SRI. Examen des fonds dans les queues de la distribution des performances en utilisant la methode des bootstrap en block suggere que la “malchance” explique la sous-performance extreme des fonds a gauche de la queue. Presque aucun fonds ne possede des qualites et competences vraiment superieures de selection des titres et de synchronisation des mouvements de marche. Les Cahiers du GERAD G–2011–07 1
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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) | 0.000 | 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