Bibliographic record
Abstract
Asset pricing models are traditionally evaluated on a set of diversified size/Bookto-Market portfolios. However, the recent literature shows that this approach may have only limited ability to distinguish between the wide variety of models that researchers can choose from. To address this issue, this paper introduces a new cross-section of test assets dubbed micro portfolios. Containing only a few stocks, these portfolios help preserve dispersion of betas across the risk factors that drive returns. This information, which is obscured in diversified portfolios, is critical to improving the evaluation of competing models. The empirical evidence sheds new light on the strengths and weaknesses of the conditional, human capital, and liquidity-based CAPMs and shows that they outperform the standard CAPM along several dimensions that would be largely unnoticed otherwise. ∗Desautels Faculty of Management, McGill University, Montreal. E-mail: laurent.barras@mcgill.ca. Phone:+15143988862. I thank Sebastien Betermier, Julien Cujean, Alexandre Jeanneret, Aytek Malkhozov, Olivier Scaillet, Sergei Sarkissian, Akiko Watanabe, Russ Wermers, as well as seminar participants at the 2012 CIRPEE Applied Financial Time Series Conference, the 2012 meeting of the Institute for Mathematical Finance, the 2012 meeting of the French Finance Association, the 2013 meeting of the Northern Finance Association, the Universities of Geneva, Laval, and Queen’s for their comments. I also thank the Social Sciences and Humanities Research Council of Canada (SSHRC) and the Institute for Mathematical Finance (IFM2) for their financial support.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".