Bibliographic record
Abstract
In this paper the authors extend results by Harvey and Zhou (1990, Journal of Financial Econometrics 26, 221–254) and Kandel, McCulloch, and Stambaugh (1995, Review of Financial Studies 8(1), 1–53) to derive the posterior distribution of a key parameter in a Bayesian analysis of asset pricing models. It is shown that this distribution depends upon the same terms that constitute the standard asset pricing test of Jobson and Korkie (1985, Canadian Journal of Administrative Science 12, 114–138). Contrary to the view held by other authors, we find straightforward expressions for the posterior distribution that can be calculated without resorting to Monte Carlo methods.
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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 | 0.038 | 0.143 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.005 | 0.017 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".