Une méthode asymptotique pour tester la validité du modèle d’équilibre d’actifs financiers (MEDAF) avec pour exemple la bourse de Paris
Bibliographic record
Abstract
One of the problems in testing the validity of the two-parameter CAPM is the determination of an efficient proxy market portfolio to represent the true market portfolio. We test the mean-variance efficiency of a pre-specified market portfolio by using a method proposed by Roll (1976) for testing the linear relation between the rate of return of an asset and its beta, and hence the mean-variance efficiency of a proxy market portfolio. This procedure exploits the asymptotic exact linearity condition of the rate of return and beta by measuring the rate of decrease of cross-sectional residual variance with respect to increasing time-series sample size. The technique is applied to samples of companies on the Paris Stock Exchange for the period 1969-1978: 144 companies and twenty-nine different time series. The results indicate that although the sum of the squared residuals of a CAPM-type regression declines as the number of time observations increases, the sum of the squared residuals does not approach zero as the temporal sample size increases, as would be required for the market proxy of our pre-specified sample to be efficient.
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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 | 0.025 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".