Bibliographic record
Abstract
This paper uses completely new data to study the variations in beta when it deviates from the constancy assumption presumed by the market model. The concentration of the literature on beta is based on post 1926 data. This makes the 19 th century Brussels Stock Exchange (BSE) data a very good out-sample dataset to test beta variations. Various models proposed in the literature to capture the variations in beta were studied. Blume’s correlation techniques reveal that beta is not stable at the individual stocks level and that the stability can be fairly improved by portfolio formations. Using root mean square error (RMSE) criterion, it was shown that the market model betas are weak in predicting future betas. The predictability can be improved by adjusting betas with the Blume and Vasicek mean reversion techniques. Further results from this study reveal that few stocks have lead or lag relationship with the market index. Small sized stocks were detected to be more prone to outliers. In effect betas in the 19 th century exhibits similar pattern as betas in the post 1926 era.
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How this classification was reachedexpand
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 | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".