The Classical Approaches to Testing the Unconditional CAPM: UK Evidence
Bibliographic record
Abstract
This empirical study attempts to test the unconditional capital asset pricing model. Two-pass regression models are employed using 86 randomly chosen companies of LSE during 1997 to 2015. A two stage approaches have been applied to investigate whether excess returns can be explained by the market risk. Based on empirical results of the first pass regression, among the 86 companies 81 companies are consistent with the prediction of CAPM except five companies. However, the estimated R-square of the sample companies are very low and indicate that market excess return has low explanatory power. In the second pass regression, empirical result shows that beta coefficient is negative and statistically significant which implies that rate of return has no linear positive relationship with beta. Further, coefficient of residual variance is also observed negative and statistically significant which violates the CAPM assumption as unsystematic risks are assumed to have no impact on rate of return. In conclusion, CAPM predictions are not consistent with the findings of this study; hence CAPM is violated and does not hold.
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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 | 0.015 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".