Explaining the Failure of the Unconditional CAPM with the Conditional CAPM
Bibliographic record
Abstract
When the cost of hedging is nil, the conditional capital asset pricing model (CAPM) holds. We empirically test the conditional CAPM by regressing asset returns onto the product of their conditional betas and market returns. Estimated intercepts are not statistically different from zero, implying that the conditional CAPM successfully explains the conditional level of asset returns. Yet, unconditional betas do not explain the cross section of average asset returns; the unconditional CAPM fails. We show why and how the success of the conditional CAPM actually explains the failure of the unconditional CAPM, thereby rationalizing the coexistence of these two intriguing results. This paper was accepted by Gustavo Manso, finance. Funding: The University of Texas at Dallas and the University of Toronto provided financial support. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4381 .
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".