Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the impact of heterogeneous expectations on the equilibrium value of a risky asset in a capital market populated by investors that choose mean‐variance efficient portfolios. Design/methodology/approach A single‐period, discrete‐time version of Williams' capital asset pricing model that incorporates heterogeneous beliefs regarding the mean vector of rates of return and homogeneous beliefs regarding the variance‐covariance matrix of rates of return is developed. It is then employed to gauge the impact of both divergence of opinion and increases thereof on the equilibrium price of a risky asset. Findings The value of a risky asset under heterogeneous beliefs differs from that under homogeneous beliefs as the former is biased towards the beliefs of wealthier and/or more risk tolerant investors. If the latter set of investors is optimistic (pessimistic), the value is higher (lower) than that which prevails in the absence of divergence of beliefs. Increasing divergence of opinion likewise affects the equilibrium price of a risky asset to accord more with the beliefs of wealthier and/or more risk tolerant investors. If the latter set of investors is optimistic (pessimistic), increasing dispersion of beliefs causes the value of a risky asset to rise (fall). Originality/value A novel simplification and application of Williams' model of capital asset pricing is presented. The findings differ from conclusions derived in previous theoretical treatments of divergence of opinions in capital markets.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".