Are Prophets Myopic? Analyst Recommendations and the Implied Cost of Equity *
Bibliographic record
Abstract
Analysts often simultaneously provide stock recommendations and forecasts of future earnings of the firms they follow, with their stock recommendations depending heavily on their expectations of the firm’s future earnings. However, they typically do not provide estimates of the other com-ponent of estimated stock value, the firm’s cost of equity capital. Using the cost of equity implied by current price and earnings forecasts, we show that analyst recommendations do not account fully for the information contained in the firm’s implied cost of equity. Moreover, we document that past upgrade (downgrade) recommendations are associated with an increase (decrease) in the current cost of equity; and past decreases (increases) in the cost of equity are associated with cur-rent upgrade (downgrades) recommendations. In addition, we find that changes in implied cost of equity and changes in analyst recommendations jointly explain about 26 % of the variation in one-year holding period returns, of which surprisingly most of it, about 23%, is explained by the im-plied cost of equity alone. There is strong evidence that, when forming their recommendations, on average analysts underestimate the role of the cost of equity, and its important long-term effects on pricing.
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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 | 0.005 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".