Do Earnings Estimates Add Value to Sell-Side Analysts’ Investment Recommendations?
Bibliographic record
Abstract
Sell-side analysts change their stock recommendations when their valuations differ from the market’s. These valuation differences can arise from either differences in earnings estimates or the nonearnings components of valuation methodologies. We find that recommendation changes motivated by earnings estimate revisions have a greater initial price reaction than the same recommendation changes without earnings estimate revisions: about +1.3% (−2.8%) greater for upgrades (downgrades). Nevertheless, the postrecommendation drift is also greater, suggesting that investors underreact to earnings-based recommendation changes. Implemented as a trading strategy, earnings-based recommendation changes earn risk-adjusted returns of 3% per month, considerably more than non-earnings-based recommendation changes. Evidence from variation in firms’ information environment and analysts’ regulatory environment suggests that recommendation changes with earnings estimate revisions are less affected by analysts’ cognitive and incentive biases. This paper was accepted by Wei Jiang, finance.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".