Mitigating the Influence of Analysts Who Issue Aggressive Stock Price Targets: The Role of Joint Versus Separate Evaluation*
Bibliographic record
Abstract
ABSTRACT Investors frequently rely on individual analysts' stock price targets. Aggressive price targets often reflect analysts' attempts to strategically influence investors. Therefore, investors' welfare may be compromised if they take aggressive price targets at face value. In this study, we examine conditions under which investors are more likely to infer that analysts who issue aggressive price targets are acting strategically. Investors can evaluate multiple analysts' price targets with or without other related information (e.g., earnings estimates). Investors can also evaluate the information provided by multiple analysts jointly or separately one analyst at a time. Two experiments find that as predicted, when investors evaluate multiple analysts' price targets without earnings estimates, there is no difference in investors' perceptions about whether the aggressive analyst is acting strategically across joint versus separate evaluation. However, also as predicted, when investors evaluate multiple analysts' price targets along with their earnings estimates, investors perceive the aggressive analyst as acting more strategically under joint evaluation than under separate evaluation. Our findings suggest that jointly evaluating multiple analysts' price targets with other related information, such as earnings estimates, can reduce the likelihood that investors would be overly influenced by aggressive analysts.
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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 | 0.012 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".