Analysts’ Institutional Client Catering and Reputation Tradeoff: Strategic Timing of Recommendations
Bibliographic record
Abstract
We examine whether sell-side analysts strategically time their favorable recommendations to cater to institutional investors while preserving analysts’ reputational capital. Although prior literature documents that analysts provide more positive recommendations for stocks that are part of their institutional clients’ (specifically, mutual funds’) portfolios, it does not explicitly address a reputation cost associated with such practice. Using a sample of analysts’ recommendations on U.S. firms for the 2002–2017 period, we document a pattern of analysts’ recommendations being more optimistic in the end month of a quarter and less optimistic in the beginning month of a quarter. This timing pattern ties to quarterly reporting periods of portfolio managers, with actively managed mutual funds’ holdings being affected the most. Analysts with Institutional Investor All-Star ranking do not engage in such stock recommendation timing practices. The market participants seem to believe rosy recommendations issued for stocks with more institutional holdings in the end month of a quarter with more positive cumulative abnormal returns to upgrade and downgrade recommendations.JEL classifications: G14, G24
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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 | 0.062 |
| 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 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".