Long-term institutional shareholdings and stock price informativeness of analyst target prices
Bibliographic record
Abstract
Purpose This study examines whether long-term institutional shareholdings affect the informativeness of analyst target price revisions. Design/methodology/approach Using institutional investors’ portfolio holdings and a sample of 53,988 target prices from 2000 to 2019, we relate cumulative abnormal returns (CARs) to long-term institutional shareholdings to investigate whether long-term investment horizon influences the stock market response to analyst target price revisions. We identify long-term institutional shareholdings based on their quarterly portfolio churn ratios. Findings We find that firms with more long-term institutional shareholdings experience positive returns on target price revisions. This positive response is pronounced for (1) firms with more long-term motivated institutional shareholdings with higher incentives to monitor, (2) firms with higher idiosyncratic volatility and (3) firms with a higher probability of informed trading. Investors find more value in target price revisions when issued by sophisticated analysts, i.e. with higher experience, higher earnings accuracy and a bigger brokerage size. Specifically, we report that target price revisions provide more incremental information to investors than earnings forecasts and stock recommendation revisions. Our findings remain robust to several alternative specifications, addressing the endogeneity issues. Originality/value Our study contributes to the extant literature on the informativeness of analyst target prices while exploring how the investment horizon influences investors’ reliance on firm-level information.
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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 | 0.002 | 0.019 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".