Institutional Ownership and the Extent to which Stock Prices Reflect Future Earnings*
Bibliographic record
Abstract
Abstract Articles in the financial press suggest that institutional investors are overly focused on current profitability, which suggests that as institutional ownership increases, stock prices reflect less current period information that is predictive of future period earnings. On the other hand, institutional investors are often characterized in academic research as sophisticated investors and sophisticated investors should be better able to use current‐period information to predict future earnings compared with other owners. According to this characterization, as institutional ownership increases, stock prices should reflect more current‐period information that is predictive of future period earnings. Consistent with this latter view, we find that the extent to which stock prices lead earnings is positively related to the percentage of institutional ownership. This result holds after controlling for various factors that affect the relation between price and earnings. It also holds when we control for endogenous portfolio choices of institutions (e.g., institutional investors may be attracted to firms in richer information environments where stock prices tend to lead earnings). Further, a regression of stock returns on order backlog, conditional on the percentage of institutional ownership, indicates that institutional owners place more weight on order backlog compared with other owners. This result is consistent with institutional owners using non‐earnings information to predict future earnings. It also explains, in part, why prices lead earnings to a greater extent when there is a higher concentration of institutional owners.
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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 | 0.001 | 0.016 |
| 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) | 0.005 | 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".