Bibliographic record
Abstract
Abstract Although many studies show that the presence of institutional investors facilitates the incorporation of accounting information into financial markets, the evidence of informed trading by institutions is rather limited in the extant literature. We address these inconsistent findings by proposing PC _ NII , percentage changes in the number of a stock's institutional investors, as a novel informed trading measure. PC _ NII is better able to detect informed trading than are changes in institutional ownership ( Δ IO )—the measure commonly used in previous studies—because (i) entries and exits are usually triggered by substantive private information and (ii) only a small fraction of institutions have superior information. As conjectured, PC _ NII subsumes the information content of Δ IO and other institutional trading and herding measures in the forecast of stock returns, and its strong predictive power for stock returns reflects mainly its close correlation with future earnings surprises. We also show that PC _ NII helps address empirical issues that require a reliable measure of institutional informed trading.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".