How Are Institutions Informed? Proactive Trading, Information Flows, and Stock Selection Strategies*
Bibliographic record
Abstract
ABSTRACT Using the relationship between institutional trades and sequential public information, this study provides a systematic way to identify institutional trades that are informative about future equity returns. By studying the US financial institutions from 1994 to 2016, I show that institutional trades initiated by managers responding proactively to upcoming informational signals strongly predict future stock returns. The predictability of informed institutions is more evident for stocks with higher information asymmetry and in periods of higher profit opportunities. The informed institutions outperform the uninformed ones by 2% on an annualized basis and their performance gap is persistent. Importantly, the return predictability of informed institutional trades is not subsumed by the return‐predictive signals documented in prior research, computed either from institutional holdings or from financial statements. Further analyses show that the informed institutional investors derive their superior ability to forecast future stock returns from processing corporate fundamentals and acquiring private information. This study derives a novel return predictor using the institutions' proactive trading behavior and identifies various informational sources of informed traders.
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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 | 0.017 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 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".