Quasi‐Indexer Ownership and Insider Trading: Evidence from Russell Index Reconstitutions*
Bibliographic record
Abstract
ABSTRACT Understanding the association between quasi‐indexer ownership and insider trading is important given the externalities that insider trading can impose on shareholders, the importance of quasi‐indexers in the capital markets, and their mixed monitoring incentives. The prior literature has produced an inconsistent set of results regarding this association. These results are difficult to interpret because the association between them is likely endogenous, and prior studies have not employed effective identification strategies to address this issue. In this study, we examine the effects of quasi‐indexer institutional ownership on insider trading using the plausibly exogenous discontinuity in quasi‐indexer ownership around the Russell 1000/2000 index cutoff. Using both regression discontinuity and instrumental variable research designs, we find higher quasi‐indexer ownership leads to less insider trading (both buys and sells) and less profitable sell trades. The effects for sells are concentrated among insider trades that, ex ante, are more likely to be based on private information. Our evidence on the profitability of buys is mixed. In addition, we find firms with higher quasi‐indexer ownership are more likely to have and/or more strictly enforce blackout policies. Overall, our results suggest that quasi‐indexers can reduce the agency costs associated with insider trading through their direct and indirect monitoring activities.
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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 | 0.003 | 0.026 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".