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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".