Correlated Trading by Life Insurers and Its Impact on Bond Prices
Bibliographic record
Abstract
Abstract Our evidence indicates that U.S. life insurers’ decisions to buy and sell individual corporate bonds are correlated across companies within the life insurance industry. On average, the correlation in sell decisions is greater in smaller bonds, bonds with lower ratings, bonds that have been downgraded, and bonds that have recently experienced relatively large abnormal returns. Correlated trading was also elevated during the financial crisis. In addition, correlated buying and selling are greater when insurers designated as systemically important financial institutions are actively trading. We also find that the bonds that insurers sell in a correlated manner exhibit negative average abnormal returns during the quarter in which insurers are selling. One explanation is that insurers’ correlated selling is temporarily pushing bond prices below their fundamental value. In this case, we would expect prices to bounce back in the subsequent quarter. However, we do not find a rebound in prices and therefore our evidence supports the alternative explanation that insurers’ correlated selling is impounding information into bond prices.
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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 | 0.019 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".