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Record W2913933910 · doi:10.1111/jori.12276

Correlated Trading by Life Insurers and Its Impact on Bond Prices

2019· article· en· W2913933910 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Risk & Insurance · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicInsurance and Financial Risk Management
Canadian institutionsnot available
Fundersnot available
KeywordsBondQuarter (Canadian coin)BusinessMonetary economicsLife insuranceValue (mathematics)Financial economicsEconomicsFinanceActuarial science

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.818

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.218
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it