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Record W3137195937 · doi:10.1111/1911-3846.12678

Common Mutual Fund Ownership and Systemic Risk*

2021· article· en· W3137195937 on OpenAlexaffvenue
Michael Iselin, Scott Liao, Haiwen Zhang

Bibliographic record

VenueContemporary Accounting Research · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSystemic riskMutual fundBusinessCommon ownershipUnintended consequencesVotingFinancial systemEconomicsFinancial crisisFinancePolitical science

Abstract

fetched live from OpenAlex

ABSTRACT We examine whether bank connections via common mutual fund ownership serve as a contagion channel affecting the systemic risk of the banking system. Examining this relation is important because common mutual fund ownership has increased dramatically over the past 20 years, and a buildup of systemic risk was at the heart of the 2008–2009 financial crisis. We predict and document that the extent of a bank's connection with other banks via common ownership increases its contribution to systemic risk. We further predict and find that this association is primarily driven by passive mutual funds. We provide evidence that common passive ownership results in higher systemic risk through two mechanisms: nondiscretionary sell‐offs of bank stocks and a common pattern of voting. Our results are also robust to two alternate instrumental variable analyses. This study contributes to the literature by documenting an unintended, macro‐level consequence of common mutual fund ownership. Our findings broaden the understanding of common ownership as one mechanism through which systemic risk materializes and should be particularly relevant for regulators who seek to prevent future systemic failures.

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 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)0.008
metaresearch head score (Gemma)0.002
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 score0.085
Threshold uncertainty score0.775

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.126
GPT teacher head0.321
Teacher spread0.195 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2021
Admission routes2
Has abstractyes

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