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Record W4378377398 · doi:10.3390/jrfm16060287

(How) Does Mutual Fund Dual Ownership Affect Shareholder and Creditor Conflict of Interest? Evidence from Corporate Innovation

2023· article· en· W4378377398 on OpenAlexvenueno aff
Lei Gao, Ying Wang, Jing Zhao

Bibliographic record

VenueJournal of risk and financial management · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessShareholderCreditorDual (grammatical number)Mutual fundAccountingFinanceFinancial systemCorporate governanceDebt

Abstract

fetched live from OpenAlex

We examine the impact of mutual fund dual ownership (i.e., simultaneous holdings of stocks and bonds of the same company by mutual fund families) on corporate innovation. Our findings indicate that dual ownership is positively associated with innovation quantity, quality, generality, and originality. This effect is mainly driven by non-index funds, which are more likely to be active monitors. Consequently, both stocks and bonds held by dual owners tend to generate higher returns, particularly for more significant, groundbreaking innovations. These results suggest that mutual fund dual ownership mitigates conflicts of interest between shareholders and creditors, thereby enhancing innovation and firm value. However, the relation between dual ownership and innovation turns negative during the recent financial crisis, suggesting that shareholder-creditor conflicts culminate in extreme financial distress, exacerbating dual holders’ risk aversion, and hence, hindering corporate innovation.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.125
GPT teacher head0.260
Teacher spread0.135 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2023
Admission routes1
Has abstractyes

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