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Record W4413408613 · doi:10.1057/s41260-025-00422-2

Asset Allocation, Diversification, and Co-Movement Effects: A Global Analysis of Bonds and Equities Issued by the Same Firm

2025· article· en· W4413408613 on OpenAlexaff
Cheng Liu, Peter Clarkson

Bibliographic record

VenueJournal of Asset Management · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsSimon Fraser University
FundersUniversity of Queensland
KeywordsDiversification (marketing strategy)Asset allocationBondBusinessIT asset managementFinancial economicsEconomicsAsset (computer security)Monetary economicsAsset managementFinancePortfolioComputer science

Abstract

fetched live from OpenAlex

Abstract This study investigates the asset return co-movement with the same issuer and examines the relationship between the correlation and agency conflicts. This study constructs a large unique panel dataset that consists of 2,089 firms from 2001 to 2019 across 50 countries. This research reveals a compelling positive correlation between the monthly returns of bonds and equities. Moreover, lower conflict of interest among stakeholders corresponds to a higher degree of co-movement between these financial instruments. This insightful discovery underscores the pivotal role of corporate governance and regulatory safeguards in harmonizing the interests of debtholders and equity holders. Notably, these results maintain their robustness when employing the rigorous two-stage least squares instrumental variable approach and conducting supplementary sub-sample tests. This study stands as a pioneering investigation into bond–equity co-movement from the same issuer, shedding light on its intricate connections to agency conflicts and regulatory influences.

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)0.001
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 score0.354
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.008
GPT teacher head0.244
Teacher spread0.236 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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