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Record W2295695672 · doi:10.1142/s2010139216500117

How do Corporate Governance Decisions Affect Bondholders?

2016· article· en· W2295695672 on OpenAlexaff
Hong Li, Yuan Wang

Bibliographic record

VenueQuarterly Journal of Finance · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsConcordia University
FundersPennsylvania State University
KeywordsCorporate governanceExpropriationAgency costInformation asymmetryShareholderDebtBusinessAsset (computer security)Agency (philosophy)Principal–agent problemAffect (linguistics)Monetary economicsEconomicsAccountingFinanceMarket economy

Abstract

fetched live from OpenAlex

Existing studies have documented a negative relationship between the GIM corporate governance index (which contains anti-takeover provisions) and the corporate cost of debt, which implies that fewer anti-takeover provisions may lead to a larger shareholder expropriation of bondholder wealth. That is, strong corporate governance hurts bondholders (asset substitution hypothesis). However, another stream of research asserts that governance mechanisms may benefit bondholders by paring down agency costs and decreasing information asymmetry between the firm and the lenders (monitoring hypothesis). We reexamine this issue by considering the self-selection effect. We find that both hypotheses can be true, and that firms consider the reduction of cost of debt when self-selecting their governance, and the cost of debt would have been much higher had the alternative governance decision been made.

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)0.002
metaresearch head score (Gemma)0.010
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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.027
GPT teacher head0.211
Teacher spread0.184 · 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

Citations5
Published2016
Admission routes1
Has abstractyes

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