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Record W2769278336

A Cross- country study of the effect of institutional ownership on credit ratings

2017· article· en· W2769278336 on OpenAlexaboutno aff
Aws AlHares, Collins G. Ntim

Bibliographic record

VenueEconStor Open Access Articles · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate governanceAccountingExtant taxonLeverage (statistics)EconomicsBusinessFinancial systemFinance
DOInot available

Abstract

fetched live from OpenAlex

A considerable number of studies have examined the relationship between corporate governance (CG) structures and corporate performance (e.g., Yermack, 1996; Gompers et al., 2003; Beiner et al., 2006; Renders et al., 2010; Ntim et al., 2012; Kumar & Zattoni 2013; Griffin, et al., 2014). In contrast, despite its importance as demonstrated by the recent financial crisis, studies examining why and how a corporation’s CG mechanisms might influence its credit ratings are rare (e.g., Switzer and Wang, 2013;Matthies, 2013; Tran, 2014). This research, therefore, seeks to contribute to the extant literature by exploring the effects of (CG) mechanisms on corporate credit ratings. Specifically, using a sample of 200 firms from 10 OECD countries over ten years covering the pre- and post-2007/08 global financial crisis period from Anglo American (i.e., Australia, Canada, Ireland, UK, and US) and Continental European (i.e., France, Germany, Italy, Japan and Spain) traditions and employing a total of 200 listed companies, this paper hopes to achieve a number of objectives. First, the paper attempted to assess the levels of compliance with, and disclosure of, CG principles contained in the 2004 OECD CG Code in firms from two different traditions: Anglo America and Continental Europe. Second, the paper sought to investigate the relationship between CG mechanisms and credit ratings. These relationships will be explored by employing firm-level CG mechanisms (ownership structures measured by Institutional Ownership) by accounting for firm-level control variables (e.g., firm size, growth, profitability, and leverage) based on a multi-theoretical framework that incorporates insights from agency and legitimacy theories. The findings revealed that there was a strong negative relationship between institutional ownership and credit ratings. From the descriptive analysis, it was shown that institutional owners did not have a very high credit rating. When the control variables were assessed, it was shown that they had a negative influence on the credit ratings with sales growth and leverage and positive significant relationship with firm size, corruption index, power distance and Anglo American countries.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.004
Open science0.0020.001
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 head0.054
GPT teacher head0.330
Teacher spread0.276 · 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.

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
Published2017
Admission routes1
Has abstractyes

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