A Cross- country study of the effect of institutional ownership on credit ratings
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".