Large shareholders and credit ratings

Kedia, Simi; Rajgopal, Shivaram; Zhou, Xing

doi:10.1016/j.jfineco.2017.03.007

Cited by 71 publications

(28 citation statements)

References 34 publications

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“…This literature strand has been extended by various features such as the relationship between capital structure decisions and credit rating (Naeem 2012;Ntswane 2014;Kedia et al 2017), the relationship between ratings and the probability of default, rating transitions, bank internal credit rating systems, and numerous methodological approaches (Anjum 2012;Stepanyan 2014;Angilella and Mazzù 2015;Sanesh 2016).…”

Section: Introductionmentioning

confidence: 99%

Credit Rating as a Mechanism for Capital Structure Optimization: Empirical Evidence from Panel Data Analysis

Sajjad

Zakaria

2018

IJFS

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This paper empirically examines the significance of credit ratings for optimal capital structure decisions. Non-financial Asian listed companies, evaluated by Standard and Poor's, are selected from 2000 to 2016. Panel data analysis with pooled ordinary least square (OLS), fixed effect (FE), and generalized method of moment (GMM) estimation techniques are employed to test the effect of each credit rating scale on capital structure choices. For the problem of heteroskedasticity in OLS, the heteroskedastic white consistent variance is used for the best fit of the model. Findings of all estimation techniques show that the relationship between credit rating scales and leverage ratio is a non-linear inverted U shape. High-and low-rated companies have a low level of leverage, whereas mid-rated companies have a high level of leverage. It is evident that costs and benefits of each rating scale have a substantial effect on the behavior of a company's choices for optimal capital structure. The study suggests that policymakers, investors, and financial officers should consider credit rating as an important measure of financing decisions.

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Section: Introductionmentioning

confidence: 99%

Credit Rating as a Mechanism for Capital Structure Optimization: Empirical Evidence from Panel Data Analysis

Sajjad

Zakaria

2018

IJFS

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“…They show that this bias affects corporate decision making, which is consistent with the evidence in Kisgen (2006). Kedia, Rajgopal, andZhou (2014, 2017) present evidence that Moody's awards differentially higher ratings to firms from which it was likely to earn more revenues after it became a publicly traded firm, or that were held in the portfolios of its two largest post-IPO shareholders (Berkshire Hathaway and Davis Selected Advisors). None of these studies, however, use the career outcomes of analysts to infer the preferences of credit rating agencies, which is our primary contribution.…”

Section: Hypothesis Development and Related Literaturementioning

confidence: 53%

Analyst Promotions within Credit Rating Agencies: Accuracy or Bias?

Kisgen

Osborn²,

Reuter

2016

SSRN Journal

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We examine whether credit rating agencies reward accurate or biased analysts. Using data collected from Moody's corporate debt credit reports, we find that Moody's is more likely to promote analysts who are accurate, but less likely to promote analysts who downgrade frequently. Combined, analysts who are accurate but not overly negative are approximately twice as likely to get promoted. Further, analysts whose rating changes are more informative to the market are more likely to get promoted, unless their ratings changes cause large negative market reactions. Moody's balances a desire for accuracy with a desire to cater to its corporate clients.

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“…Owing to rating inflation, China's credit rating agencies do not possess sufficient credibility to be recognized by the market (Kennedy, 2008). On the other hand, credit ratings may be influenced by several other factors, such as corporate governance (Ashbaugh-Skaife, Collins, & Lafond, 2006), credit rating agency competition (Bolton, Freixas, & Shapiro, 2012), and the macroeconomy (Kedia, Rajgopal, & Zhou, 2017). This complicated situation raises the question of whether credit ratings can reflect the true financial risk of the issuing firms.…”

Section: Interlocking Networkmentioning

confidence: 99%

Do social ties matter for corporate bond yield spreads? Evidence from China

Qiu

Xiao

2019

Corporate Governance

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Research Question/Issue: This paper studies the effect of top management team (TMT) network centrality on corporate bond yield spreads. In additional analyses, we examine the hierarchical structures of TMT and study which parts contribute to the decrease in bond yield spreads. We also examine the mediating roles (including informed trading probability, media coverage, political ties, financial ties, and bond ratings) and the moderating roles (including marketization and analysts following) in the correlation between TMT network centrality and corporate bond yield spreads.Research Findings/Insights: This paper finds that firms with high TMT network centrality are significantly negatively correlated with lower bond yield spreads. The findings are robust after controlling for a series of sensitivity checks and endogenous concerns. The results of the hierarchical structures show that the decrease of bond spreads is accounted for CFOs' networks and board members' networks, whereas the role of CEOs' networks is not apparent. More importantly, after controlling CEOs'

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Large shareholders and credit ratings

Cited by 71 publications

References 34 publications

Credit Rating as a Mechanism for Capital Structure Optimization: Empirical Evidence from Panel Data Analysis

Credit Rating as a Mechanism for Capital Structure Optimization: Empirical Evidence from Panel Data Analysis

Analyst Promotions within Credit Rating Agencies: Accuracy or Bias?

Do social ties matter for corporate bond yield spreads? Evidence from China

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