A study on economic factors affecting credit ratings of Indian companies

Mamilla, Rajesh; Mehta, Minesh P.; Shukla, Abhijay; Agarwal, Piyush K.

doi:10.21511/imfi.16(2).2019.27

Cited by 1 publication

(3 citation statements)

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“…Previous studies have considered a range of potential determinants for analyzing bond ratings. Some studies, for instance, Mamilla et al (2019), study the impact of certain economic variables on credit rating. However, the omission of macroeconomic variables, which do not vary cross-sectionally, would not impact the ranking of the estimation methods and hence are not considered in the analysis.…”

Section: Mf 493mentioning

confidence: 99%

“…Previous studies have considered a range of potential determinants for analyzing bond ratings. Some studies, for instance, Mamilla et al. (2019), study the impact of certain economic variables on credit rating.…”

Section: Data and Variable Descriptionmentioning

confidence: 99%

“…In the Indian context, Bhattacharya and Sharma (2019) ascertain the impact of environment, social and governance (ESG) disclosures on credit ratings using ordered logistic regression. Mamilla et al (2019) studied the impact of certain economic variables on credit rating. Bandyopadhyay (2019) discusses the accuracy of the Indian CRAs.…”

Section: Introductionmentioning

confidence: 99%

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Bond rating determinants and modeling: evidence from India

Sehgal

Vasishth

Agrawal

2022

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PurposeThis study attempts to identify fundamental determinants of bond ratings for non-financial and financial firms. Further the study aims to develop a parsimonious bond rating model and compare its efficacy across statistical and range of machine learning methods in the Indian context. The study is motivated by the insufficiency of prior work in the Indian context.Design/methodology/approachThe authors identify the critical determinants of non-financial and financial firms using multinomial logistic regression. Various machine learning and statistical methods are employed to identify the optimal bond rating prediction model. The data cover 8,346 bond issues from 2009 to 2019.FindingsThe authors find that industry concentration, sales, operating leverage, operating efficiency, profitability, solvency, strategic ownership, age, firm size and firm value play an important role in rating non-financial firms. Operating efficiency, profitability, strategic ownership and size are also relevant for financial firms besides additional determinants related to the capital adequacy, asset quality, management efficiency, earnings quality and liquidity (CAMEL) approach. The authors find that random forest outperforms logit and other machine learning methods with an accuracy rate of 92 and 91% for non-financial and financial firms.Practical implicationsThe study identifies important determinants of bond ratings for both non-financial and financial firms. The study interalia finds that the random forest technique is the most appropriate method for bond ratings predictions in India.Social implicationsBetter bond ratings may mitigate corporate defaults.Originality/valueUnlike prior literature, the study identifies determinants of bond ratings for both non-financial and financial firms. The study also experiments with modern machine learning techniques besides the traditional statistical approach for model building in case of relatively under researched market.

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Section: Mf 493mentioning

confidence: 99%

Section: Data and Variable Descriptionmentioning

confidence: 99%