Financial determinants of corporate credit ratings: An Indian evidence

Abstract

Credit rating is a vital tool in the hands of investors and other users. Standard mathematical formulas do not exist for determining credit ratings. Instead, rating agencies exercise their judgment and use their experience to determine what information is to be important in providing a rating. Thus, knowledge and understanding about factors influencing credit ratings is very crucial for companies being rated and other users. Most of the prior studies in related context have used developed countries' data, and the effect in emerging markets, especially in Indian context, is still unclear due to lack of such studies. So, this study attempts to identify the long-term and short-term financial determinants of credit ratings issued to Indian companies and examines their impact on credit ratings on the basis of panel data and cross-sectional approaches. Ordered probit analysis is used with credit ratings as the dependent variable and six financial factors as the independent variables. Results indicate that size, profitability and leverage have a significant relationship with corporate credit ratings in both panel data and cross-sectional approaches. Also, size has the highest impact on credit ratings followed by leverage and profitability in both the approaches. The major contribution of present study is that two different mathematical models are framed which have a reasonably well correct prediction percentage. These models can be used by the investors' academicians, practitioners and other users for predicting the rating categories of various companies and to get a fair idea about their creditworthiness and safety.

Impact of Competition in Credit Rating Industry: Evidence From India

“…Control variables of Size, Leverage, Profitability, and Interest Coverage account for firm-specific characteristics driving the firm’s rating, as used in several other studies (Gupta et al, 2017; Jiang et al, 2012; Kisgen, 2006). As explained earlier, rating levels “C” and “D” have been excluded from our analysis.…”

“…We use a two-sample t -test to compare the mean credit metrics of the two groups. Several studies have highlighted that these financial metrics have a significant relationship with credit rating (Blume et al, 1998; Gray et al, 2006; Gupta et al, 2017). These financial metrics are winsorized at their 1% and 99% percentiles to eliminate outliers.…”

“…Therefore, in case of hypothesis 2 is rejected, it will mean that CRA ''A'' will tend to give a higher rating to a firm if another CRA is also rating the firm. Hypothesis 2 is tested using Ordinary Least Squares (OLS) regression and Ordered Probit regression, employed in several previous credit ratings related papers (Cubas-Dı´az & Martı´nez Sedano, 2018;Gupta et al, 2017;Park & Lee, 2018). The firm's credit rating is converted into numerical values to run the regression, explained later in the Results and Discussion section.…”

Determinan Peringkat Obligasi pada Perusahaan Non Keuangan yang Terdaftar di BEI

“…Laba perusahaan ini nantinya dapat digunakan untuk membayar kewajiban dan bunga yang telah jatuh tempo sehingga risiko gagal bayar suatu perusahaan akan menurun dan peringkat obligasi suatu perusahaan akan meningkat. Hasil ini sesuai dengan penelitian yang dilakukan oleh Sahabuddin & Hadianto (2020) serta Gupta (2021). Akan tetapi, hasil ini berlawanan dengan penelitian yang dilakukan oleh Laila et al (2021) serta Utami et al (2017) yang menyatakan bahwa profitabilitas tidak berpengaruh secara signifikan terhadap peringkat obligasi.…”

“…Hal ini menyebabkan tingginya likuiditas tidak mempengaruhi terhadap peringkat obligasi suatu perusahaan. Hasil ini sesuai dengan penelitian yang dilakukan oleh Laila et al (2021); Henny (2016) serta Gupta (2021). Akan tetapi, hasil ini berlawanan dengan penelitian yang dilakukan oleh Utami et al (2017) yang menyatakan bahwa likuiditas berpengaruh positif dan signifikan terhadap peringkat obligasi.…”