Abstract
We examine the determinants of credit rating for the debt instruments of Indian firms. The ordered probit model analysis of firms with rating announcements by CARE, CRISIL, and ICRA, the major rating agencies in India, indicates that interest coverage, profitability, financial leverage, market size, stock beta, and volatility have a significant effect on credit ratings. We find evidence that the credit rating levels have become lower after the market regulator’s passage of Transparency and Disclosure Norms in June 2010. The results suggest that regulatory disclosure requirements influence rating agencies to be conservative in their rating standards.
Appendix A: Long-Term Rating Scale by Different Rating Agencies
Symbols | Rating definition |
---|---|
CARE AAA/CRISIL AAA/ICRA AAA | Instruments with this rating are considered to have the highest degree of safety regarding timely servicing of financial obligations. Such instruments carry lowest credit risk. |
CARE AA/CRISIL AA/ICRA AA | Instruments with this rating are considered to have high degree of safety regarding timely servicing of financial obligations. Such instruments carry very low credit risk. |
CARE A/CRISIL A/ICRA A | Instruments with this rating are considered to have adequate degree of safety regarding timely servicing of financial obligations. Such instruments carry low credit risk. |
CARE BBB/CRISIL BBB/ICRA BBB | Instruments with this rating are considered to have moderate degree of safety regarding timely servicing of financial obligations. Such instruments carry moderate credit risk. |
CARE BB/CRISIL BB/ICRA BB | Instruments with this rating are considered to have moderate risk of default regarding timely servicing of financial obligations. |
CARE B/CRISIL B/ICRA B | Instruments with this rating are considered to have high risk of default regarding timely servicing of financial obligations. |
CARE C/CRISIL C/ICRA C | Instruments with this rating are considered to have very high risk of default regarding timely servicing of financial obligations. |
CARE D/CRISIL D/ICRA D | Instruments with this rating are in default or are expected to be in default soon. |
Rating agencies may apply “+” (plus) or “−” (minus) signs for ratings to reflect comparative standing within the category. (Sources: http://www.careratings.com/resources/rating-resources.aspx; http://www.crisil.com/ratings/credit-rating-scale.html; https://www.icra.in/Rating/CreditRatingScale#Long-Term).
Appendix B: Prediction Probabilities
Variable | Obs | Mean | Std. dev. | Min | Max |
---|---|---|---|---|---|
Highest | 14,360 | 0.1594 | 0.2341 | 0.0000 | 1.0000 |
High | 14,360 | 0.3653 | 0.2201 | 0.0000 | 0.6529 |
Adequate | 14,360 | 0.2481 | 0.1529 | 0.0000 | 0.4450 |
Moderate | 14,360 | 0.1358 | 0.1322 | 0.0000 | 0.3602 |
Risk | 14,360 | 0.0469 | 0.0648 | 0.0000 | 0.2224 |
Highrisk | 14,360 | 0.0167 | 0.0287 | 0.0000 | 0.1279 |
Default | 14,360 | 0.0278 | 0.0736 | 0.0000 | 0.9992 |
This table shows the descriptive statistics of the prediction probabilities from the ordered probit regression model. The variables highest, high, adequate, moderate, risk, highrisk and default corresponds to rating categories AAA, AA, A, BBB, BB, B/C and D respectively.
B.1 Prediction Probabilities for Each Rating Category
Variable | Obs | Mean | Std. dev. | Min | Max |
---|---|---|---|---|---|
AAA rating code = 1 | |||||
Highest | 2326 | 0.5195 | 0.2636 | 0.0002 | 0.9921 |
High | 2326 | 0.4114 | 0.1930 | 0.0079 | 0.6529 |
Adequate | 2326 | 0.0605 | 0.0821 | 0.0000 | 0.4417 |
Moderate | 2326 | 0.0075 | 0.0213 | 0.0000 | 0.3602 |
Risk | 2326 | 0.0008 | 0.0050 | 0.0000 | 0.1717 |
Highrisk | 2326 | 0.0001 | 0.0016 | 0.0000 | 0.0647 |
Default | 2326 | 0.0001 | 0.0020 | 0.0000 | 0.0912 |
AA rating code = 2 | |||||
Highest | 5145 | 0.1791 | 0.1823 | 0.0000 | 0.9743 |
High | 5145 | 0.5132 | 0.1471 | 0.0000 | 0.6529 |
Adequate | 5145 | 0.2247 | 0.1421 | 0.0000 | 0.4450 |
Moderate | 5145 | 0.0643 | 0.0817 | 0.0000 | 0.3602 |
Risk | 5145 | 0.0123 | 0.0254 | 0.0000 | 0.2220 |
Highrisk | 5145 | 0.0030 | 0.0084 | 0.0000 | 0.1188 |
Default | 5145 | 0.0034 | 0.0269 | 0.0000 | 0.9992 |
A rating code = 3 | |||||
Highest | 3528 | 0.0347 | 0.0666 | 0.0000 | 0.7597 |
High | 3528 | 0.3203 | 0.1931 | 0.0000 | 0.6529 |
Adequate | 3528 | 0.3568 | 0.0954 | 0.0003 | 0.4450 |
Moderate | 3528 | 0.1938 | 0.1133 | 0.0001 | 0.3602 |
Risk | 3528 | 0.0564 | 0.0534 | 0.0000 | 0.2222 |
Highrisk | 3528 | 0.0171 | 0.0210 | 0.0000 | 0.1279 |
Default | 3528 | 0.0209 | 0.0441 | 0.0000 | 0.9460 |
BBB rating code = 4 | |||||
Highest | 2036 | 0.0125 | 0.0455 | 0.0000 | 1.0000 |
High | 2036 | 0.1727 | 0.1681 | 0.0000 | 0.6528 |
Adequate | 2036 | 0.3206 | 0.1097 | 0.0000 | 0.4450 |
Moderate | 2036 | 0.2713 | 0.0964 | 0.0000 | 0.3602 |
Risk | 2036 | 0.1117 | 0.0684 | 0.0000 | 0.2224 |
Highrisk | 2036 | 0.0421 | 0.0348 | 0.0000 | 0.1279 |
Default | 2036 | 0.0691 | 0.0909 | 0.0000 | 0.8083 |
BB rating code = 5 | |||||
Highest | 733 | 0.0101 | 0.0474 | 0.0000 | 0.8349 |
High | 733 | 0.1289 | 0.1556 | 0.0000 | 0.6529 |
Adequate | 733 | 0.2759 | 0.1266 | 0.0004 | 0.4450 |
Moderate | 733 | 0.2787 | 0.0932 | 0.0000 | 0.3602 |
Risk | 733 | 0.1345 | 0.0692 | 0.0000 | 0.2224 |
Highrisk | 733 | 0.0565 | 0.0394 | 0.0000 | 0.1279 |
Default | 733 | 0.1155 | 0.1392 | 0.0000 | 0.9405 |
B/C rating code = 6 | |||||
Highest | 258 | 0.0074 | 0.0239 | 0.0000 | 0.1765 |
High | 258 | 0.1189 | 0.1619 | 0.0000 | 0.6529 |
Adequate | 258 | 0.2550 | 0.1257 | 0.0000 | 0.4450 |
Moderate | 258 | 0.2776 | 0.0954 | 0.0001 | 0.3602 |
Risk | 258 | 0.1428 | 0.0722 | 0.0007 | 0.2224 |
Highrisk | 258 | 0.0618 | 0.0408 | 0.0001 | 0.1279 |
Default | 258 | 0.1363 | 0.1591 | 0.0000 | 0.9976 |
D rating code = 7 | |||||
Highest | 334 | 0.0058 | 0.0171 | 0.0000 | 0.1240 |
High | 334 | 0.1221 | 0.1540 | 0.0000 | 0.6418 |
Adequate | 334 | 0.2610 | 0.1356 | 0.0000 | 0.4450 |
Moderate | 334 | 0.2709 | 0.0903 | 0.0006 | 0.3602 |
Risk | 334 | 0.1397 | 0.0725 | 0.0019 | 0.2224 |
Highrisk | 334 | 0.0618 | 0.0426 | 0.0002 | 0.1279 |
Default | 334 | 0.1388 | 0.1584 | 0.0001 | 0.9908 |
Appendix C: Comparison of Variables Before and After the Regulation
Variables | Before | After | Difference | t-Statistic | p-Value |
---|---|---|---|---|---|
regulation | regulation | (after–before) | |||
Interest_cover | 8.297 | 9.723 | 1.426 | 3.377 | 0.001 |
Operating_margin | 39.138 | 33.466 | −5.671 | −8.659 | 0.000 |
Debt_equity | 1.901 | 1.712 | −0.189 | −1.833 | 0.067 |
Beta | 0.954 | 1.172 | 0.218 | 28.819 | 0.000 |
Stderr_beta | 0.115 | 0.132 | 0.017 | 14.792 | 0.000 |
Ln_mktcap | 9.364 | 9.811 | 0.447 | 10.293 | 0.000 |
This table compares the mean of the variables before and after the SEBI regulation. The variables are obtained from Prowess Database.
Appendix D: Rating Levels Before and After SEBI Regulation with Restricted Sample
Variables | Rating code |
---|---|
Dummy | 0.622*** |
(0.0359) | |
Interest_cover | −0.00894*** |
(0.000964) | |
Operating_margin | −0.0115*** |
(0.000654) | |
Debt_equity | 0.0333*** |
(0.00595) | |
Beta | 1.075*** |
(0.0582) | |
Stderr_beta | 5.057*** |
(0.457) | |
Ln_mktcap | −0.490*** |
(0.0110) | |
Constant cut1(α 1) | −4.718*** |
(0.127) | |
Constant cut2(α 2) | −2.767*** |
(0.117) | |
Constant cut3(α 3) | −1.566*** |
(0.114) | |
Constant cut4(α 4) | −0.655*** |
(0.115) | |
Constant cut5(α 5) | −0.0141 |
(0.118) | |
Constant cut6(α 6) | 0.446*** |
(0.125) | |
Observations | 5052 |
-
This table shows the ordered probit regression results for numerical rating codes for rating change announcements during the period 2001 to 2011. The dependent variable is the numerical rating code ranging from 1 to 7 (AAA, AA, A, BBB, BB, B/C and D). The dummy variable has a value of one for rating changes after SEBI regulation in June 2010 and zero otherwise. The remaining independent variables are interest coverage ratio, operating margin and debt to equity ratio, beta, standard error of the beta and natural logarithm of the market capitalization. The cut points cut1, cut2, cut3, cut4, cut5, and cut6 are the parameters α 1, α 2, α 3, α 4, α 5, and α 6 of the ordered probit model, which partitions each of the rating categories. Standard errors in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1.
References
Altman, E. I. 1968. “Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy.” The Journal of Finance 23 (4): 589–609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x.Suche in Google Scholar
Amato, J. D., and C. H. Furfine. 2004. “Are Credit Ratings Procyclical?” Journal of Banking & Finance 28 (11): 2641–77. https://doi.org/10.1016/j.jbankfin.2004.06.005.Suche in Google Scholar
Ashbaugh-Skaife, H., D. W. Collins, and R. LaFond. 2006. “The Effects of Corporate Governance on Firms’ Credit Ratings.” Journal of Accounting and Economics 42 (1–2): 203–43. https://doi.org/10.1016/j.jacceco.2006.02.003.Suche in Google Scholar
Baghai, R. P., and B. Becker. 2018. “Non-rating Revenue and Conflicts of Interest.” Journal of Financial Economics 127 (1): 94–112. https://doi.org/10.1016/j.jfineco.2017.10.004.Suche in Google Scholar
Baghai, R. P., H. Servaes, and A. Tamayo. 2014. “Have Rating Agencies Become More Conservative? Implications for Capital Structure and Debt Pricing.” The Journal of Finance 69 (5): 1961–2005. https://doi.org/10.1111/jofi.12153.Suche in Google Scholar
Bertrand, M., P. Mehta, and S. Mullainathan. 2002. “Ferreting Out Tunneling: An Application to Indian Business Groups.” Quarterly Journal of Economics 117 (1): 121–48. https://doi.org/10.1162/003355302753399463.Suche in Google Scholar
Bhojraj, S., and P. Sengupta. 2003. “Effect of Corporate Governance on Bond Ratings and Yields: The Role of Institutional Investors and outside Directors.” Journal of Business 76 (3): 455–75. https://doi.org/10.1086/344114.Suche in Google Scholar
Blume, M. E., F. Lim, and A. C. MacKinlay. 1998. “The Declining Credit Quality of US Corporate Debt: Myth or Reality?” The Journal of Finance 53 (4): 1389–413. https://doi.org/10.1111/0022-1082.00057.Suche in Google Scholar
Dimitrov, V., D. Palia, and L. Tang. 2015. “Impact of the Dodd-Frank Act on Credit Ratings.” Journal of Financial Economics 115 (3): 505–20. https://doi.org/10.1016/j.jfineco.2014.10.012.Suche in Google Scholar
Ederington, L. H. 1985. “Classification Models and Bond Ratings.” Financial Review 20 (4): 237–62. https://doi.org/10.1111/j.1540-6288.1985.tb00306.x.Suche in Google Scholar
Gray, S., A. Mirkovic, and V. Ragunathan. 2006. “The Determinants of Credit Ratings: Australian Evidence.” Australian Journal of Management 31 (2): 333–54. https://doi.org/10.1177/031289620603100208.Suche in Google Scholar
Gupta, R. 2021. “Financial Determinants of Corporate Credit Ratings: An Indian Evidence.” International Journal of Finance & Economics 1–16, https://doi.org/10.1002/ijfe.2497.Suche in Google Scholar
Gupta, R., D. B. Gupta, and H. Chahal. 2017. “Financial Determinants of Credit Ratings of Indian Companies.” International Journal of Business and Information Technology 10 (2): 30–7.Suche in Google Scholar
Jiang, X., and F. Packer. 2019. “Credit Ratings of Chinese Firms by Domestic and Global Agencies: Assessing the Determinants and Impact.” Journal of Banking & Finance 105: 178–93. https://doi.org/10.1016/j.jbankfin.2019.05.011.Suche in Google Scholar
Jorion, P., Z. Liu, and C. Shi. 2005. “Informational Effects of Regulation FD: Evidence from Rating Agencies.” Journal of Financial Economics 76 (2): 309–30. https://doi.org/10.1016/j.jfineco.2004.05.001.Suche in Google Scholar
Jorion, P., C. Shi, and S. Zhang. 2009. “Tightening Credit Standards: The Role of Accounting Quality.” Review of Accounting Studies 14 (1): 123–60. https://doi.org/10.1007/s11142-007-9054-z.Suche in Google Scholar
Kamstra, M., P. Kennedy, and T.‐K. Suan. 2001. “Combining Bond Rating Forecasts Using Logit.” Financial Review 36 (2): 75–96. https://doi.org/10.1111/j.1540-6288.2001.tb00011.x.Suche in Google Scholar
Kaplan, R. S., and G. Urwitz. 1979. “Statistical Models of Bond Ratings: A Methodological Inquiry.” Journal of Business 52 (2): 231–61, https://doi.org/10.1086/296045.Suche in Google Scholar
Kaur, K., and R. Kaur. 2011. “Credit Rating in India: A Study of Rating Methodology of Rating Agencies.” Global Journal of Management and Business Research 11 (12): 63–7.Suche in Google Scholar
Krishnan, K., A. Mukherji, and S. Basu. 2020. “Market Responses to Increased Transparency: An Indian Narrative.” International Review of Economics & Finance 69: 663–77. https://doi.org/10.1016/j.iref.2020.06.033.Suche in Google Scholar
Lal, J., and M. Mitra. 2011. “Effect of Bond Rating on Share Prices: A Study of Select Indian Companies.” Vision 15 (3): 231–8. https://doi.org/10.1177/097226291101500303.Suche in Google Scholar
von Lilienfeld‐Toal, U., D. Mookherjee, and S. Visaria. 2012. “The Distributive Impact of Reforms in Credit Enforcement: Evidence from Indian Debt Recovery Tribunals.” Econometrica 80 (2): 497–558.10.3982/ECTA9038Suche in Google Scholar
Pinches, G. E., and K. A. Mingo. 1973. “A Multivariate Analysis of Industrial Bond Ratings.” The Journal of Finance 28 (1): 1–18. https://doi.org/10.1111/j.1540-6261.1973.tb01341.x.Suche in Google Scholar
Pogue, T. F., and R. M. Soldofsky. 1969. “What’s in a Bond Rating.” Journal of Financial and Quantitative Analysis 4 (2): 201–28. https://doi.org/10.2307/2329840.Suche in Google Scholar
Sehgal, S., and S. Mathur. 2013. “Cross-sectional Variation in Stock Price Reaction to Bond Rating Changes: Evidence from India.” Asian Journal of Finance & Accounting 5 (2): 47. https://doi.org/10.5296/ajfa.v5i2.3998.Suche in Google Scholar
de Souza Murcial, F. C., F. Dal-Ri Murcia, S. Rover, and J. Alonso Borba. 2014. “The Determinants of Credit Rating: Brazilian Evidence.” BAR-Brazilian Administration Review 11 (2): 188–209. https://doi.org/10.1590/s1807-76922014000200005.Suche in Google Scholar
Vig, V. 2013. “Access to Collateral and Corporate Debt Structure: Evidence from a Natural Experiment.” The Journal of Finance 68 (3): 881–928. https://doi.org/10.1111/jofi.12020.Suche in Google Scholar
© 2022 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- Featured Articles (Research Paper)
- Are Stay-at-Home and Face Mask Orders Effective in Slowing Down COVID-19 Transmission? – A Statistical Study of U.S. Case Counts in 2020
- The Impact of GATS on the Insurance Sector: Empirical Evidence from Pakistan
- Processing of Information from Risk Maps in India and Germany: The Influence of Cognitive Reflection, Numeracy, and Experience
- The Determinants of Credit Rating and the Effect of Regulatory Disclosure Requirements: Evidence from an Emerging Market
- Automatic Segmentation of Insurance Rating Classes Under Ordinal Constraints via Group Fused Lasso
Artikel in diesem Heft
- Frontmatter
- Featured Articles (Research Paper)
- Are Stay-at-Home and Face Mask Orders Effective in Slowing Down COVID-19 Transmission? – A Statistical Study of U.S. Case Counts in 2020
- The Impact of GATS on the Insurance Sector: Empirical Evidence from Pakistan
- Processing of Information from Risk Maps in India and Germany: The Influence of Cognitive Reflection, Numeracy, and Experience
- The Determinants of Credit Rating and the Effect of Regulatory Disclosure Requirements: Evidence from an Emerging Market
- Automatic Segmentation of Insurance Rating Classes Under Ordinal Constraints via Group Fused Lasso