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Do sovereign credit ratings matter for corporate credit ratings?

  • S.I.: Networks and Risk Management
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Abstract

This paper proposes a new approach for tackling the issue of the impact of sovereign rating on corporate ratings. As the policy of never rating a private issuer above its government (sovereign ceiling) has been relaxed by the major credit rating agencies, further empirical investigation is needed to identify the key factors that determine the strength of sovereign-corporate nexus. We suggest implementing a nonlinear panel smooth transition regression modelling where the sovereign effect is allowed to vary across different firm-level financial states. Our results reveal that financially healthier corporations in terms of interest and debt coverage ratios are found to be less dependent on their home country credit risk. Our empirical findings have important implications for credit market participants and offer a call for a better understanding of the role of firm-specific financial characteristics in the rating decisions.

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Notes

  1. The principle of the sovereign ceiling has been gradually relaxed by the major rating agencies, Standard & Poor’s (S&P), Fitch, and Moody’s, in 1997, 1998 and 2001, respectively.

  2. See S&P (2013). “Infosys Ltd. And Wipro Ltd. Upgraded To ‘A−’ From ‘BBB + ’ On Revised Criteria; Outlook Stable”, Credit Research, Dec 13, 2013. The two Indian companies concerned here were Infosys Ltd. and Wipro Ltd.

  3. See Moody’s (2016). “Moody’s concludes review on eight Turkish corporates”. Global Credit Research, Sep. 26, 2016. The Turkish corporates involved here were KOC Holding and OYAK; CCI, TURKCELL and EFES.

  4. In the same vein, Ferri et al. (2001) have explained that a sovereign downgrading would imply larger changes in capital allocations than an upgrading.

  5. In the same vein, Huang and Shen (2015) split their sample period into two sub-periods—before the financial crisis (2002–2007) and after the financial crisis (2009–2011)—to check whether the financial crisis influenced the nexus between sovereign and bank ratings.

  6. For a recent survey of corporate distress prediction models, see e.g., Mousavi and Ouenniche (2018).

  7. The definition of the firm-level variables is not completely identical across the empirical studies on corporate default, but they account for the same underlying factors that drive firm credit risk (see e.g. Altman 2000). For a recent survey of corporate distress prediction models, see e.g. Mousavi and Ouenniche (2018).

  8. See Standard & Poor’s (2013). “Corporate Methodology: Ratios and Adjustments.”, November 2013, and Moody’s (2012b), “Moody’s Financial Metrics™ Key Ratios by Rating and Industry for Global Non-Financial Corporations”, December 2012.

  9. Based on banks’ ratings published by S&P from 86 emerging and advanced countries over the period 2002–2008, Shen et al. (2012) investigated the effects of financial ratios on ratings and whether they are affected by the level of information asymmetry. According to their empirical findings, the influence of financial ratios on bank ratings is greater in low information asymmetry countries (such as high-income countries) but reduced in countries with high information asymmetry (such as emerging market countries).

  10. In another strand of literature, Ben Hmiden et al. (2020) have assessed the effect of changes in bank ratings on capital structure. They revealed that a rating downgrade have significant negative impact on the equity-to-assets ratio.

  11. See Moody’s (2012a), How sovereign credit quality may affect other ratings, February 2012.

  12. In another strand of literature, ordered probit methods are used as the credit rating is a discrete variable and reflects an order in terms of probability of default. However, the generalization of ordered probit to panel data is not simple, because of country/firm specific effects. Also, within this framework, the need to have many observations makes it more difficult to perform robustness analysis especially when conducting a split-sample approach (see e.g. Afonso et al. 2011).

  13. The ratings assigned by the three major rating agencies tend to be highly correlated, with deviations usually restricted to within one or two notches (see e.g. Hill et al. 2010).

  14. In another strand of literature, Chalamandaris and Vlachogiannakis (2018) have revealed that financial ratio analysis continues to play a significant role in the decision-making process of Credit Default Swaps (CDS) traders. Besides, Castellano and D’Ecclesia (2013) has shown how credit rating changes may affect CDS quote dynamics. According to the authors, this will allow for a better understanding of the investor’s perception of the changing creditworthiness of a company.

  15. As discussed in the literature review section, other balance-sheet data are considered in the existing literature. For example, Brezeanu and Triandafil (2008) find solvency and liquidity variables to be more relevant for companies from emerging economies, while profitability indicators are more relevant for companies from developed countries. Our paper focuses only on four firm-specific financial variables for which we have sufficient data spans available to render nonlinear dynamic modeling possible or robust. For some financial ratios, such as liquidity (defined as working capital/assets), the presence of regime-switching behavior could not be detected due to the lack of data availability for corporations in emerging markets.

  16. For the construction of interest coverage ratio, Moody’s uses EBITA (earnings before interest, taxes and amortization). The same ratio was implemented by Borensztein et al. (2013) and Klein and Stellner (2014), among others.

  17. See e.g. Cantor and Packer (1996) for more details about the correlation between credit ratings and macroeconomic variables.

  18. Data availability of firm-level financial variables is critical in some emerging countries. We have selected only companies for which enough balance-sheet data are available to make nonlinear dynamic modeling possible. This resulted in a sample of 53 firms where key financial ratios used in their rating process, i.e., debt coverage, interest coverage, and leverage, are available.

  19. Our sample of emerging markets includes Hong Kong, Korea, Singapore, and Taiwan, which are considered advanced Asian countries, according to the International Monetary Fund (IMF) classification. We have selected firms from these advanced Asian countries having comparable firm-level financial variables with the rest of our sample. As shown in Tables 13, 14, 15 and 16 in Appendix, companies domiciled in Hong Kong, Korea, Singapore, and Taiwan are not necessarily the financially healthier ones among our sample.

  20. Similarly, if there are unobserved characteristics that are common to all countries but vary across time, then it is possible to consider time-fixed effects in order to control for common global shocks, such as the recent financial turmoil in 2008/2009. This does not alter our main results, as the introduction of regime-switching behavior, as in Eq. (2), would endogenously capture the effects of such common events on the sovereign-corporation nexus.

  21. In another strand of literature, Ben Hmiden and Ben Cheikh (2016) tests the presence of threshold effect in sovereign credit ratings with respect to government debt level. They found a strong regime-dependence in rating decisions, in the sense that the determinants of sovereign risk vary across different vulnerability levels.

  22. Detailed description of estimation method is provided in González et al. (2017).

  23. Better size properties in small samples is found for the F -version of the LM test compared to the asymptotic χ2 -based statistic (see e.g., González et al. (2017).

  24. We have run a Hausman specification test which compares the fixed versus random effects under the null hypothesis that the individual effects are uncorrelated with the other regressors in the model. According to our results, the null hypothesis of random effects is strongly rejected and the fixed effects model is preferred. Results of the Hausman test are available by request; they are not reported in the paper due to space constraints.

  25. Durbin and Ng (2005) explain that hitting or exceeding the ceiling occurs because firms have either high export earnings or an ownership link with either a foreign corporation or the foreign home government. Under these situations, the creditworthiness of a firm relies on its own reputation.

  26. In this case, performing Wald test for \({H}_{0}\): \({\beta }_{0}-\left({\beta }_{0}+{\beta }_{1}\right)=0\) is equivalent to testing the significance for the coefficient \({\beta }_{1}\), namely \({H}_{0}\): \({\beta }_{1}=0\).

  27. As stated by Altman (2000), firms that are highly leveraged would attract worse ratings.

  28. As outlined in Table 2, non-sovereign issuers with median cash flow-to-debt ratio larger than \(27.5\%\) are generally rated higher than Baa.

  29. Ferri et al. (2001) reported that the effect of sovereign ratings is negligible in developed countries and individual firm-level characteristics are the main driver of the private borrowers’ solvency.

  30. See Standard & Poor’s (2018). “Mondi Group Upgraded to ‘BBB + ’ on Strengthening Credit Metrics; Outlook Stable”. Credit Research, Apr. 12, 2018.

  31. See Standard & Poor’s (2017). “South Africa Ratings Lowered on Weakening Economic and Fiscal Trajectory; Outlook Stable”. Credit Research, Nov. 24, 2017.

  32. It is possible to classify companies with respect to the number observations below or above the estimated threshold value. For example, a company having more than half of the observations of its interest coverage ratio larger than \(16.54\) will be considered as belonging to the “high-interest coverage” group, and vice versa. This does not alter our classification, which remains quite similar to when we consider the mean annual values.

  33. For example, Moody’s has recently downgraded Turkey’s sovereign ratings from Ba1 to Ba2 due to a high political risk, whilst several Turkish companies seem to be unaffected by the downgrade (see Moody’s 2018, “Moody’s downgrades Turkey’s sovereign ratings to Ba2 from Ba1; outlook changed to stable from negative”, March 7, 2018).

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Authors and Affiliations

  1. ESSCA School of Management, 1 Rue Lakanal, 49000, Angers, France

    Nidhaleddine Ben Cheikh & Oussama Ben Hmiden

  2. EDC Paris Business School, 70 Galerie des Damiers, 92415, Courbevoie, France

    Younes Ben Zaied

  3. EM Normandie Business School, Métis Lab, Paris, France

    Sabri Boubaker

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  1. Nidhaleddine Ben Cheikh
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  2. Oussama Ben Hmiden
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  3. Younes Ben Zaied
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  4. Sabri Boubaker
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Correspondence to Younes Ben Zaied.

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Appendix

Appendix

See Tables 10, 11, 12, 13, 14, 15 and 16.

Table 10 Sample of corporations by country and industry
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Table 11 Scale and distribution of corporate and sovereign rating grades
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Table 12 Wald test for difference between regimes
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Table 13 Sample split with respect to the level of interest coverage
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Table 14 Sample split with respect to the leverage ratio
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Table 15 Sample split with respect to level of cash flow-to-debt
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Table 16 Sample split with respect to the level of debt-to-EBITDA
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Cheikh, N.B., Hmiden, O.B., Zaied, Y.B. et al. Do sovereign credit ratings matter for corporate credit ratings?. Ann Oper Res 297, 77–114 (2021). https://doi.org/10.1007/s10479-020-03590-z

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