We live in uncertain and, some would say, unique times. The benign credit cycle after the global financial crisis is quickly coming to an end as central banks stop quantitative easing programs and juggle between increasing interest rates to curb inflationary pressure, and economic recessionary risks, corporations suffer from post-pandemic debt hangover, energy supply/price shocks, and slowing revenue growth, and the whole world witnesses unprecedented geo-political instabilities.
The Corporate Bond Market Distress Index (CMDI), launched a few months ago by the US Federal Reserve, provides a timely signal of the corporate bond market functioning. The index is still tracking well below its historical worst level, but has been steadily increasing since the beginning of 2020, suggesting more strain in the Investment Grade (IG) segment.
Figure 1: United States Corporate Bond Market Distress Index.
Source: United States Federal Reserve Bank of New York. For illustrative purposes. (As of November 8th, 2022).
During increasing economic, market, and geo-political turmoil, it is critical to anticipate risks and detect investment opportunities that may surface in the bond market.
In this short blog, we discuss how S&P Global Market Intelligence’s Credit Analytics models can be used to establish an automated framework that generates early warning signals of potential credit risk deterioration or improvement. You can use these signals to manage investment portfolios, reduce exposure to troubled entities or seize opportunities well ahead of the market.
For this analysis, we have used RiskGauge™, a statistical model that combines company fundamentals, market signals, industry- and country-specific scores, as well as the ranking power of S&P Global Ratings’ issuer credit ratings to generate a holistic measure of credit risk for public and private companies, globally, on a daily basis.
The RiskGauge score is optimized to achieve great discriminatory power (circa 96%), but also to statistically match S&P Global Ratings’ (SPGR) issuer credit ratings of rated companies (circa 60% will match within one notch, and 80% will match within two notches). We stress, for the avoidance of doubt, that no statistical model can predict an SPGR issuer credit rating changes with certainty, but this historical analysis shows how Credit Analytics’ RiskGauge score can be employed to generate an early warning signal of potential future credit risk improvement or deterioration.
We analyzed the divergences between the RiskGauge score and SPGR rating, recording their value at the beginning of each month (in the period January 2016 to October 2021) and checking whether and how the SPGR rating changed in the subsequent 12 months for a sample of United States publicly listed corporates.
Figure. 2 summarizes our empirical findings. When the RiskGauge score was three+ notches better than the SPGR issuer credit rating at a certain date, in 34% of the cases, we recorded a rating upgrade within the next 12 months, and only 5% of ratings got downgraded. Conversely, when the RiskGauge score was three+ notches worse than SPGR issuer credit rating, in 20% of cases, we recorded a rating downgrade within the next 12 months, and only 3% of ratings got upgraded. Interestingly, we also found similar results for publicly-listed corporates outside the U.S. and privately held corporates.
Figure 2: Frequency of S&P Global Ratings issuer credit rating change within 12 months from the date of a detected RiskGauge score divergence.
Source: S&P Global Market Intelligence, for illustrative purposes. Based on a sample of 1,578 public corporate companies (excluding financial institutions and real estate sectors). As of October 14th, 2022.
If you would like to learn more about RiskGauge, and how you can create early warning signals of credit quality deterioration and improvement, click here.
 The index incorporates a wide range of indicators, including measures of primary market issuance and pricing, secondary market pricing and liquidity conditions, and the relative pricing between traded and nontraded bonds. Source: New York Federal Reserve, 2022 (available at www.newyorkfed.org/research/policy/cmdi#/overview).
 S&P Global Ratings does not contribute to or participate in the creation of credit scores generated by S&P Global Market Intelligence. Lowercase nomenclature is used to differentiate S&P Global Market Intelligence PD scores from the credit ratings used by S&P Global Ratings.
 Discriminatory power refers to the ability of the model to correctly rank companies, assigning worse scores to future debt defaulters. The figure refers to RG’s Receiver Operating Characteristic, a common measure of discriminatory power.
 “RiskGauge Model from S&P Global Market Intelligence, S&P Global Market Intelligence (April 2021).