Pricing Risk in Unrated Companies

Credit Ratings play a critical role in global capital markets. At their foundation, the ratings are an opinion on credit risk, specifically the willingness and capacity of the underlying borrower to repay its creditors. These ratings influence debt pricing, capital allocation, regulatory framework, and access to capital market. While the importance of ratings is profound, their absence poses a significant hurdle for investors. S&P Global Ratings has the highest global corporate¹ coverage standing at ~8350 issuers² of which ~2300 are public companies. Currently the number of publicly listed companies globally stands at ~46000³, and less than ~5% of those have a publicly available rating from S&P Global Ratings.

In the absence of ratings for such a large chunk of the global market cap, investors rely on quantitative models, market signals and internal risk rating framework to determine the default risk associated with such companies. For this effort, they must rely on multiple data sets that take sizeable resources and time to accurately build, stress-test and validate these models. While they may have strong market applicability, their predictive power is called into question if they do not incorporate accurate default data. This paper provides information on S&P Global Market Intelligences’ fundamental and market-driven credit models that provide a lower-case letter grade output that have large applicability to these unrated public companies and can serve as a strong proxy for missing agency ratings.

Key Takeaways

S&P Global Market Intelligence’s Credit Analytics consists of a powerful suite of fundamental & market driven credit models that provide point-in-time and through-the-cycle risk assessments that help evaluate the creditworthiness of unrated public companies.

Model Accuracy & Performance

CreditModel^TM

• Trained on 10+ years of S&P Global historical data across Corporates, Banks (from 2001) and Insurance (from 2005), CM Corporates (North America & EU sub-models) score within 2 notches of actual S&P Global Ratings 89% of the time. CM Financial Institutions (Banks) performs even stronger at 94% within 2 notches, while CM Insurance achieves 93%, demonstrating exceptional accuracy for financial sector entities.

Probability of Default Fundamentals (PDFN)

• Optimally suited for public and private companies with less than $50M in revenue, a segment largely underserved by traditional rating coverage.  Calibrated on 15+ years of default data the 2024 validation confirms excellent discriminatory power across both private and public corporate universes.

Probability of Default Market Signals  (PDMS)

• Covers 86,000+ public companies globally, providing daily-updated, point-in-time credit risk signals derived from a structurally enhanced Merton model framework.

Investment Management Use-Case

The models collectively support two primary investment management applications across the publicly unrated universe:

Credit Protection

• PDMS early warning signals allow portfolio managers to identify credit deterioration in unrated holdings ahead of formal rating actions enabling proactive hedging, position reduction, or covenant monitoring.
• The Accendra Health case⁴ illustrated a 12-month early warning window before a 2-notch downgrade, providing sufficient lead time for risk mitigation.
• Persistent 3+ notch spreads between PDMS Implied Scores and official ratings can be systematically integrated into credit surveillance workflows to trigger alerts.

Alpha Generation

• CreditModel's shadow ratings for the unrated universe enable investment managers to identify undervalued credits, companies with strong fundamental credit scores but no formal rating and potentially wider spreads.
• With pre-scored data dating back to 2002, index benchmark and country/industry benchmarks allow relative value analysis across equity indices, sectors, and geographies.

Fundamental & Market-Driven Credit Models

CreditModel^TM

CreditModel (CM) is a widely used statistical model trained on credit ratings from S&P Global Ratings⁵. It facilitates an easy, efficient, and cost-effect evaluation of  a company’s credit quality by generating credit scores for global corporates. This model has been trained on more than 10 years of S&P Global Ratings’ historical ratings for Corporates, Banks⁶ & Insurance companies. The below tables shows the performance metrics for CM Corporates, CM Financial Institutions⁷

Table 1. CM Corporate Performance

Table 2. CMFI Banks Performance

Table 3. CMFI Insurance Performance

In the tables above, the third column (Within 2 notches) is considered a standard of performance measure. For CM Corporates, across North American & EU sub-models the performance measure sits at 89%, which means that 89% of the time CM Corporates will produce a lower-case credit score that sits within 2 notches of an actual S&P Global Rating. Similarly, for CMFI Banks & Insurance, the model is even stronger, sitting at an overall 94% and 93%, respectively.

Synopsys Inc. (NasdaqGS:SNPS)

Synopsys provides software and hardware used to validate the electronic systems that incorporate chips and the software that runs on them. Synopsys was assigned a new issuer credit rating of BBB on Feb 27^th, 2025, by S&P Global Ratings. Before the official S&P Issuer Credit Rating, CreditModel had a lower-case credit score available for this company and that was bbb. In the snippet below, it can be observed that the CM lower-case credit score sits at bbb based on Oct 31, 2024, financials and also the historical scores that scored the company at bbb before the S&P Global Rating assigned its issuer credit rating of BBB.

Pinnacle Financial Partners, Inc. (NYSE:PNFP)

Pinnacle Financial Partners, Inc. operates as the bank holding company for Pinnacle Bank that provides various banking products and services. On Jan 1 , 2026, Pinnacle Financial Partners Inc. (Pinnacle) merged with Synovus Financial Corp. (Synovus), with Pinnacle Financial Partners and Pinnacle Bank the surviving entities. S&P Global Rating assigned a long-term issuer credit rating of BBB- to Pinnacle Financial Partners, Inc. and BBB to Pinnacle Bank. CreditModel had Pinnacle scored at bbb+ before the official S&P issuer credit rating was assigned, which put the credit score within 2 notches of the issuer credit rating (BBB-) assigned to Pinnacle Financials Partners and within 1 notch of the issuer credit rating (BBB) assigned to Pinnacle Bank. In the snippet below, it can be observed that the CreditModel scores Pinnacle Financial at bbb+ based on the Dec 31 financials, and it also presents the historical scores going back a year at bbb+.

Probability of Default Model Fundamentals (PD Fundamentals)

PD Fundamental is a quantitative fundamental driven model that identifies potential default by assessing financial and business risk. The model is calibrated on 15+ years of defaults⁸ and yields a PD term structure with time horizons ranging from one month to 10+ years. PD fundamental incorporates both financial risk and business risk variables to generate the overall PD value. This approach captures the important credit risk drivers from S&P Global Ratings corporate analytical methodology in a statistical PD model and provides users with a well-rounded measure of credit risk where different sources can be easily identified. This is the optimum model for public and private companies with less than $50M in revenue.

An important aspect of the PD assessment system is its ability to correctly rank order default risk. Discriminatory power analysis assesses how well a model discriminates between different scores, if existent, and tests the ordinal ranking of the PD values. For these purposes, Accurate Ratio (AR) and ROC are utilized and the results are documented below for both private and public corporates. The below results showcase excellent discriminatory power in the PDFN model as per the 2024 validation.

Table 4. PDFN Private Corporates

Table 5. PDFN Public Corporates

PD Model Market Signals (PDMS)

PD Model Market Signal is a market-driven credit risk model developed by S&P Global Market Intelligence. It builds on a structural model proposed by Merton[1], with several enhancements and refinements. It provides a point-in-time view of credit risk for 86,000+ public companies and provides timely flags to anticpate creditworthiness deteriorations or defaults with high model reliability due to good discriminatory power in differentiating defaulters from non-defaulters. The below graphs show how the dynamic nature of PDMS can provide timely flags to anticipate credit deterioration.

Accendra Health (NYSE:ACH)

Accendra Health operates as a healthcare solutions company that received a 2 notch downgrade by S&P Global Ratings on Jan 17th, 2026. PDMS was able to forecast a credit deterioration and provided early warning signals a year prior to the actual rating downgrade.

Table 6. Credit Score Spread

In the graph above, the difference between S&P Issuer Credit Rating (Green) and PDMS Implied Score (Red)  is 4 notches which is a large spread and it remained constant a year prior to the company receiving a 2 notch downgrade to B from BB-.  Post-downgrade the spread has gone back to a 2-3 notch difference which is acceptable but anything more than that serves as a distress signal. Furthermore, looking at the PDMS Implied Score between Accendra Health and its Industry benchmark in the below graph, it can be observed that at the time of the issuer credit rating downgrade, there was a 4 notch spread between the company and the industry’s PDMS implied score.

Table 7. Company & Industry Implied Score 

Delivery Mechanism

CreditAnalytics^® models are available for delivery through both Xpressfeed and Snowflake.