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Does Size Matter? Incorporating company size in credit risk assessment

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Does Size Matter? Incorporating company size in credit risk assessment

This article is written and published by S&P Global Market Intelligence, a division independent from S&P Global Ratings. Lowercase nomenclature is used to differentiate S&P Global Market Intelligence credit scores from the credit ratings issued by S&P Global Ratings.

Company size, which is commonly measured in either revenue, assets, book value of equity or market capitalization, is frequently considered to be one of the key factors in assessing credit risk. Small companies are often more flexible and streamlined in their operations and decision-making than large companies and, thus, may be capable of effectively providing products and services for market segments that are too difficult for large companies to reach, or that are not sufficiently profitable. However, the lack of diversification in revenue sources and poorer access to capital markets for financing purposes reduce small companies’ strength to withstand turbulence in the business environment. There is numerous empirical evidence showing that small companies are generally less creditworthy and more prone to default than large companies. For example, Figure 1 illustrates that small- and medium-sized enterprises (SMEs) where annual turnover is less than €50 million have higher overall default rates in various countries.

Figure 1: 2021 Q2 median default rates of Internal Ratings-Based approach (IRB) bank portfolios from European Banking Authority supervisory reporting

Source: European Banking Authority. As of November 2, 2021. For illustrative purposes only.

Company size plays an important role in S&P Global Ratings credit risk assessments. S&P Global Ratings has no minimum size criterion for any given rating level. In assessing financial flexibility, market position and diversity of an issuer, relative company size is a key analytical focus.[1] In an empirical analysis, company size (measured by total reserves) was found to be strongly related to ratings for U.S. oil and gas exploration and production companies.[2]

At S&P Global Market Intelligence, company size variables are frequently selected as inputs within our suite of Credit Analytics quantitative models. These include market- and fundamentals-driven analytical models that deliver probabilities of default (PDs) and credit scores that are designed to broadly align with ratings from S&P Global Ratings. PD Model Fundamentals provides an innovative approach to assessing potential default by looking at financial risk and business risk to measure the likelihood of default of publicly listed and privately owned corporations and banks, with no revenue and asset size limitation. CreditModel™, on the other hand, is a scoring model that covers medium and large corporations (with total revenue above $25 million U.S.), and banks and insurance companies (with $100 million or more in total assets).

Total revenue is selected in PD Model Fundamental 2.0 – Private Corporates (PDFN 2.0 Private), while total equity is used in PD Model Fundamentals 2.0 – Public Corporates (PDFN 2.0 Public). Various size variables are included in sub-models of CreditModel 3.0 (CM 3.0).

During the development of our Credit Analytics statistical models, one of the vital steps was variable selection in which the variable candidates were tested for their univariate predictive power, as well as their monotonic relationship with credit risk. In PDFN 2.0 Private, we grouped the training data into 10 equally populated buckets by the companies’ total revenue during the variable selection step. Figure 2 shows the distribution of default samples among the 10 buckets. We can see a strong negative correlation between company size and default, especially when the total revenue is below $50 million.

Figure 2: Distribution of default samples by company size in PDFN 2.0 Private training data

Source: S&P Global Market Intelligence. As of November 2, 2021. For illustrative purposes only.

Similarly, in PDFN 2.0 Public we placed the training data into 10 equally populated buckets by the companies’ total equity. A strong negative correlation between company size and default is shown in Figure 3.

Figure 3: Distribution of default samples by company size in PDFN 2.0 Public training data

Source: S&P Global Market Intelligence. As of November 2, 2021. For illustrative purposes only.

In addition to selecting company size variables as model inputs, PDFN 2.0 further adjusts the model output (i.e., the PD) by company size dependent parameters such that the model output is aligned with external benchmarks.

We compared the PFDN 2.0 credit scores of rated non-financial corporates as at 12/31/2019 with the issuer credit rating by S&P Global Ratings. Figure 4 confirms the relationship between company size and credit risk is successfully captured by PDFN 2.0.

Figure 4: Average PDFN 2.0 credit scores as at 12/31/2019 of rated non-financial corporates by company size[3]

Source: S&P Global Market Intelligence. As of November 2, 2021. For illustrative purposes only.

Company size is one of the important elements for credit risk assessment. We can easily find empirical evidence showing small companies have a higher default risk. At S&P Global Market Intelligence, we have statistically tested the relationship between company size and default risk for both public and private companies and have carefully crafted PDFN 2.0 to capture this average relationship. For users who focus on a credit portfolio of small companies with strictly low default rates, we have also developed a scaling tool that provides the flexibility to remap the PDFN outputs such that the average model output of those companies is aligned to the user's historically observed default rates and the specific characteristics of the portfolio.

Learn more about S&P Global Market Intelligence’s Credit Analytics models.



[1] S&P Global Ratings, “Australasian Corporate Ratings: Does Company Size Matter?”, March 29, 2000.

[2] S&P Global Ratings, “Company Size Is Material for U.S. Oil and Gas Exploration and Production Ratings”, January 8, 2004.

[3] aaa=1, aa+=2, aa=3, aa−=4, bbb+=5, etc.

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