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Alternative Approaches for Assessing Credit Risk Using Consensus Estimates

Evaluating the Credit Risk of SMEs and the Impact of a Global Pandemic

Machine Learning and Credit Risk Modelling

Estimating Credit Losses Under COVID-19 and the Post-Crisis Recovery

Climate Change: Energy Transition Risks and Opportunities for European Public Companies’ Creditworthiness

Alternative Approaches for Assessing Credit Risk Using Consensus Estimates

The unprecedented times caused by the COVID-19 pandemic have shown that traditional credit risk analysis that uses the latest published financials for companies creates challenges in capturing the true impact of the pandemic on the economy and businesses. As an alternative, analysts can use consensus data from equity analysts to add independent views of the pandemic’s impact on businesses within each industry. For example, as shown in Figure 1, within the consumer discretionary sector, consensus estimates show negative revenue changes for 2020 over 2019 for most industries due to the pandemic. Only four industries in the sector had positive revenue changes on a year-over-year basis, with internet and direct marketing retail benefiting the most, posting a 34% increase.

Figure 1: Consensus estimates of revenue changes by the end of 2020 compared to the end of 2019 for different industry groups within the consumer discretionary sector.


Consensus estimates are an average value of forecasts for public company’s projected financials based on the combined estimates and assessments by analysts that cover the stock. These estimates depend on a variety of factors: company financials, plus the business, socio-economic and political environment. These estimates can be used as an alternative to the published historical financials as inputs into credit risk models, such as S&P Global Market Intelligence’s Credit Analytics credit risk models (e.g., Probability of Default Model Fundamental (PDFN) and CreditModelTM). This alternative approach enables the inclusion of the pandemic impact on companies when calculating counterparties’ credit risk, as shown in Figure 2.

Figure 2: Consensus estimates of revenue changes and PD changes by the end of 2020 compared to the end of 2019, for different industry sectors.



Moreover, this approach of integrating current market conditions in credit risk is an important component for PD and loss given default (LGD) assessments within the International Financial Reporting Standard (IFRS) 9 and Current Expected Credit Losses (CECL) accounting frameworks.

In a world of constant change, the availability of alternative approaches and data can be vital for firms in managing exposures and credit risk. S&P Global Market Intelligence offers solutions that can help measure the impacts of political, macroeconomic and social conditions on credit risk analysis.

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