COVID-19 Impact on UK Firm Credit Risk

In this paper, we examine the impact of the coronavirus and the concurrent shock in oil prices on UK firms to identify which sectors have the greatest credit deterioration. Using Moody’s Analytics probability of default models, we see credit deterioration across firms of all sizes and industries. 

Construction and Services sectors see the largest increase in default risk, while industries already showing some signs of weakness before COVID-19 continue to outpace other sectors in terms of absolute default risk. These include Healthcare and Transportation. Average size-weighted expected loss across industries increase by nearly 80%. 

The as-of date for this analysis is April 7, 2020. In addition, this study does not consider potential government aid. 

Executive summary 
The purpose of this exercise is to identify which sectors can expect to sustain the greatest impact from COVID-19–related credit stressors. We performed this analysis by running firms’ financial statements through default risk models and comparing the following three risk measures:  

1. Pre-COVID-19 point-in-time (PIT) probability of default (PD). This measure reflects the lower-risk credit cycle conditions just before the coronavirus pandemic (end of January market information).
2. COVID-19 PIT PD. This point-in-time probability of default risk measure includes the economic stress caused by the coronavirus pandemic and oil price shocks per the as-of date for this analysis.
3. Through-the-cycle (TTC) PD. This measure captures the risk level associated with firms’ financial ratios without any consideration of prevailing credit cycle conditions.

We compare these three measures to evaluate how COVID-19 has changed point-in-time probability of default measures from pre-COVID-19 levels (Pre-COVID-19 vs COVID-19 PD) and to what extent COVID-19 point-in-time probability of default measures exceed through-the-cycle probability of default measures (TTC PD vs COVID-19 PD). We perform this analysis at an industry level and at the risk-band level within industries. 

This paper identifies which industries COVID-19 affects most and with what magnitude. We do not stress financials for individual firms because we expect that market reactions captured by the PIT PD reflect potential future changes in financials.  

Data 
For this study, we selected all of the UK firms with recent financial statements available from our database. The sample includes over 88,000 UK firms. The chart on the left in Exhibit 1 presents the distribution of firms, which span all the industry sectors in the model. The Unassigned sector is for firms that either do not have an industry identifier or are in an industry that does not map to the Moody’s Analytics RiskCalc™ model’s sectors. The chart on the right shows the firms’ geographic location. London contributes more than 20% of the firms in the sample, though all regions are well-represented. 

Analysis and results 

Methodology 
The analysis focuses on the impact of COVID-19 and the oil price shocks on default risk for UK borrowers. The model uses financial statement data and associated default observations on historical data samples with defining characteristics similar to those of the data samples used in this exercise. We evaluate firms by their PD measure. The model produces through-the-cycle default risk measures using only firms’ financial statements without any consideration of the prevailing credit cycle conditions. The model also produces point-in-time default risk measures that account for the credit cycle by translating public equity market signals. The model also translates PD into a PD-implied rating.¹  

We compare these three measures to evaluate how COVID-19 has changed point-in-time probability of default measures from pre-COVID-19 levels and to what extent COVID-19 point-in-time probability of default measures exceed through-the-cycle probability of default measures. We perform this analysis at an industry level and at the risk-band level within industries. 

Results 
Construction and Services see the greatest increase in risk, while Healthcare and Transportation remain the riskiest sectors 
Exhibit 3 identifies increased PIT PDs and implied PIT ratings across all industries from the pre-COVID-19 PIT PD to the COVID-19 PIT PD. The pre-COVID-19 PIT is the point-in-time probability of default risk measure that reflects the lower-risk credit cycle conditions just before the coronavirus pandemic. The COVID-19 PIT PD includes the economic stress caused by the coronavirus and oil price shocks. The Construction and Services sectors have the greatest average increase due to COVID-19. Agriculture and Mining see the smallest average PIT PD movement. All sectors see downgrade pressure. Construction sees the largest rating downgrade for its firms, 1.63 rating grades on average, while on the lowest end, Mining sees a 0.42 rating grade downgrade on average (for example, one rating grade change would be from Baa2 to Baa3). 

Exhibit 3 shows how the pandemic drives change in the PIT PD and PIT PD-implied rating on an industry level. We further break down the impact by rating grades. Exhibit 4 is a further breakdown of Exhibit 3. Exhibit 4 takes a firm whose financial ratios imply a TTC PD rating of Baa2 (~0.35% PD) and then applies the pre-COVID-19 credit cycle adjustment and the COVID-19 credit cycle adjustment of each industry to see how COVID-19 affects default probability at different starting risk levels. The pre-COVID-19 PIT PD shows that before the coronavirus, some industries were in a stronger credit cycle than others. Following the onset of COVID-19, all industries suffer an increase of credit risk per the COVID-19 PIT PD, although it maintains nearly the same rank ordering as before. Healthcare and Transportation see their default risk increase and remain the riskiest sectors. Construction and Utilities jump from lowest risk sector while the Mining and Agriculture sectors’ risk moves to the lowest position (though at a riskier position than before the COVID-19 outbreak).  

The table on the right in Exhibit 4 measures the PD movement for each sector from the TTC to the COVID-19 PIT PD. Users can apply this percentage change to their TTC PD to approximate the impact of the coronavirus on their portfolio. Exhibit 4 also measures the PD movement for each sector from the pre-COVID-19 PIT PD to the COVID-19 PIT PD. Users can apply this percentage change to their pre-COVID-19 PIT PD or equivalent point-in-time probability of default measure to approximate the impact of COVID-19 on their portfolio. For example, if you have an agriculture firm that has a risk profile similar to a Baa2 risk rating with a TTC PD of 0.35%, you can multiply that 0.35% TTC PD by (1 + 33%) to get a point-in-time default risk measure of 0.46% that incorporates the coronavirus credit cycle stressors. If you have an agriculture firm that has a pre-COVID-19 PIT PD of 0.36%, you can multiply that 0.36% PIT PD by (1 + 28%) to adjust the point-in-time default risk measure for COVID-19, resulting in a COVID-19 PIT PD of 0.46%. Appendix II: Expected Movements in PIT PD Due to COVID-19 presents tables with percentage change adjustments for each industry and implied-rating grade combination. 

Impact on expected losses 
To assess potential impacts on expected losses, we assume a constant loss given default (LGD) of 50% and multiply it by our monthly weighted average PIT PDs. Exhibit 5 charts the weighted average expected loss rates beginning in January 2019 through April 7, 2020. The final datapoint shows the COVID-19 PIT PD based on the latest coronavirus and oil price shock credit cycle information. We see expected loss rates increase by nearly 80%. 

Conclusion 
The recent global coronavirus contagion and the drop in oil prices has shocked firms across all sectors and sizes. Month-over-month, we typically do not expect many PIT risk rating grade changes—and if there are changes, they are typically not more than a one-rating grade upgrade or downgrade. Exhibit 6 illustrates that COVID-19 and oil price shocks have, in general, downgraded borrowers by one or two rating grades across sectors and firm sizes. Although their default probability is increasing, roughly 10% of borrowers see no rating grade impact. More than 55% of borrowers see a one-grade downgrade, roughly 30% see a two-grade downgrade, and around 1% see a three-grade downgrade. 

Construction and Services sectors see the largest increase in default risk, while industries already showing some signs of weakness before COVID-19 continue to outpace other sectors in terms of absolute default risk. This includes Healthcare and Transportation. 

For our analysis, we incorporate the public equity market signals through April 7, 2020. We continue to monitor equity market response to COVID-19 in the United Kingdom that may influence the PIT PD. 

Appendixes 

Appendix I: Credit Cycle Adjustment Overview 
The credit cycle adjustment (CCA) model produces our PIT risk measures. It accounts for the credit cycle by combining the distance-to-default (DD). DD factors for a given month are obtained in the beginning of the next month. Data goes through quality checks and additional transformations to be released in the CCA for the following month. For this exercise, we have updated the CCA to reflect data as of April 7, 2020. 

Exhibit 7 illustrates the progression of the through-the-cycle default risk measure through our financial statement only (FSO) model (TTC PD model) before applying the adjustment to produce the point-in-time CCA default risk measure. The CCA model demonstrates how the current market signals compare to historical market signals. If the current credit environment is better than the historical average, we adjust the FSO Expected Default Frequency (EDF™) down to arrive at a less risky CCA EDF. If the current credit environment is worse than the historical average, we adjust the FSO EDF up to arrive at a riskier CCA EDF. EDF is Moody’s Analytics nomenclature for probability of default. 

Appendix II: Expected Movements in PIT PD Due to COVID-19 
If you are using the RiskCalc CCA EDF (PIT PD) with an analysis date in March, the CCA EDF is using credit cycle information from the end of January. Exhibit 8 shows the expected change in CCA EDF when using the latest credit market signals as of April 7, 2020. These tables help users bridge the gap to the latest coronavirus and oil price shocks.

Exhibit 9 identifies the expected change in CCA EDF (PIT PD) relative to the FSO EDF (TTC PD) when using the latest credit market signals as of April 7, 2020.