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Companies And Sectors Most Impacted By U.S.-Chinese Tariffs

2019 Credit Risk Perspectives: Is The Credit Cycle Turning? A Fundamentals View

2019 Credit Risk Perspectives: Is The Credit Cycle Turning? A Market Driven View

Flying Into The Danger Zone; Norwegian Air Shuttle

Sears Strikes Out What Is In Store For Other Retailers In The US

Credit Analysis
Companies And Sectors Most Impacted By U.S.-Chinese Tariffs

Highlights

President Trump’s proposed tariffs impacted the short-term market perceived credit quality of U.S. firms more than Chinese ones

May. 07 2018 — Written by Camilla Yanushevsky, with analysis contributions from Paul Bishop and Jim Elder, Directors of Risk Services, Melissa Doscher, Senior Manager, Risk Services, and Chris Rogers, Panjiva Research Director.

Consumer confidence soared to an 18-year high in February, on the tailwinds of the passage of the most sweeping tax rewrite in over 30 years at the end of 2017. But now, with U.S. President Donald Trump’s ramp up of protectionist rhetoric and heightened concerns of a global trade war, the optimism has begun to diminish. The Conference Board Consumer Confidence Index declined to 127.7 in March, from the high of 130.0 in February, with many pointing to President Trump’s tariffs as playing a major role for the drop off. [i] Companies have already started to examine the potential impact to their supply chains and are reevaluating the way they conduct business. Although the implementation details of the President’s tariffs have yet to be provided, we went ahead and evaluated the levies’ potential market implications.

U.S. tariff announcements have occurred 31 times in the last 35 years, according to an S&P Global Market Intelligence analysis using Kensho, provider of next-generation analytics and data visualization systems, which was recently acquired by S&P Global. On a rolling quarterly basis, following the announcement, the S&P 500 increased, on average, by 2.79%, trading positively more than 78% of the time. Energy stocks tended to be the bottom performing among the S&P 500 sectors, while S&P 500 Information Technology and S&P 500 Consumer Discretionary companies posted slight positive returns for the quarter.

Figure 1: S&P 500 average return and percent of trades positive after U.S. tariff announcement
S&P 500 average return and percent of trades positive after U.S. tariff announcement

Following President Trump’s March 22, 2018 signing of an executive memorandum to impose regulatory tariffs on up to $60 billion in Chinese products belonging to the aerospace, information and communication technology, and machinery industries, among others, we examined and highlighted notable sector, industry, and company-level probability of default (PD) changes as indicated by our PD Market Signal Model, a structural model that calculates the likelihood of a company defaulting on its debt or entering bankruptcy protection over a one- to five-year horizon.[ii]

U.S. Financials, Energy companies among the biggest losers

Following the memorandum signing, the U.S. Financials sector saw the largest escalation in market-perceived credit risk. The sector’s PD increased 29.32% from 0.39% on March 21, 2018 to just under 0.50% on March 29, 2018, nearly crossing into a speculative grade equivalent (bb+) median credit score for the sector. [iii]

While not directly impacted by President Trump’s tariffs, diversified banks and investment banking and brokerage companies are reexamining their business investment and lending decisions due to the levies’ potential negative repercussions on economic growth.

According to an analysis conducted by the Tax Foundation: “$37.5 billion in tariffs would lower GDP and wages 0.1 percent, lower employment by the equivalent of 79,000 fewer full-time jobs in the long run, and make the US tax burden less progressive.” [iv] On such concerns, as well as the possibility of retaliation by other countries, fund managers have already begun to reduce their U.S. holdings and look for opportunities overseas. [v]

President Trump’s proposed tariffs also dealt a significant blow to the U.S. Energy sector, which relies heavily on steel and aluminum for various projects, including pipeline construction and wind and solar power installation. Following the announcement, the U.S. Energy’s PD jumped 25.15%, from 1.56% on March 21, 2018 to 1.95% on March 29, 2018.

President Trump’s proposed tax on steel and aluminum imports will not only raise the costs of these projects and drive up prices for consumers, but in the long run can also reduce the demand for clean energy, while harming the quest for ‘American energy dominance’ in the process.

Figure 2: U.S. 1-week median Market Signal Probability of Default change by GICS sector (%)

U.S. 1-week median Market Signal Probability of Default change by GICS sector (%)

Taking a deeper dive into subsectors, aluminum, a subset of Materials, saw the largest increase in PD of 120.71%. Copper, another subset of Materials, also saw a substantial incline in PD of 120.54%. Both these important industrial metals were singled out on the President’s proposed list of tariff targets. [ii]

Figure 3: U.S. largest increases in 1-week Market Signal Probability of Default by industry (%)

U.S. largest increases in 1-week Market Signal Probability of Default by industry (%)

China’s Consumer Discretionary sector takes a blow

Chinese consumer discretionary companies also are bearing the brunt of the looming trade war, with President Trump’s tariffs targeting a range of consumer goods from China including flat screen televisions, household appliances, and auto parts. Immediately following the announcement, the sector observed the largest market-perceived escalation in credit risk. The sector saw its PD increase 8.09% from 1.82% on March 21, 2018 to 1.96% on March 29, 2018.

President Trump’s tariffs also carry far-reaching implications on China’s property market, which after two stellar years of property sales and developer margins, is seeing a toughening of industry conditions — tighter lending rules, restrictive policies to control price appreciation, and intensifying competition. [vi]

Fears of faster-than-expected rate hikes and inflation growth spiraling from the tariff battle does not bode well for Chinese developers looking for capital overseas. Following the signing of the March 22, 2018 memorandum, China’s real estate sector observed a PD uptick of 6.4%, from 0.93% on March 21, 2018 to 0.99% on March 29, 2018.

Figure 4: China 1-week median Market Signal Probability of Default change by GICS sector (%)

China 1-week median Market Signal Probability of Default change by GICS sector (%)

On a subsector level, China’s property and casualty insurance, a subset of Financials, observed the largest one-week escalation in credit risk with its PD jumping 133.9% from 0.23% to 0.53%. The industry’s PD uptick is likely a ‘spillover’ of the tightening of the credit markets for property developers to the insurers offering project assurance.

Figure 5: China largest increases in 1-week Market Signal Probability of Default by industry (%)

China largest increases in 1-week Market Signal Probability of Default by industry (%)

Tariff headwinds hit both sides

On a company-level, roughly 65% of U.S. and 58% of Chinese publicly traded companies experienced an increase in their one-year PD the week following the announcement. U.S. companies saw a larger escalation in credit risk, with a median PD change of 13%, compared to China’s 3%. Companies with U.S./China cross-border exposure were also more likely to see an increase in credit risk.

Figure 6: 25 largest increases in 1-week Market Signal Probability of Default by U.S. S&P Global Market Intelligence-covered companies with exposure to China (%)

25 largest increases in 1-week Market Signal Probability of Default by U.S. S&P Global Market Intelligence-covered companies with exposure to China (%)

Figure 7: 25 largest increases in 1-week Market Signal Probability of Default by Chinese S&P Global Market Intelligence-covered companies with exposure to the U.S. (%)

25 largest increases in 1 week Market Signal Probability of Default by Chinese S&P Global Market Intelligence

Some U.S. companies uneasy over China tariff threat to supply chains

Considering the complexity of international supply chains, many market participants are on edge that new tariffs might have damaging unintended consequences. According to supply chain market intelligence firm Panjiva Inc., which was recently acquired by S&P Global:

“The targeting [striking] of China’s duties is significantly more focused than those introduced by the U.S., with 106 categories compared to 1333 in America’s section 301 duties. They are also more focused in terms of products, with the top three products accounting for 71.7% of total product coverage. Those include aircraft (HS 8802.40, worth $14.05 billion, or 26.3% of the total, soybeans (HS 1201.90 worth $13.96 billion) and midsize engine cars (8703.23, $10.32 billion).

The inclusion of soybeans is particularly notable given that the promotion of imports were a part of the package of trade enhancements announced when President Trump visited China in November 2017.” [vii]

Figure 8: Focused strike on politically important U.S. products

Focused-strike-on-politically-important-U.S.-products

In summary, our PD Market Signal model shows that President Trump’s proposed tariffs impacted the short-term market perceived credit quality of U.S. firms more than Chinese ones. While the trade penalties have yet to be implemented, we saw steep tariffs and protectionism policies spur declines in global trade in the 1930s, stifle economic growth, and contribute to the depth of the Great Depression. More recently, we saw trade fears trigger volatility in global equities. Likewise, President Trump’s tariffs will likely create similar supply and demand imbalances, while boosting prices for consumers, increasing costs for manufacturers, and potentially exacerbating trade tensions with other countries. Companies, as well as individuals, should be especially alert as the negotiations play out.

This report was updated on May 15, 2018 to add the last two columns, Implied Credit Score and S&P Rating/Outlook, to Figures 6 and 7, as well as to clarify that the companies listed have reported revenue exposure to China on a consolidated basis.

[i] The Conference Board Consumer Confidence Index Declined in March (March 27, 2018). Retrieved April 25, 2018, from https://www.conference-board.org/data/consumerconfidence.cfm

[ii] Notice of Determination and Request for Public Comment Concerning Proposed Determination of Action Pursuant to Section 301: China’s Acts, Policies, and Practices Related to Technology Transfer, Intellectual Property, and Innovation (n.d.). Retrieved April 25, 2018, from https://ustr.gov/sites/default/files/files/Press/Releases/301FRN.pdf

[iii] Mapping Letter Grade Score to Probability of Default Technical Reference Guide. Published November 2017.

[iv] Modeling the Impact of President President Trump’s Proposed Tariffs (April 12, 2018). Retrieved April 25, 2018, from https://taxfoundation.org/modeling-impact-president-President Trumps-proposed-tariffs/

[v] President Trump’s tariffs prompting some U.S. fund managers to look overseas. (March 9, 2018). Retrieved April 25, 2018, from https://www.reuters.com/article/us-usa-stocks-weekahead/President Trumps-tariffs-prompting-some-u-s-fund-managers-to-look-overseas-idUSKCN1GL1KV

[vi] China’s Developers Strengthen Defense for A Funding Crunch (April 22, 2018). Retrieved April 25, 2018, from S&P Global Ratings.

[vii] Four Facts About China’s $53 Billion President Trump Tariff Retaliation (April 5, 2018). Retrieved April 25, 2018, from Panjiva Inc.

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Credit Analysis
2019 Credit Risk Perspectives: Is The Credit Cycle Turning? A Fundamentals View

Mar. 15 2019 — On November 20, 2018, a joint event hosted by S&P Global Market Intelligence and S&P Global Ratings took place in London, focusing on credit risk and 2019 perspectives.

Pascal Hartwig, Credit Product Specialist, and I provided a review of the latest trends observed across non-financial corporate firms through the lens of S&P Global Market Intelligence’s statistical models.1 In particular, Pascal focused on the outputs produced by a statistical model that uses market information to estimate credit risk of public companies; if you want to know more, you can visit here.

I focused on an analysis of how different Brexit scenarios may impact the credit risk of European Union (EU) private companies that are included on S&P Capital IQ platform.

Before, this, I looked at the evolution of their credit risk profile from 2013 to 2017, as shown in Figure 1. Scores were generated via Credit Analytics’ PD Model Fundamentals Private, a statistical model that uses company financials and other socio-economic factors to estimate the PD of private companies globally. Credit scores are mapped to PD values, which are based on/derived from S&P Global Ratings Observed Default Rates.

Figure 1: EU private company scores generated by PD Model Fundamentals Private, between 2013 and 2017.

Source: S&P Global Market Intelligence.2 As of October 2018.

For any given year, the distribution of credit scores of EU private companies is concentrated below the ‘a’ level, due to the large number of small revenue and unrated firms on the S&P Capital IQ platform. An overall improvement of the risk profile is visible, with the score distribution moving leftwards between 2013 and 2017. A similar picture is visible when comparing companies by country or industry sector,3 confirming that there were no clear signs of a turning point in the credit cycle of private companies in any EU country or industry sector. However, this view is backward looking and does not take into account the potential effects of an imminent and major political and economic event in the (short) history of the EU: Brexit.

To this purpose, S&P Global Market Intelligence has developed a statistical model: the Credit Analytics Macro-scenario model enables users to study how potential future macroeconomic scenarios may affect the evolution of the credit risk profile of EU private companies. This model was developed by looking at the historical evolution of S&P Global Ratings’ rated companies under different macroeconomic conditions, and can be applied to smaller companies after the PD is mapped to a S&P Global Market Intelligence credit score.

“Soft Brexit” (Figure 2): This scenario is based on the baseline forecast made by economists at S&P Global Ratings and is characterized by a gentle slow-down of economic growth, a progressive monetary policy tightening, and low yet volatile stock-market growth.4

Figure 2: “Soft Brexit” macro scenario.5

Source: S&P Global Ratings Economists. As of October 2018.

Applying the Macro-scenario model, we analyze the evolution of the credit risk profile of EU companies over a three-year period from 2018 to 2020, by industry sector and by country:

  • Sector Analysis (Figure 3):
    • The median credit risk score within specific industry sectors (Aerospace & Defense, Pharmaceuticals, Telecoms, Utilities, and Real Estate) shows a good degree of resilience, rising by less than half a notch by 2020 and remaining comfortably below the ‘b+’ threshold.
    • The median credit score of the Retail and Consumer Products sectors, however, is severely impacted, breaching the high risk threshold (here defined at the ‘b-’ level).
    • The remaining industry sectors show various dynamics, but essentially remain within the intermediate risk band (here defined between the ‘b+’ and the ‘b-’ level).

Figure 3: “Soft Brexit” impact on the median credit risk level of EU private companies, by industry.

Source: S&P Global Market Intelligence. As of October 2018.

  • Country Analysis (Figure 4):
    • Although the median credit risk score may not change significantly in certain countries, the associated default rates need to be adjusted for the impact of the credit cycle.6 The “spider-web plot” shows the median PD values for private companies within EU countries, adjusted for the credit cycle. Here we include only countries with a minimum number of private companies within the Credit Analytics pre-scored database, to ensure a robust statistical analysis.
    • Countries are ordered by increasing level of median PD, moving clock-wise from Netherlands to Greece.
    • Under a soft Brexit scenario, the PD of UK private companies increases between 2018 and 2020, but still remains below the yellow threshold (corresponding to a ‘b+’ level).
    • Interestingly, Italian private companies suffer more than their Spanish peers, albeit starting from a slightly lower PD level in 2017.

Figure 4: “Soft Brexit” impact on the median credit risk level of EU private companies, by country.

Source: S&P Global Market Intelligence. As of October 2018.

“Hard Brexit” (Figure 5): This scenario is extracted from the 2018 Stress-Testing exercise of the European Banking Authority (EBA) and the Bank of England.7 Under this scenario, both the EU and UK may go into a recession similar to the 2008 global crisis. Arguably, this may seem a harsh scenario for the whole of the EU, but a recent report by the Bank of England warned that a disorderly Brexit may trigger a UK crisis worse than 2008.8

Figure 5: “Hard Brexit” macro scenario.9

Sources:”2018 EU-wide stress test – methodological note” (European Banking Authority, November 2017) and “Stress Testing the UK Banking system: 2018 guidance for participating banks and building societies“ (Bank of England, March 2018).

Also in this case, we apply the Macro-scenario model to analyze the evolution of the credit risk profile of EU companies over the same three-year period, by industry sector and by country:

  • Sector Analysis (Figure 6):
    • Despite all industry sectors being severely impacted, the Pharmaceuticals and Utilities sectors remain below the ‘b+’ level (yellow threshold).
    • Conversely, the Airlines and Energy sectors join Retail and Consumer Products in the “danger zone” above the ‘b-’ level (red threshold).
    • The remaining industry sectors will either move into or remain within the intermediate risk band (here defined between the ‘b+’ and the ‘b-’ level).

Figure 6: “Hard Brexit” impact on the median credit risk level of EU private companies, by industry.

Source: S&P Global Market Intelligence. As of October 2018.

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  • Country Analysis (Figure 7):
    • Under a hard Brexit scenario, the PD of UK private companies increases between 2017 and 2020, entering the intermediate risk band and suffering even more than its Irish peers.
    • Notably, by 2020 the French private sector may suffer more than the Italian private sector, reaching the attention threshold (here shown as a red circle, and corresponding to a ‘b-’ level).
    • While it is hard to do an exact like-for-like comparison, it is worth noting that our conclusions are broadly aligned with the findings from the 48 banks participating in the 2018 stress-testing exercise, as recently published by the EBA:10 the major share of 2018-2020 new credit risk losses in the stressed scenario will concentrate among counterparties in the UK, Italy, France, Spain, and Germany (leaving aside the usual suspects, such as Greece, Portugal, etc.).

Figure 7: “Hard Brexit” impact on the median credit risk level of EU private companies, by country.

Source: S&P Global Market Intelligence. As of October 2018.

In conclusion: In Europe, the private companies’ credit risk landscape does not yet signal a distinct turning point, however Brexit may act as a pivot point and a catalyst for a credit cycle inversion, with an intensity that will be dependent on the Brexit type of landing (i.e., soft versus hard).

1 S&P Global Ratings does not contribute to or participate in the creation of credit scores generated by S&P Global Market Intelligence.
2 Lowercase nomenclature is used to differentiate S&P Global Market Intelligence credit scores from the credit ratings issued by S&P Global Ratings.
3 Not shown here.
4 Measured via Gross Domestic Product (GDP) Growth, Long-term / Short-term (L/S) European Central Bank Interest Rate Spread, and FTSE100 or STOXX50 stock market growth, respectively.
5 Macroeconomic forecast for 2018-2020 (end of year) by economists at S&P Global Ratings; the baseline case assumes the UK and the EU will reach a Brexit deal (e.g. a “soft Brexit”).
6 When the credit cycle deteriorates (improves), default rates are expected to increase (decrease).
7 Source: “2018 EU-wide stress test – methodological note” (EBA, November 2017) and “Stress Testing the UK Banking system: 2018 guidance for participating banks and building societies”. (Bank of England, March 2018).
8 Source: “EU withdrawal scenarios and monetary and financial stability – A response to the House of Commons Treasury Committee”. (Bank of England, November 2018).
9 As a hard Brexit scenario, we adopt the stressed scenario included in the 2018 stress testing exercise and defined by the EBA and the Bank of England.
10 See, for example, Figure 18 in “2018 EU-Wide Stress Test Result” (EBA November 2018), found at:https://eba.europa.eu/documents/10180/2419200/2018-EU-wide-stress-test-Results.pdf

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2019 Credit Risk Perspectives: Is The Credit Cycle Turning? A Market-Driven View

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Credit Analysis
2019 Credit Risk Perspectives: Is The Credit Cycle Turning? A Market Driven View

Mar. 15 2019 — On November 20, 2018, a joint event hosted by S&P Global Market Intelligence and S&P Global Ratings took place in London, focusing on credit risk and 2019 perspectives.

Giorgio Baldassarri, Global Head of the Analytic Development Group, and I provided a review of the latest trends observed across non-financial corporate firms through the lens of S&P Global Market Intelligence’s statistical models.1 In particular, Giorgio focused on the analysis of the evolution of the credit risk profile of European Union companies between 2013 and 2017, and how this may change under various Brexit scenario; if you want to know more, you can visit here.

I started with an overview of key trends of the credit risk of public companies at a global level, before diving deeper into regional and industry sector-specific performance and pointing out some key drivers of country- and industry-level risk. Credit Analytics Probability of Default (PD) Market Signals model was used to derive these statistics. This is a structural model (enhanced Merton approach) that produces PD values for all public corporates and financial institutions globally. Credit scores are mapped to PD values, which are derived from S&P Global Ratings observed default rates (ODRs).

From January 2018 to October 2018, we saw an increase in the underlying PD values generated by PD Market Signals across all regional S&P Broad Market Indices (BMIs), as shown in Figure 1. For Asia Pacific, Europe, and North America, the overall increase was primarily driven by the significant shift in February 2018, which saw an increase in the PD between 100% to 300% on a relative basis. The main mover on an absolute basis was Latin America, which had a PD increase of over 0.35 percentage points.

Figure 1: BMI Benchmark Median credit scores generated by PD Market Signals, between January 1 and October 1, 2018.

Source: S&P Global Market Intelligence. As of October 2018.

Moving to the S&P Europe BMI in Figure 2, we can further isolate three of the main drivers behind the overall increase in PDs (this time measured on a relative basis), namely Netherlands, France, and Austria. Among these, the Netherlands had the most prominent increase between August and October. Again, one can identify the significant increase in the PDs in February, ranging from 150% to 230%, across all three countries. Towards July, we saw the spread between the three outliers shrink significantly. In August and September, however, the S&P Europe BMI began to decrease again, whilst all three of our focus countries were either increasing in risk (Netherlands, from a 150% level in the beginning of August to a 330% level at the end of September) or remaining relatively constant (France and Austria).

Figure 2: European Benchmark Median PD scores generated by PD Market Signals model, between January 1 and October 1, 2018.

Source: S&P Global Market Intelligence. As of October 2018.

In the emerging markets, Turkey, United Arab Emirates (UAE), and Qatar were the most prominent outliers from the S&P Mid-East and Africa BMI. As visible in Figure 3, the S&P Mid-East and Africa BMI saw less volatility throughout 2018 and was just slightly above its start value as of October. Two of the main drivers behind this increase were the PDs of the country benchmarks for Turkey and the UAE. Turkey, especially, stood out: the PD of its public companies performed in line with the S&P Mid-East and Africa BMI until mid-April, when it increased significantly and showed high volatility until October. On the other hand, the benchmark for Qatar decreased by over 60% between May and October.

Figure 3: S&P Mid-East and Africa BMI Median PD scores generated by PD Market Signals, between January 1 and October 1, 2018.

Source: S&P Global Market Intelligence. As of October 2018.

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We then looked at different industries in Europe. As shown in Figure 4, the main shift in S&P BMIs occurred in February, with most industries staying on a similar level for the remaining period. The main outliers were the S&P Industrials, Materials, and, in particular, Consumer Discretionary Europe, Middle East, and Africa (EMEA) BMIs. The S&P Energy BMI saw some of the highest volatility, but was able to decrease significantly throughout September. At the same time, the Materials sector saw a continuous default risk increase from the beginning of June, finishing at an absolute median PD level of slightly over 1% when compared to the start of the year.

Figure 4: S&P EMEA Industry BMI Median PD scores generated by PD Market Signals, between January 1 and October 1, 2018.

Source: S&P Global Market Intelligence. As of October 2018.

In conclusion, looking at the public companies, Latin America, Asia Pacific, and Europe pointed towards an increase of credit risk between January 2018 and October 2018, amid heightened tensions due to the current U.S. policy towards Latin-American countries, the U.S./China trade war, and Brexit uncertainty.

1 S&P Global Ratings does not contribute to or participate in the creation of credit scores generated by S&P Global Market Intelligence.

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Credit Analysis
Flying Into The Danger Zone; Norwegian Air Shuttle

Highlights

This analysis was published by S&P Global Market Intelligence and not by S&P Global Ratings, which is a separately managed division of S&P Global. This is not investment advice or a stock suggestion.

Feb. 13 2019 — The headwinds are picking up for Norwegian Air Shuttle ASA (“Norwegian”), the eighth largest airline in Europe. The carrier has been battling with rising fuels costs, increased competition from legacy carriers, and persistent aircraft operational issues. Norwegian’s problems are a continuation of what have been turbulent months for budget airlines in Europe resulting in a collapse of Primera Air, based in Denmark, near-default of WOW air, Iceland’s budget carrier, and most recently bankruptcy of Germania.

When we pull back the curtain and review the creditworthiness of European airlines to explore further some of the causes for Norwegian’s turbulent period, we see Norwegian’s business strategy and financial structure have made the carrier highly exposed. Coupled with the traditionally slow winter season, the airline may have to navigate through the storm clouds forming on the horizon.

A View From Above

S&P Global Market Intelligence has developed CreditModelTM Corporates 2.6 (CM2.6), a statistical model trained on credit ratings from our sister division, S&P Global Ratings. The model combines multiple financial ratios to generate a quantitative credit score and offers an automated solution to efficiently assess the credit risk of both public and private companies globally.1 Within CreditModel, the airline industry is treated as a separate global sub-model to better encompass the unique characteristics of this industry.

Figure 1 shows the overview of S&P Global Market Intelligence credit scores obtained using CreditModel for European airlines. Norwegian’s weak position translate into the weakest credit score among its competitors. The implied ‘ccc+’ credit score suggests that Norwegian is vulnerable to adverse business, financial, or economic conditions, and its financial commitments appear to be unsustainable in the long term. In addition to Norwegian, Flybe and Croatian Airlines rank among the riskiest carriers in Europe and share a similar credit risk assessment. The airlines with the best credit scores are also Europe’s biggest airlines (Lufthansa, Ryanair, International Airlines Group (IAG), and easyJet). The exception among the top five European airlines is Air France-KLM, which is crippled by labour disputes and its inability to reshape operations and improve performance.

Figure 1: Credit Risk Radar of European Airspace
Overview of credit scores for European airlines

Source: S&P Global Market Intelligence. For illustrative purposes only.
Note: IAG operates under the British Airways, Iberia, Vueling, LEVEL, IAG Cargo, Avios, and Aer Lingus brands. (January 3, 2019)

S&P Global Market Intelligence’s sister division, S&P Global Ratings, issued an industry outlook for airlines in 2019 noting that the industry is poised for stability.2 It stated the global air traffic remains strong and is growing above its average rate at more than 6% annually. The report also cited rising interest rates dampening market liquidity while increasing the cost of debt refinancing and aircraft leases. Oil prices are expected to settle, and any further gradual increases in oil prices are expected to be compensated by rising airfares and fees. The most significant risks for airlines are geopolitical. Potential downside scenarios include a crisis in the Middle East or other disruptions in oil, causing oil prices to spike. The possibility of trade wars and uncertainty surrounding the Brexit withdrawal agreement represent additional sources of potential disruption or weakening in travel demand.

Flying into the danger zone

Although Norwegian has so far dismissed any notion of financial distress as speculation, it has simultaneously implemented a series of changes to prevent further turbulence.3 The airline announced a $230mm cost-saving program that included discontinuing selected routes, refinancing new aircraft deliveries, divesting a portion of the existing fleet, and offering promotional fares to passengers to shore up liquidity.

In Figure 2, we rank Norwegian’s financial ratios within the global airline industry and benchmark them against a selected set of competitor European budget carriers (Ryanair, easyJet, and Wizz Air). Through this chart, we can conclude that Norwegian’s underlying problems are persistent and the company’s financial results are weak. Norwegian’s business model of rapid growth and a debt-heavy capital structure have resulted in severe stress for its financials. Norwegian ranks among the bottom 10% of the worst airlines in the industry on debt coverage ratios, margins, and profitability. This is in sharp contrast to other European budget carriers, which are often ranked among the best in the industry. On the flip side, Norwegian’s high level of owned assets represents its strong suit and gives the carrier some flexibility to adjust its operations and improve performance in the future.

Figure 2: Flying at Low Altitude
Norwegian’s financial ratios are among the worst in the industry

Source: S&P Global Market Intelligence. For illustrative purposes only. (January 3, 2019)
Note: Presented financial ratios are used in CreditModelTM Corporates 2.6 (Airlines) to generate quantitative credit score in Figure 1.

Faster, Higher, Farther

Norwegian has undergone a rapid expansion in recent years, introducing new routes and flying over longer distances. Between 2008 and 2018, the carrier quadrupled its fleet from 40 to 164 planes.4 This enabled it to fly more passengers and become the third largest budget airline in Europe, behind Ryanair and easyJet. However, unlike its low-cost rivals, Norwegian ventured into budget long-haul flights. After establishing its new base at London Gatwick, it started operating services to the U.S., South-East Asia, and South America.

As a result of this expansion, Norwegian’s capacity as measured by available seat kilometres (ASK) and traffic as measured by revenue passenger kilometres (RPK) grew nine-fold between 2008 and 2018, as depicted in Figure 3. By offering deeply discounted fares, the carrier was able to attract more passengers and significantly grow its revenues, which were expected to reach $5bn in 2018. However, to be able to support this rapid growth, Norwegian accumulated a significant amount of debt and highly increased its financial leverage. This rising debt is putting Norwegian under pressure to secure enough liquidity to repay maturing debt obligations.

Figure 3: Shooting for the Stars
Norwegian’s rapid growth propelled by debt

Source: S&P Global Market Intelligence. All figures are converted into U.S. dollars using historic exchange rates. Figures for 2018 are estimated based on annualized YTD 2018 figures. For illustrative purposes only. (January 3, 2019)

Norwegian’s strategy to outpace growing debt obligations by driving revenue growth is coming under pressure. The data tells us that expansion to the long-haul market and the undercutting of competitors to gain market share proved to be costly and negatively impacted Norwegian’s bottom line. Operational performance, measured as unit revenue (passenger revenue per ASK) and yield (passenger revenue per RPK), have been slipping continuously since 2008, as depicted in Figure 4. Negative free operating cash flow required Norwegian to continuously find new sources of capital to finance its operations, and profitability suffered. The carrier was able to ride a tailwind of low oil prices and cheap financing for a while, however, the winds seem to be turning.

Figure 4: Gravitational Pull
Slipping operational and financial performance

Source: S&P Global Market Intelligence, Norwegian Air Shuttle ASA: “Annual Report 2017”, Norwegian Air Shuttle ASA: “Interim report - Third quarter 2018”. Figures for 2018 are estimated based on annualized YTD 2018 figures. For illustrative purposes only. (January 3, 2019)

Norwegian’s plan to outrun a looming mountain of debt obligations is resulting in a turbulent flight. While growing its top line, the carrier has been unable to convert increased capacity and traffic into consistent profit. With a stable industry outlook and cost-cutting measures in place, Norwegian lives to fly another day. However, any additional operational issues or adverse macroeconomic developments could send Norwegian deep into the danger zone.

Learn more about S&P Global Market Intelligence’s Credit Analytics models.
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S&P Global Market Intelligence leverages leading experience in developing credit risk models to achieve a high level of accuracy and robust out-of-sample model performance. The integration of Credit Analytics’ models into the S&P Capital IQ platform enables users to access a global pre-scored database with more than 45,000 public companies and almost 700,000 private companies, obtain credit scores for single or multiple companies, and perform scenario analysis.

S&P Global Market Intelligence’s RatingsDirect® product is the official desktop source for S&P Global Ratings’ credit ratings and research. S&P Global Ratings’ research cited in this blog is available on RatingsDirect®.

1 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 credit model scores from the credit ratings issued by S&P Global Ratings.
2 S&P Global Ratings: “Industry Top Trends 2019: Transportation”, November 14, 2018. https://www.capitaliq.com/CIQDotNet/CreditResearch/viewPDF.aspx?pdfId=36541&from=Research.
3 Norwegian Air Shuttle ASA, “Update from Norwegian Air Shuttle ASA”, press release, December 24, 2018 (accessed January 3, 2019), https://media.uk.norwegian.com/pressreleases/update-from-norwegian-air-shuttle-asa-2817995.
4 Norwegian Air Shuttle ASA: “Investor Presentation Norwegian Air Shuttle”, September 2018.

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Sears Strikes Out What Is In Store For Other Retailers In The US

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Credit Analysis
Sears Strikes Out What Is In Store For Other Retailers In The US

Nov. 13 2018 — Recently, Sears Holdings Corp. (“Sears”) became yet another retailer in the U.S. that defaulted, and the firm filed for Chapter 11 on October 15, 2018. Similar to other retailers, this former giant could not keep pace with its more agile competitors in a fast-changing landscape. As reported by S&P Global Ratings,1 Sears’s default brings the annual corporate default tally in the U.S. to 37 as of October of this year, making the retail sector the largest contributor with eight defaults to date.

Sears transformed how America shopped and was once the largest retailer and largest employer in the country. However, it gradually fell out of consumers’ favour, and online stores and big box rivals took the helm. Sears' last profitable year was in 2010, and rapidly declining sales and weak cash flow offered the company limited room to improve its operations. Sears dipped into its assets to fund ongoing operations, closing more than 3,000 out of its 4,000 stores between 2011 and 2018. However, continuously negative earnings and a high interest burden prevented Sears from going on without a reorganization. The company’s credit rating by S&P Global Ratings captured this gradual decline in credit quality, deteriorating from ‘A-’ in 2000 to ‘CCC-’ prior to default.2

Only a limited number of companies have assigned credit ratings by a Credit Rating Agency (CRA), leaving out a significant part of the corporate universe. S&P Global Market Intelligence’s Credit Analytics suite includes a range of statistical tools that facilitate an efficient and cost-effective evaluation of a company’s credit quality by generating credit scores3 for both rated and unrated corporates globally. PD Model Fundamentals (PDFN) is a quantitative model that utilizes both financial data from corporates and the most relevant macroeconomic data available to generate probability of default (PD) values over a one- to more than 30-year horizon for corporations of any size. The numerical PD values can be mapped to S&P Global Market Intelligence credit scores (e.g. ‘bbb’), which are based on historical observed default rates extracted from the S&P Global Ratings’ database (available on CreditPro®).

In the analysis that follows, we review the credit risk landscape of retailers in the U.S., and explore which risk factors are the main drivers of PD. We then take a look down the road and assess how possible future macroeconomic scenarios may impact the credit risk of retailers in the U.S.

U.S. Retail Landscape

By leveraging PDFN, we can broaden the view beyond the realm of the CRA-rated universe. Figure 1 shows the distribution of implied credit scores obtained using PDFN for U.S. public retail companies. From 2013 to 2018, the distribution of the credit scores has been gradually shifting toward lower credit scores. In this period, the average credit score shifted from ‘bb+’ to ‘bb’, whilst the percentage of companies in the speculative credit score category (credit score ‘bb+’ and below) increased from 60% to 74%. These trends are symptoms of a change in the risk profile of the U.S. retail industry, which resulted in the bankruptcy of several other big retailers (e.g., Toys “R” US, Inc., RadioShack Corp., and The Bon-Ton Stores, Inc.).

In the second quarter of 2018, 45% of companies in our sample had been assigned a credit rating by S&P Global Ratings. Importantly, whilst a majority (76%) of companies in the investment grade universe do have a CRA rating, the speculative grade is largely unaddressed, with only 35% of companies in our sample having been assigned a credit rating by S&P Global Ratings. The use of statistical models, such as PDFN, assists investors in expanding the analysis and providing a comprehensive overview of credit risk across a wider universe.

Figure 1: Distribution of PDFN credit scores for public companies in the retail sector in the U.S.

Source: S&P Global Market Intelligence (as of October 22, 2018). For illustrative purposes only.
Notes: Public companies in the retail sector in the U.S. (GICS® 2550 and 3010). Distribution of companies based on PDFN credit scores calculated using the latest financial data for each respective period.

Drivers of Default

We further explore which risk factors are the main contributors of the PD for the retail sector. Credit Analytics’ models are equipped with tools, such as contribution analysis, which helps users identify drivers of risk in absolute or relative terms. These tools assess the “weight” or importance of the contribution of each risk factor to the credit risk estimate.

We divide the companies in deciles based on their PD and construct financial ratios for a median company in each decile. Next, we calculate PDs for each median company and evaluate associated absolute contributions of each risk factor.

Figure 2 shows the absolute contribution of each risk factor for a median retail company, a median retail company in the top decile, and a median retail company in the bottom decile. Note that absolute contributions for each company add to 100% to facilitate comparability, however, their nominal values are scaled by PD values and are, thus, markedly different.

Figure 2: Overview of absolute contribution of credit risk drivers for public companies in the retail sector in the U.S.

Source: S&P Global Market Intelligence (as of October 22, 2018). For illustrative purposes only.
Notes: Public companies in the retail sector in the U.S. (GICS® 2550 and 3010).

Efficiency and profitability represent important drivers of PD, directly reflecting an operating environment of low margins in the retail sector. Together with company size, these three risk factors represent roughly 50% of the PD value. In addition, low margins also limit available funds to respond to unexpected expenses and investment opportunities, resulting in restricted financial flexibility. However, the current macroeconomic environment for retail companies in the U.S. is favourable, and represents a small part of the PD value. On average, these companies have sustainable capital structures and sufficient liquidity, resulting in the limited contribution of these factors to PD.

For retail companies with high credit risk (bottom decile denoted in red in Figure 2), the company size, whilst still important, is no longer the only dominant factor. Factors like profitability, sales growth, and debt service capacity grow in importance and represent key factors when determining the credit quality of riskier retail companies.

View Down the Road

Next, we review how future economic scenarios may impact the credit risk of the public companies within the U.S. retail sector from a systematic point of view. The Credit Analytics Macro-Scenario model enables risk managers and analysts to gauge how a firm’s credit risk may change across both user-defined and pre-defined forward-looking scenarios, based on a set of macroeconomic factors. The model is trained on S&P Global Ratings’ credit ratings and leverages the historical statistical relationship observed between changes in credit ratings and corresponding macroeconomic conditions to explore what future scenarios may look like.

In Figure 3, we analyse the impact of different macroeconomic scenarios on companies with various credit scores. We depict relative changes in PD to directly demonstrate, in numerical terms, the impact on the expected loss calculation and, in turn, credit risk exposure. As a baseline prediction, we apply macroeconomic forecasts for the U.S. in 2019, developed by economists at S&P Global Ratings. We observe an increase in one-year PDs across all credit scores, suggesting a possibility of further deterioration of creditworthiness in the U.S. retail sector. Additionally, we evaluate the influence of two downturn scenarios: a mild recession scenario and a global recession scenario, which are based on economic trends during the early 2000s recession and the great recession of 2008, respectively. The downturn scenarios show proportionally larger increases in PDs, in accordance with the severity of each recession scenario.

Figure 2: Relative change of PD in the retail sector in the U.S. under various macroeconomic scenarios

Source: S&P Global Market Intelligence (as of October 22, 2018). For illustrative purposes only.
Notes: Macro-Scenario model captures the average tendency of all companies with the same creditworthiness profiles to transition to a different creditworthiness level (or remain at the same level) under a given macroeconomic condition, and does not take into account company-specific characteristics.

S&P Global Market Intelligence’s Credit Analytics suite helps users unlock relevant credit risk information to perform an overview of a company’s creditworthiness and undertake an insightful deep-dive analysis. All model inputs can be easily adjusted to perform sensitivity analysis for selected financial ratios, or to conduct a stress-test exercise using a fully-adjusted set of financials. The Macro-Scenario model integrates with standalone credit risk models and provides a forward-looking tool to help support expected credit loss calculations required by the new accounting standards: International Financial Reporting Standards 9 (IFRS 9) and the Financial Accounting Standards Board’s (FASB) Current Expected Credit Loss (CECL).

S&P Global Market Intelligence leverages leading experience in developing credit risk models to achieve a high level of accuracy and robust out-of-sample model performance. The integration of Credit Analytics’ models into the S&P Capital IQ platform enables users to access a global pre-scored database with more than 45,000 public companies and almost 700,000 private companies, obtain PD values for single or multiple companies, and perform a scenario analysis.

1 S&P Global Ratings: “Default, Transition, and Recovery: The Global Corporate Default Tally Jumps To 68 After Two U.S. Retailers And One Russian Bank Default This Week”, October 18, 2018.
2 S&P Global Ratings: “Credit FAQ: Credit Implications Of Sears Holdings Corp.'s Bankruptcy Filing For Retailers, REITs, CMBS, And Our Recovery Analysis”, October 17, 2018.
3 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 credit model scores from the credit ratings issued by S&P Global Ratings.
4 Numbers as of September 15, 2018.

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