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Peeking Into The Future Without Staring At A Crystal Ball Brexit Scenarios And Their Impact On Energy Firms’ Credit Risk

Flying Into The Danger Zone; Norwegian Air Shuttle

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

Credit Analytics Case Study: Hyflux Ltd.

Four Early Warning Signs Of Public Company Credit Risk Deterioration

Credit Analysis
Peeking Into The Future Without Staring At A Crystal Ball Brexit Scenarios And Their Impact On Energy Firms’ Credit Risk

Jul. 24 2018 — After so many years of living and working in London, two years ago I applied for, and was finally granted, British citizenship. Imagine my surprise when, a few weeks later, the UK European Union referendum took place and the majority of voters opted for Brexit!

As a dual national, both European and British, I feel twice the pain of an uncertain future and sometimes I wish I had a crystal ball.

While it is hard to predict how the whole separation process will pan out, S&P Global Market Intelligence offers a new statistical model that allows users to understand how firms’ credit risk on either side of the ocean may change under multiple exit scenarios. The Credit Analytics Macro-scenario model covers the United States, Canada and European Union countries plus the United Kingdom (EU27+1). In addition, the model can be run via the S&P Global Ratings’ Economists macro-economic multi-year forecasts, tailored for this specific model and updated on a quarterly basis.1

Figure 1 shows the Economists’ forecasts of the inputs used in our statistical model for EU27+1, for year-end 2018, 2019 and 2020.

Source: S&P Global Market Intelligence (as of June 2018). For illustrative purposes only. L/S ECB Interest rate spread is the spread between long-term and short-term ECB interest rates. Y-Axis is % of; GDP Growth, Stoxx50 Growth, Interest Rate Spread or FTSE100 Growth, depending on the correlating symbol as described in the key.

The expectation is for economic growth to slow-down in the EU27+1. This will be accompanied by progressive monetary policy tightening and a volatile performance of the stock market index growth. This view is aligned with the baseline scenario included in the European Banking Authority (EBA) and the Bank of England (BoE) 2018 stress testing exercise that “[…] reflects the average of a range of possible outcomes from the UK’s trading relationship with the EU”.2

Figure 2 shows the evolution of the median credit score of Energy sector (left panel) and Utility sector (right panel) large-revenue companies in EU27+1, obtained by running the economists’ forecasts via the Macro-scenario model.3 The median score for 2017 is generated via S&P Global Market Intelligence’s CreditModelTM 2.6 Corporates, a statistical model that uses company financials and is trained on credit ratings from S&P Global Ratings.4 The model offers an automated solution to assess the credit risk of numerous counterparties, globally. The scores are mapped to a numerical scale where, for instance, bb- (left panel, left scale) is mapped to 13.0; a deterioration by 1 notch corresponds to an increase of one integer on the numerical scales.

Figure 2: Evolution of the median credit score of Energy and Utility sector companies in EU27+1, based on S&P Global Economists’ macro-economic forecasts run via the Macro-scenario model.

Source: S&P Global Market Intelligence (as of June 2018). For illustrative purposes only.

Starting from 2017, we see a higher level of credit risk in the UK (red line) than in EU27 (blue line); in subsequent years, the median credit risk increases on both sides of the Channel but the “risk fork” between the UK and EU27 tends to widen up at the expenses of the UK, for both sectors.5

Despite the fact that the median credit score may not change sizably between 2017 and 2020, remaining below half a notch overall in all cases, it is worth keeping in mind that the probability of default (PD) associated with a credit score changes in line with the economic cycle, and thus increases (decreases) during periods of contraction (expansion).

In our model, we account for this effect by first mapping the credit score output to a long-run average PD; next we scale it via a “Credit Cycle Adjustment” (CCA) that looks at the ratio between the previous year and the long-run average default rate historically experienced in S&P Global Ratings’ rated universe.6 If we adjust the long-run average PD via the CCA, we can easily identify potential build-up of default risk pockets in different countries within the EU27+1 as time evolves, as shown in the animations within Figure 3. Green refers to a lower PD than 2017, orange refers to a higher PD than 2017, and red refers to a PD breaching a pre-defined threshold (4.5% for Energy Sector and 0.3% for Utilities sector).7

Figure 3: Potential pockets of default risk in Energy and Utility sector companies in EU27+1, based on S&P Global Economists’ forecast.

Energy Sector Utility Sector
Default Risk in Energy map Default Risk in Utilities map

Source: S&P Global Market Intelligence (as of June 2018). For illustrative purposes only. Green refers to a lower PD than 2017, orange refers to a higher PD than 2017, and red refers to a PD breaching a pre-defined threshold (4.5% for Energy Sector and 0.3% for Utilities sector).

With the Macro-scenario model, we aimed for a user friendly model, and took into account the strong economic ties within EU27+1, the existence of a common market and the circulation of a shared currency in the majority of the EU countries, in order to select a parsimonious yet statistically significant set of inputs (just imagine otherwise forecasting multiple macro-economic scenarios for 28 individual countries, over multiple years).8

Readers may wonder how the model differentiates the evolution of credit risk by country if it uses a limited set of aggregate macro-economic factors (e.g. EU28 GDP growth, etc.) across EU27+1. Nine separate sub-models were actually optimized, based on economic commonalities and historical evolution of the S&P Global Ratings transitions in those countries, to account for the existence of different EU “sub-regional” economies (for instance Nordic countries as opposed to Eastern European countries). For the UK, we went one step further, by explicitly including a market indicator, the FTSE100, as a precautionary measure given a potential “full decoupling” of EU27 and UK economies in the near future.

Well, so far so good, at least in the case of a “soft” Brexit! But what if we end up with a “hard” Brexit?

The EBA and the BoE 2018 stress testing exercise include a stressed scenario that “[…] encompasses a wide range of economic risks that could be associated with {hard} Brexit”.9 The scenario corresponds to a prolonged recessionary period, with negative GDP growth for several years and a generalized collapse of the stock markets, similar to what happened during the 2008 global recession. Unsurprisingly, the median credit score output by our macro-scenario model companies significantly deteriorates for both Energy and Utility sector. Figure 4 shows the build-up of potential default risk pockets and their evolution over time, under stressed economic conditions, depicting a bleak view over the length of time needed for a recovery of these sectors.10

Figure 4: Potential pockets of default risk in Energy and Utility sector companies in EU27+1, based on EBA’s and BoE’s 2018 stressed scenario.

Energy Sector Utility Sector
Default Risk in Energy map Default Risk in Utilities map

Source: S&P Global Market Intelligence (as of June 2018). For illustrative purposes only. Green refers to a lower PD than 2017, orange refers to a higher PD than 2017, and red refers to a PD breaching a pre-defined threshold (4.5% for Energy Sector and 0.3% for Utilities sector).

I do not have yet a crystal ball to predict the future, e.g. whether petrol will cost more or less, or whether I will be paying higher utility bills in the UK as opposed to (the rest of) the European Union, but S&P Global Market Intelligence’s Macro-Scenario allows gauging potential credit risk changes in individual countries, under a soft or a hard Brexit scenario. More in general, the Macro-Scenario model offers a quick, scalable and automated way to assess credit risk transitions under multiple scenarios, thus equipping risk managers at financial and non-financial corporations with a tool that enables them to make decisions with conviction.

Notes

1 The macro-economic forecasts will become available on the S&P Capital IQ platform from 2018Q4. S&P Global Ratings does not contribute to or participate in the creation of credit scores generated by S&P Global Market Intelligence.

2 Source: “Stress Testing Exercise 2018” available at http://www.eba.europa.eu/risk-analysis-and-data/eu-wide-stress-testing/2018. The baseline scenario is the consensus estimate among EU27+1 Central Banks.

3 The results of this analysis depend on the portfolio composition. In addition, other industry sectors may react differently from the Energy and Utility sectors.

4 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 scores from the credit ratings issued by S&P Global Ratings.

5 The 2017 median score for the Utility sector is better than the score for the Energy sector, due to the inherently higher risk of companies in the latter.

6 An optional market-view adjustment is available within the macro-scenario model. In our analysis, we did not include this adjustment, for the sake of simplicity.

7 4.5% (0.3%) is close to the historical long-run average default rate of companies rated B- (BBB-) by S&P Global Ratings.

8 This is also one of the reasons we found it unnecessary to include oil price for the modelling of credit risk of the energy sector in EU27+1, as we found the stock market growth was sufficient.

9 Source: “Stress Testing Exercise 2018” available at http://www.eba.europa.eu/risk-analysis-and-data/eu-wide-stress-testing/2018. The baseline scenario is the consensus estimate among EU27+1 Central Banks. Curly brackets refer to the author’s addition.

10 We adopt the same colour conventions as in Figure 3.

Understanding Drivers Of Credit Risk

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Macro-Scenario Model: Conditioning Credit Risk Transitions On Macro-Economic Scenarios

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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.
Learn more about S&P Global Market Intelligence’s RatingsDirect®.

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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Tesla Contemplates Going Private; But Who Is Going to Power Its Batteries

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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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Credit Analytics Case Study Poundworld Retail Ltd

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Credit Analytics Case Study The Bon-Ton Stores, Inc

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Credit Analysis
Credit Analytics Case Study: Hyflux Ltd.

Nov. 02 2018 — Hyflux Ltd. (Hyflux) is a Singaporean water utility company, which announced on 22 May 2018 that it had applied to the Singaporean High Court to begin a court-supervised process of debt and business reorganization.1 S&P Global Market Intelligence’s Fundamental Probability of Default (Fundamental PD) increased nearly nine-fold from 0.5046% (an implied credit score of bb+)2 to 4.5233% (an implied credit score of b) between fiscal year (FY) 2016 and FY 2017. Since FY 2017, the Q1 2018 Fundamental PD increased by approximately 4% to 4.7275% and the company has not filed any further public accounts since entering court supervision.

Hyflux experienced a period of rapid sales growth expansion over the course of 2016 with sales year-on-year (y-o-y) growing from 38.5% in Q4 2015, to peak at a high of 152.48% in Q3 2016. However, over the course of 2017 sales growth slowed and the company began to struggle to service its debt facilities. From mid-2016 the company’s current liabilities/net worth exceeded 100% and would continue to rise until in mid-2018 where it would stand at 141.5%.

Exhibit 1: Fundamental PD Escalation

Source: S&P Global Market Intelligence as of July 24, 2018. For illustrative purposes only.

Business Description
Hyflux provides various solutions in water and energy areas worldwide. The company operates through two segments, Municipal and Industrial. The Municipal segment supplies a range of infrastructure solutions, including water, power, and waste-to-energy to municipalities and governments. The Industrial segment supplies infrastructure solutions for water to industrial customers. The company provides seawater desalination, raw water purification, wastewater cleaning, water recycling, water reclamation, and pure water production services to municipal and industrial clients, as well as to home consumers; and filtration and purification products. It also designs, constructs, owns, operates, and sells water treatment, seawater desalination, wastewater treatment, and water recycling plants under service concession arrangements; and sells oxygen-rich water and related products and services. In addition, the company designs, constructs, owns, operates, and sells power plants and waste-to-energy plants; trades in the electricity markets; and sells retail electricity contracts. Hyflux was founded in 1989 and is headquartered in Singapore.

Fundamental Probability of Default Analysis
The analysis of S&P Global Market Intelligence’s one-year Fundamental PD reveals Hyflux had consistent implied credit scores in the bb+/bbb- range over the course of 2016.3 In the time after FY2016 the Fundamental PD rose consecutively for 5 periods until the company applied for court supervision in May 2018. It is therefore possible to detect the deteriorating credit quality of Hyflux as early as 12 months before the credit event in 2018. Prior to Q2 2017, the company had been below the global water utility industry median one-year PD consistently since 2010 with a single exception in 2015. The escalation of company PD levels in 2017 therefore show a significant increase in company risk levels on an absolute basis but also when compared to that of its global peers.

Fundamental PDs produced over the course of 2017 and into H1 2018 highlights several areas of financial risk elements which were driving risk levels continually higher. As of Q1 2018, the company had a financial risk assessment from PD Fundamentals as highly leveraged. Over the course of 2016, sales growth became negative and moved from a high point of 152.5% y-o-y sales growth in Q3 2016 to a low of -53.48% y-o-y sales growth in Q3 2017. This contraction in sales revenues led to a situation where the company was under increasingly constrained liquidity. Over the same period, the company’s current liabilities/net worth rose from 57.2% in Q3 2016 up to 141.52% as of Q1 2018, demonstrating the increasingly fragile state the company was in with respect to its liabilities. EBIT Interest Coverage fell from 3.05x in Q3 2016 down to -1.61x as of Q1 2018 while EBITDA margins fell into negative territory over the course of 2017 and stood at -24% as of Q1 2018. The combination of these factors – declining sales revenues, increasing leverage and erosion of profit margins, prompted the company to enter into court supervised debt restructuring conversations.

Exhibit 2: Fundamental Probability of Default Contribution Analysis

Source: S&P Global Market Intelligence as of June 28, 2018. For illustrative purposes only.

Exhibit 3: Key Developments

Source: S&P Global Market Intelligence as of October 29, 2018. For illustrative purposes only.

1 Source:The Business Times, Hyflux seeks court protection to reorganise business, debt, as published on May 23, 2018.
https://www.businesstimes.com.sg/companies-markets/hyflux-seeks-court-protection-to-reorganise-business-debt .
2 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 scores from the credit ratings used by S&P Global Ratings.
3 S&P Capital IQ platform as of October 29, 2018.

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Credit Analysis
Four Early Warning Signs Of Public Company Credit Risk Deterioration

Highlights

Co-Author: Hrvoje Tomicic

Oct. 24 2018 — A firm’s stock price is often thought to be a reflection of its expected future cash flow. Based on this idea, in 1974 Merton proposed a model for assessing the structural credit risk of a company,leveraging Black-Scholes’ options pricing paper.2 This model has become popular among financial and academic practitioners and is still employed to monitor the credit risk of public companies or for investment purposes.

Due to its market-driven nature, the model’s daily outputs are often plagued by unwanted noise that makes it hard to detect genuine signs of a firm’s impending credit risk deterioration.

At S&P Global Market Intelligence, we have developed PD Model Market Signals (PDMS), a statistical model that builds on the original framework proposed by Merton with further enhancements and refinements such as:

  • Model calibration: PDMS is calibrated based on the industry-sector long-term default rates observed in S&P Global Ratings’ historical database of rated companies3, thus anchoring the model outputs to stable reference levels.
  • Granularity: By capturing important business risk drivers, such as Country Risk Scores and industry risk components, and market-risk drivers such as CDS Market Derived Signals,4 PDMS provides greater insight into global public companies headquartered in different countries, or operating in different industries.
  • Noise reduction: We employ advanced statistical techniques to filter out potential outliers, thus generating cleaner and easier-to-interpret market signals.

How can you use PD Model Market Signals?

Based on this model, there are four early warning signs of imminent credit deterioration of the public companies under your surveillance. Below, we outline those key indicators and how to apply our PDMS model as a best practice approach for measuring credit deterioration.

1. The Probability of Default (PD) increases beyond a fixed level, based on our observed historical trends: Our model can be used to flag a company every time its PD passes the median or the bottom quartile of the distribution of defaulted companies. Figure 1, shows the historical behavior of the median and bottom quartile PD generated by PDMS for several hundred public non-financial companies that defaulted between 2003 and 2015, as they approached the default date. Half of the defaulters had a PD above 8%, a full twelve months prior to default, increasing to 15% at the default date. For a quarter of the companies that “went bust” (the “bottom quartile”), the PD goes from 16% (12 months prior to default) to more than 28% at the default date. Keeping in mind your own risk appetite, it is relatively straightforward to define reference points that can be used to generate timely alert signals that can trigger specific actions when breached in advance of a potential default.5

Figure 1: Median and bottom-quartile PD generated by PD Model Market Signals for non-financial (non-FI) public corporations that defaulted in the period 2003-2015, from twelve months prior to default to default date.

Source: S&P Global Market Intelligence (as of August, 1st 2018). For illustrative purposes only.
2. The PD is markedly different from the typical values of companies in the same industry/country peer group: When you have exposure to several companies in the same sector or country and their PD’s are all quite volatile, you still need to monitor and separate the “bad from the good apples”. Figure 2 shows the case of Noble Group Limited that defaulted in March 2018. Over a twenty-four month period prior to default, our PDMS model generated a very volatile PD that peaked above 30% on several occasions. This is even more significant when compared to the median, bottom quartile, and 10th percentile PD of companies in the same peer-group for the corresponding period. One suggestion to get additional insight is to set a threshold based on the bottom quartile or the 10th percentile PD so that whenever a firm’s PD exceeds the chosen threshold, the company is moved into a watch-list for further action. The converse would happen when the PD goes back within the “norm” range. This approach is also validated by the Key Developments reported for this company within the S&P Capital IQ platform, as shown in the callouts within Figure 2.

Figure 2: Market Signal PD (PDMS) of Noble Group Limited and median, bottom quartile and 10th-percentile PD of peer-companies listed in the Singapore stock exchange within the Trading Companies and Distributors sector.

Source: S&P Capital IQ Platform (as of August, 1st 2018). For illustrative purposes only.
3. The PD of a company exceeds its moving average: This third sign is important when analyzing stock markets, where moving averages are often employed to remove unwanted noise to more easily gauge short-term and long-term trends of a stock’s price. A firm’s PD can often be very volatile, but its moving average (over 30 or 180 days) is less eventful, and any time the short-term moving average crosses the long-term average, a warning signal is generated. Cumulus Media Inc., which defaulted in November 2017, is a good example (see Figure 3). As you can see, a more timely alternative would consider the actual PD value in relation to the 30 days moving average. For example, in the Cumulus Media Inc. example, the last time the PDMS was higher than the 30 day moving average was in August 2017.This additional intelligence would have potentially allowed for precious time to carry out further analysis or take an appropriate remediation action. In addition, in this case, checking key developments and news may have further provided signal confirmation, as shown in Figure 3.

Figure 3: Market Signal PD (PDMS) of Cumulus Media Inc. and its moving average PD (over 30 or 180 days) for the period September 2016 to November 2017.

Source: S&P Capital IQ Platform (as of August, 1st 2018). For illustrative purposes only.
4. The PDMS-implied credit score deteriorates more than the corresponding S&P Global Ratings’ issuer credit rating: Our approach becomes particularly powerful when the S&P Global Ratings’ issuer credit rating is non-investment grade and the PDMS implied credit score becomes (significantly) worse than the actual rating. This is exemplified in Figure 4 for the case of Bon-Ton Stores, which defaulted in December 2017. Here you can see that the implied credit score is compared to the rating from S&P Global Ratings. The combination of a weak issuer credit rating by S&P Global Ratings and a weak credit score implied by S&P Global Market Intelligence’s PDMS statistical model represents a “deadly combination” that should ring a very loud alarm bell; the S&P Capital IQ platform’s key developments call-outs complete the picture.

More generally, our internal analysis on non-FI corporates rated in the speculative grade range by S&P Global Ratings shows that whenever the PDMS-implied score is three or more notches worse than the actual credit rating, there is a 30% chance of a further S&P Global Ratings’ downgrade6 within 12 months. This helps confirm the versatility of this technique in generating actionable signals even for asset management purposes. We will follow with a separate white paper on how asset managers can use this model, and what happens when the PDMS implied-score sizably deviates from the S&P Global Ratings’ issuer credit rating.

What about the public companies that are not rated? One can still combine the PDMS output with the credit score generated by S&P Global Market Intelligence’s CreditModelTM, a quantitative model that uses company financials and other socio-economic factors to generate a quantitative credit score for a longer time horizon that statistically matches S&P Global Ratings’ issuer credit ratings7 for rated companies, but also covers unrated companies.

Figure 4: S&P Global Ratings’ issuer credit rating (ICR) and PD Model Market Signals (PDMS) implied credit score for Bon-Ton Stores, Inc.

Source: S&P Global Market Intelligence (as of August, 1st 2018).6 Key developments extracted from the S&P Global Market Intelligence’s Capital IQ platform. For illustrative purposes only.

Some will argue that looking at a firm’s stock price should be sufficient for most purposes, as its price already embodies all necessary market information. However, the main advantage of a structural model such as PDMS is to link the capital structure of a company to the uncertainty around a company’s future cash-flows, and to properly quantify the probability of default based on empirical evidence.

As a final remark, we stress that none of the techniques mentioned above will be infallible all the time, due to the unpredictable nature of default events. In general, a combination of multiple signals will achieve better performance, and should trigger further due diligence. For example looking at the company financials and their trend over time, comparing the focus company vs its peers, complementing the market-implied credit risk assessment with alternative statistical models (for example S&P Global Market Intelligence’s PD Model Fundamentals), and ultimately validating the assessment with news, key developments or alternative information.

1 “On the pricing of corporate debt: the risk structure of interest rates”, R.C. Merton, J. Finance 29, 449–70 (1974).
2 “The pricing of options and corporate liabilities”, F. Black and M. Scholes, J. Polit. Econ. 81, 637–54 (1973).
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 PD credit model scores from the credit ratings issued by S&P Global Ratings.
4 Fundamental credit risk analysis shows that country and industry risk capture importance risk drivers linked, for instance, to ease of doing business, level of corruption, industry barrier to entry, etc. S&P Global Market Intelligence broadly employs these scores that enhance the granularity of model outputs and statistical model performance.

5 Past performance does not predict future results. As such, statistical models are calibrated on companies that have and have not defaulted

6 By 1 or more notches, up to and including default.
7 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 scores from the credit ratings issued by S&P Global Ratings.

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