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Credit Analytics Case Study Poundworld Retail Ltd

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
Credit Analytics Case Study Poundworld Retail Ltd

Highlights

Co-written by Elijah Harden, Risk Services

Aug. 29 2018 — Bankruptcy Summary

Poundworld Retail Ltd (Poundworld) is a discount store operator located in the United Kingdom that on June 11, 2018, while operating around 350 stores, filed for administration in order to work to find a buyer for the chain1. S&P Global Market Intelligence’s Fundamental Probability of Default (Fundamental PD) increased nearly fivefold from 1.69% (an implied credit score of ‘bb-’2), a level that was better than the median general merchandise store in the U.K., to 10.39% (an implied credit score of ‘ccc+’) between fiscal year (FY) 2015 and FY 2016. To summarize, the increased Fundamental PD is similar to a credit score decline from ‘bb-’ to ‘ccc+’. The following year between FY 2016 and FY 2017, the Fundamental PD increased nearly 72% from 10.39% to 17.84% (an implied credit score of ‘ccc-’).

As of the reporting date November 10, 2016 for the period ending March 31, 2016, 19 months before the company filed for bankruptcy, Poundworld fell into ‘ccc’ range and was unable to recover. Poundworld’s inability to recover was due to competing in an increasingly competitive discount retail environment where there was less foot traffic to traditionally populous town centers and exchange rate pressure due to importing goods while the pound was weaker than the dollar3. This resulted in increasingly narrow margins, higher leverage, and decreasing profitability.

Exhibit 1: Fundamental PD Escalation

Business Description

Poundworld operates a chain of discount department stores in the United Kingdom and sells products through its online shop. It offers food and drinks, cleaning and laundry products, health and beauty products, home products, garden and outdoor supplies, pet care products, electrical products, stationery items, toys, baby products, party and gift products, and leisure time products. Poundworld was founded in 1974 and is based in Normanton, United Kingdom.

Fundamental Probability of Default Analysis

The analysis of S&P Global Market Intelligence’s one-year Fundamental PD reveals Poundworld had consistent implied credit scores in the ‘single b’ range for 10 of its 13 reporting periods from FY 2005 to FY 20174. In the time after FY 2012 the volatility of the implied credit scores increased in response to the volatility of Poundworld’s net income. As recently as FY2015, Poundworld, with a PD of 1.69% (implied credit score of ‘bb-‘), sat in the top half of UK general merchandise stores. However, in FY 2016 the company fell into the worst 25% of its UK peers with a PD of 10.39% (implied credit score of ‘ccc+’), roughly a year and a half before filing for administration. Subsequently, in FY 2017, it approached the worst 10% of its UK peers with a PD of 17.84% (implied credit score of ‘ccc’). This shows a notable escalation in risk, both on an absolute basis and with respect to its peers.

The Fundamental PD as of August 16, 2017 for the reporting period ended March 31, 2017 (FY 2017) highlights business and financial risk were significant problems for the company with vulnerable and highly leveraged scores, respectively. The most noteworthy factors contributing to the increased PD were total revenue, profit margin (net income to total revenue), a ratio of how much of every dollar earned is kept within the company, and current liabilities to net worth, a measure of how leveraged the company is/how much debt is used to finance the business. Poundworld experienced a revenue growth rate decline of 57.15% between FY 2015 and FY 2016 from 22.32% to 9.56% with a subsequent decline of 40.88% ultimately ending with a profit margin of 5.65% by FY 2017. As revenue growth for Poundworld slowed, the company became exceedingly leveraged. The average current liability to net worth ratio between FY 2013 and FY 2017 was an extraordinary 667%, signaling the company was unable to pay off debt obligations that were due within a year. In addition to the increasing leverage, Poundworld was battling diminishing profit margins until they eventually became negative, with an average profit margin of -0.02% between FY 2013 and FY 2017. Poundworld’s illiquid position made the company particularly vulnerable to the other operating expenses which totaled approximately £9MM in FY 2016 and FY 2017, which only carried the company closer to the brink of bankruptcy.

Source: S&P Global Market Intelligence as of July 19, 2018. For illustrative purposes only.
Note: Current Liabilities to Net Worth ratio in FY 2017 is actually -1317%, but the model assumes the worst possible profile and assigns the value of 10842%

Source: S&P Global Market Intelligence as of July 19, 2018. For illustrative purposes only.

1 Unless otherwise noted, all information sourced from the S&P Capital IQ platform as of July 24, 2018.
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 Source: Financial Times, Poundworld files for bankruptcy, as published on June 11, 2018. https://www.ft.com/content/5f00154e-6d54-11e8-852d-d8b934ff5ffa
4 Source: S&P Capital IQ platform as of July 24, 2018.

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Credit Analytics Case Study The Bon-Ton Stores, Inc

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Credit Analytics Case Study: Carillion Plc

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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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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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