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

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

Project Finance How To Weigh High Yield Against Expected Loses

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
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 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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Credit Analysis
Project Finance How To Weigh High Yield Against Expected Loses

Sep. 02 2018 — In what seems like a perennial state of low interest rates, there appears to be no end in sight to market participants’ search for income. Decreasing bond yields – some markets even flirting with negative yields – has meant the search continues to expand away from the traditional bond markets and towards alternatives, which may offer steady income.

An area where we are seeing increasing interest from market players is the higher yielding project finance industry with its steady projected cash flows, which offers some means to achieving returns not available elsewhere in the current yield-tight environment.

There is, however, the perception that higher yield projects will involve the risk of higher losses, and as with most investments, higher returns can go hand-in-hand with additional risks. Although past performance does not guarantee future results, returns may be influenced by a number of factors, including the asset class itself.

In the case of project finance, market players should understand the importance of reliable risk measurement calculations.

Computing Expected Losses (EL): Understanding the several components

From conversations with clients and other market participants, we understand worries over high expected losses may be a deterrent due to a cautious assessment being performed to determine the risks involved.

A better understanding involves looking at the fundamentals behind the credit analysis.

Starting with the basics…We know that:

Expected Loss (EL) = Probability of Default (PD) x Loss Given Default (LGD) x Exposure at Default (EAD)

Where:

  • Probability of default (PD): Probability that an investment will default on an agreement (i.e. not pay when due);
  • Loss Given Default (LGD): Proportion of investment lost due to default;
  • Exposure at Default (EAD): Exposure “at risk” of loss at date of calculation.

There is a great focus on the PD associated with any given investment, which in turn depends on the entity’s credit quality. Generally, the better the credit quality - the lower the expected PD.

However, as we can see from the formula above, to fully understand the EL it is necessary to consider the recovery prospects (i.e., the LGD).

An informed decision regarding EL should encompass both the PD and LGD assessment. The interaction between the two components for the purpose of computing the EL is illustrated in the figure below:

Computing Expected Losses (EL): Relationship between Probability of Default (PD) and Loss Given Default (LGD)

Source: S&P Global Market Intelligence. For Illustrative purposes only.

In an ideal scenario where both PD and LGD are low, the probability of the investment defaulting is very low. If it ends up defaulting, the recoveries are close to 100%. Moreover, it is easy to understand that the EL is very low, reflecting the low risk of these investments. Inversely, if both PD and LGD are high, the EL will reflect the higher risk associated with these investments.

The assessment of EL is harder to perform when one of these components displays a high risk reading and the other reads low risk. In these cases, it is very important to fully understand both risks in order to make an informed decision.

When an investor decides to move into the high yield space (e.g., investments that have lower credit quality and hence higher PDs), the key determinant for computing EL is no longer the credit quality assessment, but rather the collateral value and security enforceability. In these cases, given that the investment has a higher likelihood of defaulting, the EL is largely determined by the LGD assessment (how much can you recover).

In certain cases, a high PD might not necessarily mean that the EL is high. If the LGD is low (your recoveries are high), then the EL can actually be low as well.

If the investments are in project finance, the LGD associated will typically tend to be low , leading to low EL even within the high yield space. This may explain why we have noticed an increase in new market participants (for example, insurance companies) in this asset class over the last few years.

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