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Consumer Insights Online Video User Overview

Credit Analytics Case Study: Hyflux Ltd.

2018 US Property Casualty Insurance Market Report

A New Media Use Report Reveals Millennials Consume 8 Hours of Media Per Day

Four Early Warning Signs Of Public Company Credit Risk Deterioration

Technology, Media & Telecom
Consumer Insights Online Video User Overview

Highlights

49% of survey respondents use more than one SVOD service.

Sep. 14 2018 — Data from Kagan’s U.S. online consumer surveys shows that 23% of respondents exclusively use one service, while almost half (49%) use more than one SVOD service. The service which is most commonly used exclusively is Netflix, while users of smaller services almost always use at least one other service.

Netflix is so universally used that it is both the most exclusively used service and the service most often used in conjunction with another service. In terms of demographics, Netflix users are very similar to the general population compared to smaller services that tend to have a younger user base.

With the exception of Netflix, most respondents indicated they have never subscribed to the top four services, including Netflix, Hulu, Amazon Prime Video and HBO NOW. Among those who indicated they dropped one of the top services, price was a principal reason for dropping, although content-specific reasons differed by service. Content is one of the most defining characteristics of online streaming services, which can be seen in the content viewed and most enjoyed on each service. In large part users of Netflix, Hulu and Amazon Prime Video most enjoy the content each service is known for.

A broader overview of this data was presented in a recent webcast.

Data presented in this blog is from U.S. Consumer Insights surveys conducted in September 2017 and March 2018. The online survey included 2,526 (2017) and 2,523 (2018) U.S. internet adults matched by age and gender to the U.S. Census. The survey results have a margin of error of +/-1.9 ppts at the 95% confidence level.

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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 Analytics Case Study Poundworld Retail Ltd

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Credit Analytics Case Study Saudi Aramco

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Insurance
2018 US Property Casualty Insurance Market Report

Highlights

S&P Global Market Intelligence’s 2018 US Property & Casualty Insurance Market Report offers a five-year outlook for the P&C sector, which should return to underwriting profitability for the first time since 2015.

Oct. 26 2018 — The federal tax reform President Donald Trump signed into law in December 2017 should help provide for an extended period of P&C industry profitability in 2018 and beyond as companies benefit from the lower corporate tax rate, but the impact is not limited to after-tax profitability. Actions by several prominent European-headquartered insurers to change the way certain of their U.S. business is reinsured materially impacted premium growth rates in the first quarter of 2018 and are likely to affect full-year results.

1 quarter does not a trend make

Historically strong results for the State Farm group in the first quarter
helped drive favorable comparisons in several key measures of underwriting profitability. To the extent the improvement continues for State Farm — the industry’s largest group based on direct premiums written — it could provide an additional tailwind for 2018 and beyond.

While there is a risk of recency bias in reading too much into a single quarter’s worth of data, the industry was already positioned for improved underwriting results in 2018. The second half of 2017 saw elevated catastrophe losses as the United States was hit by three landfall-making hurricanes and an unusual spate of fourth-quarter wildfires in California. Projected results for 2018 and subsequent years, all of which show combined ratios of less than 2017’s total of 103.5%, assume a normal catastrophe load.

Auto repairs in progress

Competition will remain intense in certain non-auto business lines given ample reinsurance capacity, high levels of industry capitalization and a macroeconomic environment that remains characterized by relatively slow growth in gross domestic product. Though modestly higher business volume driven by that economic expansion will help offset downward pressure on premiums, the industry will be challenged to achieve profitable top-line growth.

Trends in litigation will increasingly weigh on underwriting results in several business lines, including professional lines and the Florida homeowners business. They also could lead to greater demand for coverage, particularly for new and emerging risks.

The macro view

A rising federal funds rate and 10-year Treasury yields that have reached seven-year highs bode well for an industry that has long been suffering from low interest rates. And the relief cannot come quickly enough after the industry’s net yield on invested assets slipped to a new low of only 3.03% in 2017. Though projected results provide for increasing yields from that floor, the improvement will still take place gradually and is unlikely in and of itself to materially impact how companies are underwriting business

S&P Global Market Intelligence client? Click here to login and read the full 2018 US Property & Casualty Insurance Market Report.

The projections reflect various assumptions regarding premiums, losses and expenses. They are a product of a sum-of-the-parts analysis of individual business lines that is informed by third-party macroeconomic forecasts, historical trends and recent market observations that include first-quarter 2017 statutory results and anecdotal commentary about market conditions. Projected results are displayed on a total-filed basis and are not intended for application to individual states, regions or companies. S&P Global Market Intelligence reserves the right to update the projections at any time for any reason.

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U.S. Insurance Market Report – Property & Casualty (June 2017)

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Technology, Media & Telecom
A New Media Use Report Reveals Millennials Consume 8 Hours of Media Per Day

Highlights

By generation, GenZ/Millennials were the biggest media users at eight hours and 11 minutes per day with the rates dropping the older the survey taker gets.

At 42 minutes per day, GenZ/Millennials are more likely to use their phones for viewing video than they are their PCs (39 minutes).

The following post comes from Kagan, a research group within S&P Global Market Intelligence.

To learn more about our TMT (Technology, Media & Telecommunications) products and/or research, please request a demo.

Oct. 26 2018 — GenZ/Millennials are watching more video than other generations, just not on TV, according to our first report on consumers' daily media use by hour.

Data derived from Kagan’s U.S. Consumer Insights survey conducted during third quarter 2018 showed that overall survey takers spend about six hours and 50 minutes per day on video, gameplay and listening to music, using on average 51% of this time for video, 28% for music and 21% for playing games.

By generation, GenZ/Millennials were the biggest media users at eight hours and 11 minutes per day with the rates dropping the older the survey taker gets. Males also over-index in terms of media use, especially with games. By ethnicity, Black/African Americans, Hispanic/Latino and Other (including Native Americans and Aleut Eskimos) all were above the average for total respondents at eight hours and 50 minutes per day, seven hours and 39 minutes per day and six hours and 58 minutes per day, respectively, for the three activities combined.

We calculated this data by asking survey takers to self-report how many hours they spend on these activities on a "typical" day.

For those who view the video, we asked a follow-up question to estimate the share of time spent viewing video by screen: TV, PC, smartphone or tablet. Overall survey takers estimated they spend about two hours and 19 minutes per typical day watching video on TV.

As might be expected, the TV screen is most popular with those 53 and older at two hours and 38 minutes per day. Those least likely to use the TV were GenZ/Millennials, other races and Asian survey takers at just one hour and 59 minutes, one hour and 45 minutes and one hour and 23 minutes per day, respectively.

Overall, survey takers spend about 31 minutes per day watching video on their PCs. Boomers/Seniors were the least likely to watch video from their laptop or PC screen at 25 minutes per day. Black/African Americans and GenZ/Millennials were the biggest PC video viewing fans at around 40 minutes per day each.

Smartphone viewing among all surveyed was 24 minutes per day. At 42 minutes per day, GenZ/Millennials are more likely to use their phones for viewing video than they are their PCs (39 minutes). Smartphone video viewing was more popular with females than males.

Overall, daily minutes for tablet video viewing among all surveyed was just 12 minutes. Other races and Asians were most likely to use tablets for video at 19 minutes and 18 minutes per day, respectively. Again, tablet video viewing was more popular with females than males.

Interest in video among younger Americans remains strong. Their viewing time is simply spent more on smaller screens compared to older survey takers.

Data presented in this article is from Kagan's U.S. Consumer Insights survey conducted in September 2018. The online survey included 2,536 U.S. internet adults matched by age and gender to the U.S. Census. The survey results have a margin of error of +/-1.9 ppts at the 95% confidence level. Generational segments are as follows: GenZ/Millennials: 18-37, Gen X: 38-52, Boomers/Seniors: 53+.

Consumer Insights is a regular feature from Kagan, a group within S&P Global Market Intelligence's TMT offering, providing exclusive research and commentary.

Daily Media Use By Hour

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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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