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Daily Media Use By Hour

4G/5G Densification To Push Global Small Cell Spend To $2 Billion By 2020

Energy

Power Forecast Briefing: Fleet Transformation, Under-Powered Markets, and Green Energy in 2018

Fintech Investors Pushed Stripe To Massive Valuation In September

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

Technology, Media & Telecom
Daily Media Use By Hour

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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Technology, Media & Telecom
4G/5G Densification To Push Global Small Cell Spend To $2 Billion By 2020

Highlights

In 2023/2024, emerging markets will begin to see volume small cell deployments.

Nov. 19 2018 — Global mobile operators are expected to rely heavily on small cells to dramatically improve network coverage in indoor locations, select metro areas, suburbs and rural and remote locations over the next five years. According to new research from Kagan, global small cell revenues are expected to grow to $2.4 billion in 2020, as mobile operators and enterprises look to improve the coverage of both 4G LTE and 5G networks in both indoor and outdoor environments.

Total combined indoor and outdoor small cell revenue is expected to reach a peak in 2022, before declining in 2023 and 2024. Our expectation is that the bulk of initial small cell rollouts for 4G LTE and 5G densification will be complete by 2022, particularly those deployments throughout EMEA designed to enhance coverage and capacity for 4G LTE networks. We do expect a slight decline in revenue in 2019 as pricing on indoor small cell units decreases due to volume shipments. In 2023 and 2024, more emerging markets in CALA and Africa will begin to see volume small cell deployments. However, those deployments will be on a smaller scale when compared with larger markets, including the U.S., China, India and those of Western Europe.

Small cells are low-power radio access points designed to complement and enhance existing macrocell locations. They can operate in both licensed and unlicensed spectrum bands and be deployed both indoors and outdoors and with multiple form factors. The largest of these devices are typically used in urban and rural outdoor locations and the smallest are reserved for indoor residential applications.
There are three primary types of small cells:

  • Femtocells: small footprint, low-power devices designed to improve coverage in homes and small businesses. Femtocells can improve coverage gaps indoors, especially when signals originating from outdoor macrocells are unable to penetrate walls. Current femtocells support anywhere from four to eight mobile devices and have a range of 50 feet or less.
  • Picocells: base stations designed to cover a small area of around 700 feet or less. Picocells can be deployed both indoors and outdoors, with most being deployed indoors in shopping malls office complexes and train stations to add network capacity. Outdoor locations include stadiums and urban areas to improve network capacity and coverage. 
  • Microcells: typically have a range of around three miles, compared with macrocells, which have an average range of 22 miles. Microcells are used to add network capacity and coverage in outdoor locations, including dense urban areas, as well as rural and remote areas.

This summary is from Kagan’s first article providing in-depth global coverage of mobile infrastructure technologies. It includes worldwide small cell locations by region, revenue and units forecast through 2024. Following this will be an article covering global distributed antenna systems, or DAS, also with forecasts through 2024.

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Watch: Power Forecast Briefing: Fleet Transformation, Under-Powered Markets, and Green Energy in 2018

Steve Piper shares Power Forecast insights and a recap of recent events in the US power markets in Q4 of 2017. Watch our video for power generation trends and forecasts for utilities in 2018.

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Banking
Fintech Investors Pushed Stripe To Massive Valuation In September

Nov. 14 2018 — Stripe Inc.'s $245 million capital raise was the clear highlight of U.S. financial technology fundraising in the month of September, weighing in at more than four times the next-highest raise. Stripe's massive round even stood out in comparison to other sectors, ranking in the top 15 of all U.S. private placements during the month.

The latest raise values Stripe, a payments company founded in 2009, at $20.25 billion on a post-money basis. The most recent valuation is a massive increase from November 2016, when a $150 million infusion valued the company at $9.15 billion. But on a percentage basis, an even bigger jump in valuation came from May 2012, when the company was valued at about $100 million, to January 2014, when its valuation skyrocketed to $1.75 billion.

The Stripe tale is a classic tech story. With only seven lines of code, two brothers in their early twenties launched the company that now counts industry giants such as Amazon.com Inc., Alphabet Inc., Facebook Inc. and Microsoft Corp. as clients. Patrick and John Collison believed that PayPal Holdings Inc., one of the leading payments companies, was designed specifically for peer-to-peer transactions, rather than e-commerce, and that it was not built for scale, as they explained in a May episode of How I Built This. In response, they built a system that allows e-commerce companies to easily add payment functionality to their websites. For instance, an e-commerce company can add a button to its page that collects the customer's credit card information and sends it to Stripe for processing.

Stripe was also unique in that it appealed to software developers. As a result, it could attract tech startups as customers that, while small at the time, would grow to become sizable institutions. This reputation in the developer community even led to companies approaching Stripe about creating new systems. For instance, ride-hailing company Lyft Inc., a loyal customer, specifically asked Stripe to help create an instant cash-out service for its drivers, according to a Recode article published in September 2016.

Due to Stripe's large capital raise in September, the payments category ranked highest in terms of aggregate deal value among the six fintech categories that S&P Global Market Intelligence tracks. But based on number of transactions, financial media and data solutions was the leader.

The financial media and data category includes companies such as Crux Informatics Inc., which helps institutions store and process data. San Francisco-based Crux, which was founded in 2017, has some big-name backers from the financial services world: Citigroup Inc., Goldman Sachs Group Inc. and, more recently, Two Sigma Investments LP. Of the three, Two Sigma would seem the most likely user, as a quantitative asset manager that, according to its website, crunches more than 35 petabytes of data. But Wall Street investment banks seem increasingly focused on tech as well. A recent article in the Financial Times, for instance, said that a fresh crop of analysts in JPMorgan Chase & Co.'s asset management division were required to complete coding lessons.

No particular investor appeared to make an inordinately large amount of fintech investments during the month, but there were several institutions that made two investments.

Stripe lined up a large number of investors — eight in total — which is not surprising given the size of the transaction. Meanwhile, OODA Health Inc. also managed to assemble a roster of eight investors for an offering that was about one-sixth the size of Stripe's. Founded in 2017, OODA Health aims to simplify and automate payments in the healthcare industry, using technology to help payers and providers collaborate on a better system. The investor list on its September capital raise demonstrates this effort to bring together both sides, with participation from health insurers and hospital systems alike.

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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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