videos Market Intelligence /marketintelligence/en/news-insights/videos/sp-global-data-services content

Login to Market Intelligence Platform

New User / Forgot Password

Looking for more?

Contact Us

Request a Demo

You're one step closer to unlocking our suite of comprehensive and robust tools.

Fill out the form so we can connect you to the right person.

  • First Name*
  • Last Name*
  • Business Email *
  • Phone *
  • Company Name *
  • City *

* Required

In this list
Capital Markets

S&P Global - Data Services

Fintech Investors Pushed Stripe To Massive Valuation In September

The Essential IFRS 9 Checklist For Insurers

How Americans Get Their News: Research Summary

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

Watch: S&P Global - Data Services

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 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.

S&P Global Market Intelligence client? Click here to login and read the full article

Learn more about Market Intelligence
Request Demo

The Essential IFRS 9 Checklist For Insurers

Nov. 13 2018 — While the January 1, 2021 IFRS 9 deadline for insurers may seem a long way off, there is still a lot of work that has to be done. Whether you go for an internal or an external solution, it is essential to have an understanding of all the core components and considerations involved in either approach.

First, there is the technology required for IFRS 9 data management, calculations, and reporting. In comparison to the banking sector, where many of the banks had advanced risk management systems in place for their normal course of business prior to IFRS 9 implementation, insurers may need to upgrade existing, or invest in new, risk management systems. These systems should also be ready to support a parallel run ahead of the “go-live” date. Ideally this would be for two years (and no less than one year), given the complexity and implications of IFRS 9 on Profit and Loss (P&L) accounts.

From an operational standpoint, there may also be a need to recruit new employees, or train existing employees, in order to update and implement the operational processes, accounting processes, and reports needed for IFRS 9.

There are a number of additional IFRS requirements that will need attention, in particular the reconciliation between IFRS 17 (accounting for insurance contracts) and IFRS 9, given that they both share the January 1, 2021 deadline.

Some of the key considerations for insurers in implementing an IFRS 9 calculation framework include:

  • Classification of relevant financial instruments (assets and liabilities). Typically, but not exclusively: corporate bonds, government bonds, irrevocable loan commitments and financial guarantee contracts (not accounted for at fair value through P&L (FVTPL)), structured debt (senior tranches), and mortgage loans.
  • Incorporation of past, present, and forward-looking information in order to perform the expected credit loss (ECL) calculation.
  • Definition, implementation, and monitoring of the correct staging process – 12 month versus lifetime horizon, and looking out for a significant increase or reduction in credit risk since initial recognition

Therefore, it is evident that there are many important considerations for IFRS 9 adoption. One of the most critical is the ECL calculation itself and its composite parts. Looking at this in more detail, the four potential steps to arrive at the ECL are shown below (Figure1).

Figure 1: Steps to Arrive at ECL

1This is optional as detailed in section 5.5.4 of the IFRS 9 standard that suggests the use of: "all reasonable and supportable information that is available without undue cost or effort at the reporting date about past events, current conditions, and forecasts of future economic conditions."

Although the above steps may make the ECL calculation seem straightforward, in practice the lifetime calculation is more complex than the 12-month figure shown in the inset, and there are many complexities surrounding the calculation of the PiT values, as it needs to integrate past, present, and future information across multiple investments. This is particularly difficult for low-default asset classes, which will normally make up the majority of an insurer’s investment portfolio. This is because representative default data is hard to find and analytical capabilities and workflow tools are needed to undertake these estimates across multiple exposures. In addition, for the portion of an insurer’s portfolio that is unrated, a framework is needed that can reliably identify that risk.

Internal model approach

While it is recommended to leverage the approach described above, some companies may instead attempt to solely use internal models and data to calculate the PD, LGD, and ECL by using their company’s historical default data and applying this to the corresponding asset types.

This internal model approach can suffer from the following problems:

  • Insufficient data for reliable modelling: This will particularly be a problem for small- and medium-sized insurers. Some of the larger insurers may have adequate volumes of historical data and sufficient sample sizes for internal model creation.
  • Poor prediction of default: If only internal data is used, the broader default experience provided from complementary models built on deep and rich data sets will not be reflected in the IFRS 9 estimates. The predictive power of these internal models could then be compromised.

Moreover, a possible lack of precision, which can be inherent in this type of internal modelling approach, may prove more costly as more loss provisions may need to be allocated, and the potential benefits of lower ECL costs would then be lost.

Other Possible Benefits

There are two other possible benefits that can be gained from full IFRS 9 implementation:

  • The hedge accounting component may provide a benefit to insurers in helping firms better reflect their risk management practices on their financial statements, which can potentially provide a competitive advantage since most investors typically like to see a company smooth out P&L volatility on their financial statements.
  • It will also add transparency to financial reporting, which can be attractive to investors.

To find out more about the suite of S&P Global Market Intelligence solutions for IFRS 9 contact us here.

1 Permission to reprint or distribute any content from this presentation requires the prior written approval of S&P Global Market Intelligence. Not for distribution to the public

Learn more about Market Intelligence
Request Demo

How Americans Get Their News: Research Summary

Nov. 13 2018 — We started the News in America series of articles with the lofty goal of better understanding how news media trends are impacting Americans and our democracy. The data used for this series was derived from a Kagan U.S. Consumer Insights survey conducted during third quarter 2018. The following is a summary of what we learned.

Who pays attention to news? Our survey results show there is a wide variance in news consumption. U.S. internet adults fall into the following news consumption segments:

  • A small group of consumers (6%) that do not pay attention to news at all (No time for news group)
  • A substantial group of consumers (38%) that are largely unaware of details related to national news topics but pay attention to local news (Locals group).
  • A relatively small group (12%) of consumers that are generally aware of what is happening in the news, but may not follow the topics in detail (The awares group).
  • A large group of consumers (36%) that closely follow both local and national news topics, but do not rely on TV cable news (Well informed group).
  • A small group of consumers (8%) that are the largest consumers of local and national news (News junkies group).

These results indicate that the national news media reaches 56% of the U.S. internet adult population but only 44% keep up with national news on a daily basis. Voter turnout for the 2016 presidential election was 58% and the turnout for mid-term elections is traditionally in the 35% range. The survey did not identify voting history, but the results suggest that those who track national news are also most likely to vote.

In Part 1 of the series we learned that most Americans acquire their news from multiple sources. Approximately half (49%) of internet adults watch local TV news programs, 40% watch TV network news shows and 33% watch TV cable news. Less than half (44%) of internet adults subscribe to a newspaper and 40% acquire news online.

In Part 2 of the series we revealed that only a slim majority (51%) of internet adults believe most journalists try to report accurate news. Only one-third (34%) of Fox News viewers believe that journalists try to report the news accurately, compared to over three-quarters (76%) of MSNBC viewers and 73% of CNN viewers. Approximately half of those who do not keep up with national news also believe that journalism is biased.

Part 3 of the series found that the influence of TV cable news may be overstated. Only one-quarter (27%) of internet adults said that TV cable news is their most influential source of news. However, among cable news viewers, 38% said TV cable news was their most influential source.

In Part 4 we highlighted that only one-third (32%) of U.S. internet adults keep up with developments in the Russia investigation.

Part 5 showed that Americans who follow news the most also tend to rely on one trusted news source. For example, half (50%) of Fox News viewers, along with 44% of MSNBC views and 39% of CNN viewers said they rely on one trusted source of news.

Part 6 of the series found that two-thirds (65%) of internet adults keep up with local news, most using multiple news sources (newspaper, TV or radio).

In Part 7 we concluded that consumers who watch TV talk shows for politics news do so primarily for entertainment, rather than to be informed. Viewers who watch TV talk shows for political news tend to already be well informed on national and local issues.

In Part 8 we learned that those who closely follow national and local news also are most likely to frequently talk politics. Gender, education and where you live can also impact one’s desire to discuss politics.

This research series highlighted many of the human behavior factors related to news consumption. Where we get our news may change over time, but our interest in following the news, especially national news, may not. Finding innovative ways to convert the 44% of Americans who do not follow national news into informed voters would truly be a worthy cause.

Learn more about Market Intelligence
Request Demo

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.

Learn more about Market Intelligence
Request Demo

Credit Analytics Case Study Poundworld Retail Ltd

Learn More

Credit Analytics Case Study The Bon-Ton Stores, Inc

Learn More