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2016 Inaugural Global Financial Services Default Study and Rating Transitions

Hurricane Watch: Monitoring the Financial Impact on Governments in Florence's Path

Deleveraging Trend is Set to Pause for China’s Rated Corporates

Beyond the Map: Building Competitive Advantage Through Leveraging Geospatial Data

A Hitchhiker's Guide to Alternative Data

2016 Inaugural Global Financial Services Default Study and Rating Transitions

Financial services entities rated by S&P Global Ratings, including the bank, nonbank financial institution (NBFI), and insurance sectors, experienced overall high ratings stability and credit quality in 2016, even amid rising global pressures and uncertainty, from volatile commodity prices to rising geopolitical risks, along with political votes that shifted the global dynamic, including the Brexit referendum. A total of 16 rated global financial services issuers defaulted in 2016, an increase from 12 rated defaults in 2015, marking the highest number of defaults from rated financial entities since 2009. These defaults included several NBFIs from the U.S. in advance of pending regulatory actions, along with several banks from Russia and Central and Eastern Europe that defaulted through a regulatory directive. Meanwhile, the largest default of the year was a U.S. bank, the Government Development Bank for Puerto Rico, and the highest-rated defaulter was Panamanian reinsurer Istmo Compania de Reaseguros Inc., which was rated 'BBB' as of the beginning of 2016. This marked the first time in five years that a financial services company defaulted in a year in which it was rated investment grade ('BBB-' or higher) as of the beginning of the year.

Despite rising uncertainty and defaults, ratings remained stable for the majority of financial services entities during the year, and measures of credit quality and ratings stability remained generally in line with historical averages. Default rates were above average only for the lowest rating categories of 'B' and 'CCC'. The default rate for speculative-grade (rated 'BB+' or lower) financial entities globally rose to 2.71% in 2016, and while this was the highest rate since 2010, it remained below the default rate for speculative-grade nonfinancial companies. While the default rate for financial services companies has been rising in recent years, the pace of defaults slowed through the first half of 2017.


  • The number of rated financial entities that defaulted rose to 16 in 2016 from 12 in 2015. The highest rated among the year's defaults was Istmo Compania de Reaseguros, which was rated 'BBB' as of the beginning of 2016, marking the first investment-grade default from a financial services company since the default of MF Global in 2011.
  • The trailing-12-month default rate for speculative-grade financial entities rose to 2.71% in 2016 from 2.33% in 2015, considerably lower than the 4.19% speculative-grade default rate for corporate entities globally.
  • The ratings on financial entities were largely stable in 2016, with 77.1% of ratings remaining unchanged during the year.
  • The Gini coefficient, which we use to measure ratings performance, had a long-term (since 1981) weighted average of 79.56% for financial services companies. The one-year Gini coefficient rose to 91.77% in 2016 from 91.5% in 2015.

This is the inaugural global financial services study, where we review the defaults, transitions, and ratings performance of the broad financial services sector, including the bank, NBFI, and insurance sectors. Our findings in this study show that ratings continue to serve as effective indicators of relative credit risk for financial services companies. We identified a clear negative correspondence between ratings and defaults: The higher the issuer credit rating, the lower the observed default frequency. Historically, financial services issuers tend to have lower default rates than in the nonfinancial corporate sectors, though they also tend to be more sensitive to changes in investor confidence. For those companies that do default, the decline in credit quality can be swift once lenders and counterparties lose confidence in the entity.

Defaults of financial services companies have often involved some type of asset/liability mismatch, wherein a combination of illiquid assets and accelerating liabilities is ignited by a collapse in confidence (such as in the institution or in the value of the assets), sparking a "run on the bank" scenario, where depositors, policyholders, or lenders make demands that cannot be met, either due to illiquidity or insolvency. In many cases, this leads to a regulatory intervention, such as when a bank or insurer is taken under regulatory supervision by its regulator or, as we saw during the 2008-2009 period, when a government steps in to provide extraordinary support to a systemically important financial institution.

Defaults of banks and NBFIs are often cyclical, with defaults rising during periods of recession or other times of financial stress. Some of the notable periods of financial stress that contributed to elevated default rates for banks and NBFIs included the housing crash and Great Recession of 2008-2009, as well as the savings and loan crisis in 1989. Additionally, global financial services default rates have risen in years with a sovereign default, such as following that of the Russian Federation in 1999 and of Argentina in 2001 and 2002. Meanwhile, though the insurance sector also experiences periods of rising defaults, these cycles tend to follow industry trends of aggressive pricing or reserving and do not necessarily follow the broader business cycle.

Our study of global financial defaults identified a clear negative correlation between ratings and defaults. We show this relationship with a Gini ratio, which is a measure of the rank-ordering power of ratings over a given time horizon (see table 1). This measure shows the ratio of actual rank-ordering performance to theoretically perfect rank ordering. Despite the increasing number of defaults in 2016, including one investment-grade default, the one-year Gini ratio--a key measure of the relative ability of ratings to differentiate risk--remained elevated because most of the year's defaults came from the lowest-rated companies.

The one-year Gini ratio rose to 91.77% in 2016 from 91.5% in 2015 and remains above its long-term average. In recent years, financial services ratings have had one-year Gini coefficients well above the average, holding above 90% over the past five years as multiple central banks have pursued quantitative easing. Over longer time horizons, Gini ratios continue to attest to ratings as effective indicators of relative default risk. The one-year weighted-average Gini coefficient for financial entities is 79.56%, the three-year is 68.44%, the five-year is 60.87%, and the seven-year is 55.91%. These weighted-average Gini ratios are weighted by yearly issuer counts since 1981 (see Appendix II for Gini methodology details).

Gini Coefficients For Financial And Nonfinancial Issuer Ratings (1981-2016)
  --Time Horizon--
  One-year Three-year Five-year Seven-year
Weighted average 79.56 68.44 60.87 55.91
Average 81.67 74.23 65.79 58.76
Standard deviation (20.93) (14.58) (15.87) (14.21)
Weighted average 80.77 73.03 69.92 68.15
Average 84.14 76.87 73.03 70.03
Standard deviation (6.11) (5.34) (5.62) (5.27)

Note: Financials consist of banks, NBFIs, and insurance companies. Nonfinancials consist of all nonfinancial corporates. Numbers in parentheses are standard deviations. Sources: S&P Global Fixed Income Research and S&P Global Market Intelligence's CreditPro®.

Hurricane Watch: Monitoring the Financial Impact on Governments in Florence's Path

Anticipating the path of a hurricane, gathering information on the damage, and then evaluating its potential credit implications can be challenging and the effects can vary widely, even within a relatively small geographic area. The 2018 hurricane season brings Florence to the Southeast, and it is predicted to make landfall in the U.S. over the Carolinas. S&P Global Ratings anticipates monitoring more than 50 local governments within North and South Carolina and we will likely expand the scope of our review to include Georgia given the recent track of the storm. S&P Global Ratings views the availability of federal grants and disaster assistance as a key component to stabilizing communities after a natural disaster, especially for areas with little-to-no support from private insurance.

Since issuers are focused on cleanup and post-storm assessment, it often takes them several weeks to connect with S&P Global Ratings and provide an assessment of damage, including any potential long- and short-term effects. As with our previous hurricane coverage, our first step is to triage the potentially affected issuers. During this time, we look at the most recent financial information available, particularly liquidity levels and upcoming debt service due dates, as well as any other reserves available to provide a cushion before state and federal aid arrives. Then, once the storm has passed, we begin contacting issuers and reaching out as they become available to communicate with us. Once we have assessed the potential effects on an issuer's credit quality, we take rating actions as necessary. In some instances, particularly when issuers remain unavailable for some time following a storm, we may place ratings on CreditWatch with negative implications or revise the outlook to negative. This would occur if the information we have indicates the presence of extreme or prolonged stress that we feel could affect overall credit quality.

Deleveraging Trend is Set to Pause for China’s Rated Corporates

Rated Chinese companies will have trouble maintaining their deleveraging trend now that the earnings rally has faded. However, corporate spending appetite remains restrained, and Chinese authorities are committed to deleveraging for state-owned enterprises. That's according to a report S&P Global Ratings published today titled, "Slower Earnings Growth Drags On Deleveraging For Corporate China."

"We project that debt leverage for our rated portfolio of Chinese companies will increase slightly in 2018, reversing the downward trend in 2017," said S&P Global Ratings credit analyst Chang Li.

Earnings growth is decelerating after a commodities-fueled boost over the past two years, and amid generally tougher economic and financial conditions.

By our estimates, EBITDA will expand by 10% on average for our rated portfolio this year, down from a heady 25% in 2017. Among large sectors in our portfolio, property and mining drove the earnings recovery in 2017. For example, EBITDA jumped 41% in the property sector and 43% in the mining sector. However, EBITDA growth in these two sectors will shrink to 24% and 2% respectively in 2018.

China's deleveraging campaign has contributed to slower investment spending by local governments, supply-glut heavy industries, and property companies. This combined with a crackdown on "shadow banking" and rising U.S.-China trade tensions will lead to slower average revenue and profit growth in 2018.

Chinese authorities have recently begun to fine-tune their financial-risk reduction measures to support corporate financing. This comes amid rising stress and higher default rates, especially for private enterprises.

"We believe easing efforts could take pressure off some Chinese companies facing difficulties in refinancing their debt maturities, especially state-owned enterprises that rely on borrowing new funds to pay off old debt," said Mr. Li.

However, in our view, the most vulnerable borrowers, in particular private enterprises, will continue to face higher refinancing and default risk.

Overall, we have a modest negative net bias in our portfolio, due to deteriorating liquidity, especially for companies in capital goods, metals and mining, and local government financing vehicles.

As of July 2018, our list of "weakest links" and "fallen angels" within the China corporate portfolio has expanded slightly, to nine companies. Four of these companies are in real estate, reflecting this sector's heightened vulnerability to tightening liquidity and refinancing risk.


Beyond the Map: Building Competitive Advantage Through Leveraging Geospatial Data

Geographic Information Systems (GIS) offer a framework for understanding information in relation to its physical location in space. And GIS technology today takes us far beyond traditional cartography to providing key dimensions of location and interconnectivity to every data point. New GIS tools and techniques help organize layers of spatial data with related attributes, empowering users to better understand the physical world.

Of course, businesses and investors have long employed data to inform decision making. And for the majority of that history, primary analysis was performed by interrogating clues from the recent past, from the rearview mirror. What were profits like last year? What was the yield from the last production run? Which suppliers delivered for us last quarter? Those who best quantified outcomes exploited an information advantage and were rewarded.

Today’s technology produces data that reveals far more rich, detailed insight into both economic activity and financial results than ever before – much of it with a spatial component.

Connected devices track our location and behaviors; Internet of Things (IoT) sensors are infiltrating commercial and consumer goods; more satellites are launched every year, with increasingly powerful imaging capabilities. Pair this explosion of data with plummeting cost of storage and awesome processing power delivered through cloud computing, and there is massive potential for the creation of new information advantages.

Those who harness the best predictions in their operations will find an edge, and data is the fuel.

Nate Haskins, Chief Data Officer, S&P Global

But data in isolation has limited value. It’s when disparate data sets are combined that new actionable insights are delivered. Data science techniques such as machine learning and deep learning are being used to correlate massive, previously intimidating data sets, allowing them to be used in new and creative ways, often ways that were not necessarily intended when the data was produced. Operators and investors alike are now using data in alternative ways to create predictions and inform decisions. Those who harness the best predictions in their operations will find an edge, and data is the fuel.

Nowhere has technology had a larger influence on what is possible than in the field of GIS. A recent report by Bryce Space & Technology noted a 53% increase in satellites launched between 2012-2016, averaging 144 launches per year. Advances in imaging and radar deliver higher resolution outputs and three-dimensional renderings, in some cases independent of cloud cover. As a result, we can now understand the location of ships, levels of reserves in oil terminals, forest health, construction progress, impact of natural disasters, car and foot traffic, and much more -- in near-real time.

What advantages can be created using this new data? Let’s look at a few examples:

  • Understanding global oil supply
    Businesses are using new data to understand the massive, complex global oil markets. Machine learning techniques can be used on imagery to estimate weekly crude oil inventories otherwise not reported; monitors on tankers reveal proximity to ports and refineries. Together with an understanding of refining capacities, this data offers a timely view into global supply. Layering on advanced demand forecasts accounting for weather, economic growth and consumption trends, traders are gaining new predictive insights into the future price of petroleum products. S&P Global has invested in Ursa Space Systems, a firm that specializes in leveraging radar satellite imagery while S&P Global Platts customers have access to sophisticated trade flow analytics through the Platts cFlow tool.
  • Maximizing the impact and returns of renewable power
    Advances in photovoltaics - the process of converting sunlight to electricity - wind turbine efficiency and large-scale battery storage efficiency have unlocked the viability of renewable power sources. GIS data is being employed to inform site selection to maximize impact. For example, imagery can be used to identify areas with high recurring solar exposure, suitable slope and terrain, and proximity to low-voltage transmission lines, roads and populated areas while avoiding conservation areas. Machine learning algorithms can be used to identify the pitch and surface conditions of commercial roofs, identifying the best candidates for rooftop commercial installations.
    Similar conditions apply to siting of wind farms using factors such as typical wind speeds and directions. All of those are helping bright down the cost of renewable power and accelerating the shift to clean energy sources.
  • Gaining advantages in insurance underwriting
    Savvy insurance companies are improving their underwriting practices using detailed imagery. An understanding of changing climate as well as forestation and underbrush levels help predict the likelihood of wildfires. Detailed topographical analysis dramatically improves upon ancient or incomplete flood zone maps previously used to price flood insurance products. In both cases, GIS is becoming an intrinsic part of risk modelling which gives insurance companies the knowledge to price the policies according to the risk they are undertaking.
  • Mastering markets with custom demographics
    Businesses now use GIS to answer the question of “what do my markets look like?” by building custom demographic tapestries within drive time areas around their locations. Demographic information, including historical and projected data, combines with road infrastructure and traffic data to define detailed trade areas for analyzing market potential, market penetration, and competitive threats. Gaps and overlaps in market coverage drive decision making for closing or opening additional locations.

Every industry is now a technology industry, and every company a technology company. Your grocer, your cabbie, even your local pizza shop all use data to tailor and promote services, identify prospects, and inform their strategy. If you run into a company not thinking of themselves that way, my guess is they won’t be around for long.

Are you leveraging data fully, or are you destined for irrelevance?

Nate Haskins, Chief Data Officer, S&P Global

Those who master this information first will be rewarded. Are you leveraging data fully, or are you destined for irrelevance?

The following was originally published on CIO Review on August 22, 2018: Beyond the Map: Building Competitive Advantage through Leveraging Geospatial Data

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