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2016 Annual Sovereign 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 Annual Sovereign Default Study and Rating Transitions

In 2016, two defaults occurred among the sovereign obligors that S&P Global Ratings rates. This was one more default than in 2015 and the fifth consecutive year with at least one sovereign default. Including the default, we lowered 24 sovereign ratings and raised nine in 2016. Rating actions were less numerous than in 2015, but the number of downgrades was the highest since 2011.


  • The rank ordering of sovereign ratings has been consistent with historical default experience, and these ratings continue to serve as effective indicators of relative credit risk.
  • Of the 130 active sovereign ratings at the end of 2016, 52% were investment grade.
  • Sovereign ratings generally exhibited greater stability at higher rating levels than at lower levels, as we would expect.
  • The ratio of downgrades to upgrades increased to 2.67x in 2016 from 1.40x in 2015. In addition, the average number of notches of foreign-currency downgrades increased to 1.50 from 1.15. The average number of notches for upgrades remained the same at 1.00.

We track rating movement according to the number of ratings that changed during 2016 as opposed to the number of times a rating changed. For example, this study considers a rating that was lowered twice in 2016 to reflect one lower rating at the end of 2016 than at the beginning of 2016. On the other hand, if a rating was lowered and later raised to the rating at the start of the year, no rating action would appear in this study.

This study is based on long-term sovereign credit ratings. The methodology tracks rating migrations over time and includes revisions to 'SD' (selective default). An 'SD' rating is more common for sovereign issuers than a 'D' rating because defaulting sovereigns often continue to service some of their debt. This is an issuer ratings-based study, as opposed to being based on issue ratings. In other words, we look at the sovereign ratings on the central governments themselves, not the ratings on the individual securities these governments might have issued.

Our metrics treat all issuers equally and are not adjusted for size or influence. Therefore, for the purposes of this study, a default by Argentina counts the same as a default by Mali, even though the latter has a much smaller economy. Our study tracks defaults on a sovereign's commercial debt, including both bonds and bank loans.

Withdrawn ratings (as indicated with the abbreviation 'NR,' which stands for "not rated") are included up until the date of withdrawal. We record defaults after the date of withdrawal if we obtain knowledge of those defaults. As of Dec. 31, 2016, S&P Global Ratings had withdrawn 11 public sovereign ratings: Benin, Cambodia, Gabon, Guernsey, Isle of Man, Kyrgyz Republic, Libya, Madagascar, Mali, Seychelles, and Tunisia. (We reinstated the rating on Guernsey in October 2014.) There are a total of 166 rating records for sovereigns, including those of defaulted ratings.

The number of sovereigns that we rate has grown as more governments access the international bond markets. We have rated 141 sovereigns (foreign currency) since 1975, and 130 of these had active ratings at the end of 2016.

In the 1990s, speculative-grade-rated sovereigns became more common as we started rating smaller and less-diversified economies. The percentage of speculative-grade ratings significantly increased to 37% in 1999 from 3% in 1991. This was mostly a result of new ratings being assigned, as the number of speculative-grade ratings rose to 30 from two, while the number of investment-grade ratings increased to 52 from 29. During 2000-2009, speculative-grade ratings continued to outpace investment-grade ratings, but the latter group grew as well. Speculative-grade ratings increased to 53 in 2009 from 33 in 2000, while investment-grade ratings grew to 70 in 2009 from 54 at the start of the decade. The percentage of speculative-grade ratings was 43% in 2009. During 2010-2016, the number and percentage of speculative-grade ratings increased to 62 and 48%, respectively, while the same for investment-grade ratings declined to 68 and 52%. In 2016, three investment-grade sovereigns were downgraded to speculative grade, while one speculative-grade sovereign was upgraded to investment grade.

Seventy-one of the initial sovereign ratings we have assigned have been speculative grade; we assigned the first to Hungary in April 1992. At the end of 2016, 11 of the 71 sovereigns with initial speculative-grade ratings were rated investment grade, 18 defaulted at some point, and nine had withdrawn ratings. On the other hand, of the 69 sovereigns initially assigned investment-grade ratings since 1975, seven were rated speculative grade at the end of 2016, four defaulted, and three were withdrawn. Of the 130 sovereigns with active foreign-currency ratings at the end of 2016, 68 (52%) were investment grade and 62 (48%) were speculative grade. As of March 17, 2017, three sovereign ratings were rated one notch above speculative grade ('BBB-') with negative outlooks: Kazakhstan, Oman, and South Africa. Among the speculative-grade sovereigns, one was rated one notch below investment grade ('BB+') with a positive outlook: Indonesia and Russia.

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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