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A Perspective On Machine Learning In Credit Risk

2019 Credit Risk Perspectives: Is The Credit Cycle Turning? A Fundamentals View

2019 Credit Risk Perspectives: Is The Credit Cycle Turning? A Market Driven View

Concentration and Cross Holdings of Chinese Banks

Flying Into The Danger Zone; Norwegian Air Shuttle

Credit Analysis
A Perspective On Machine Learning In Credit Risk

Highlights

Co-Authored by Danny Haydon

Aug. 20 2018 — There have been major advances in the application of Machine Learning (ML) in the recent past due to a plethora of industry drivers that have revolutionized the utilization of these techniques in the risk management sphere, and beyond. In this primer we will cover the key transformational drivers causing these high adoption rates, some of the techniques, and how to assess their utility within credit risk.

Drivers

Firstly, data in general has experienced a large expansion in several dimensions; size, velocity and variety. Simultaneously the abilities to record, store, combine and then process large datasets from many disparate sources has experienced wholesale improvements. This is not limited to just traditional sources, but also alternative data which fueled the need to extract information value from these sources. However, the side effect of this data expansion is an elevated level of data pollution that needs to be contended with. Data pollution includes noisy, conflicting and difficult to link datasets.

Secondly, the ease of access to enhanced computational efficiency through hardware that can run specialized operations in large scale, and also in coding language enhancements which have moved towards functional programming, have transformed the game in terms of integrating Machine Learning techniques. Languages, such as R, become the hub for numerical computing using functional programming. They leverage a lengthy history of providing numerical interfaces to computing libraries. Supervised and unsupervised algorithms allow data scientists to process these datasets into actionable insights with relative ease and to code with cheaply executable hardware.

Thirdly, reproducible research and analysis has been widely adopted by the data science community. This is defined as a set of principles about how to do quantitative and data science driven analysis, where the data and code that leads to a decision or conclusion should be able to be replicated in an efficient and clear way.

Finally, the pervasiveness of Open Source libraries, packages and toolkits has opened doors for the community to contribute via teams of specialists, sharing code base and packaging them into easy and modular functions.

ML Techniques in Risk and considerations in their application

The typical phases of applying ML within a Risk context include the following pipeline:

Fig 1: Generalized Machine Learning Pipeline

Assessing which ML techniques to use and when is an important step that needs to be done thoughtfully with the target context in mind. There is no prescriptive method that is purely tied to a particular class of algorithms; the risk context always needs to be kept in mind in order to assess the tradeoffs.

A simple example to consider is the variance-bias tradeoff. Variance reflects the instability of the model to various factors. For example, if small changes to the data result in big changes to the model, then the technique has a high variance. Bias is the ability of the model to show fidelity to the underlying pattern. See Fig 2 for a simple example of this.

Fig 2: Demonstration of model fit comparison visualization

In the above figure we see that Random Forest exhibits low bias, but high variance to the dataset. Quadratic Regression exhibits low variance, but high bias to this data set. Nonlinear regression, in this trivial example with ex-ante known data generating process, seems to achieve low bias and low variance and provide appropriate fit. In the real world, however, finding a sweet spot between over-fitting and under-fitting is less trivial and requires appropriate definition of model selection criteria and exploration of different levels of model complexities. The key takeaway here is that none of these techniques are categorically wrong; it really depends on what tradeoffs we have to make to achieve as close to low bias and low variance as is possible. We need the model to adapt as the real-world adapts and ideally contend with polluted information with minimal supervision, while being as transparent as possible. These are all competing objectives and need to be accounted for within the applied risk domain.

Within a risk scoring context a simple example of being able to communicate to the business the supervision and complexity tradeoff is shown below.

Fig 3: Supervision and Complexity Trade-offs

Here we see that given the characteristics of the dataset, there is a trade-off between coupling the model with the data and the level of transparency of the ultimate model.

Another application of ML in credit risk is within sentiment analysis. A generalized sentiment analysis pipeline is provided below:

Fig 4: Generalized Sentiment Analysis Pipeline

Sentiment analysis methods can generally be split into either deterministic models that rely on a dictionary (bag of words) or neural network models that typically engage a deep learning exercise. The sentiment analysis can be further divided into ‘classification’ and ‘attribution’ where in each case given a target variable, a sentiment polarity label is assigned to a particular article (in the classification case) or attributes segmented within articles which are actually relevant and would impact the target variable.

Fig 5: Usage in Sentiment Analysis

Once again we see the considerable tradeoffs between supervision and complexity. Dependent on the risk context any of these techniques would be applicable.

We have covered the key drivers of the adoption of ML within a credit risk context and showed a few simple examples of the uses. It is important to consider the tradeoffs which are largely dependent on the actual final application. ML functions are a complementary class of techniques but they are not a panacea for every use case within credit risk. Ultimately, being able to communicate their value to the business audience and why they are being used in this context is of critical importance.

Moody Hadi
Senior Director – Innovation & Product Research
Risk Services
S&P Global Market Intelligence

Danny Haydon
Head of Relationship Management, Americas
Risk Services
S&P Global Market Intelligence

All figures are for illustrative purposes only. Source: S&P Global Market Intelligence as of July 2018. Content including credit-related and other analyses are statements of opinion as of the date they are expressed and are not statements of fact, investment recommendations or investment advice. S&P Global Market Intelligence and its affiliates assume no obligation to update the content following publication in any form or format.

The authors would like to express their thanks to Max Kuhn and Jonathan Regenstein from R Studio who provided their expertise and input into the article contents. R Studio is not affiliated with S&P Global or its divisions.

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Credit Analysis
2019 Credit Risk Perspectives: Is The Credit Cycle Turning? A Fundamentals View

Mar. 15 2019 — On November 20, 2018, a joint event hosted by S&P Global Market Intelligence and S&P Global Ratings took place in London, focusing on credit risk and 2019 perspectives.

Pascal Hartwig, Credit Product Specialist, and I provided a review of the latest trends observed across non-financial corporate firms through the lens of S&P Global Market Intelligence’s statistical models.1 In particular, Pascal focused on the outputs produced by a statistical model that uses market information to estimate credit risk of public companies; if you want to know more, you can visit here.

I focused on an analysis of how different Brexit scenarios may impact the credit risk of European Union (EU) private companies that are included on S&P Capital IQ platform.

Before, this, I looked at the evolution of their credit risk profile from 2013 to 2017, as shown in Figure 1. Scores were generated via Credit Analytics’ PD Model Fundamentals Private, a statistical model that uses company financials and other socio-economic factors to estimate the PD of private companies globally. Credit scores are mapped to PD values, which are based on/derived from S&P Global Ratings Observed Default Rates.

Figure 1: EU private company scores generated by PD Model Fundamentals Private, between 2013 and 2017.

Source: S&P Global Market Intelligence.2 As of October 2018.

For any given year, the distribution of credit scores of EU private companies is concentrated below the ‘a’ level, due to the large number of small revenue and unrated firms on the S&P Capital IQ platform. An overall improvement of the risk profile is visible, with the score distribution moving leftwards between 2013 and 2017. A similar picture is visible when comparing companies by country or industry sector,3 confirming that there were no clear signs of a turning point in the credit cycle of private companies in any EU country or industry sector. However, this view is backward looking and does not take into account the potential effects of an imminent and major political and economic event in the (short) history of the EU: Brexit.

To this purpose, S&P Global Market Intelligence has developed a statistical model: the Credit Analytics Macro-scenario model enables users to study how potential future macroeconomic scenarios may affect the evolution of the credit risk profile of EU private companies. This model was developed by looking at the historical evolution of S&P Global Ratings’ rated companies under different macroeconomic conditions, and can be applied to smaller companies after the PD is mapped to a S&P Global Market Intelligence credit score.

“Soft Brexit” (Figure 2): This scenario is based on the baseline forecast made by economists at S&P Global Ratings and is characterized by a gentle slow-down of economic growth, a progressive monetary policy tightening, and low yet volatile stock-market growth.4

Figure 2: “Soft Brexit” macro scenario.5

Source: S&P Global Ratings Economists. As of October 2018.

Applying the Macro-scenario model, we analyze the evolution of the credit risk profile of EU companies over a three-year period from 2018 to 2020, by industry sector and by country:

  • Sector Analysis (Figure 3):
    • The median credit risk score within specific industry sectors (Aerospace & Defense, Pharmaceuticals, Telecoms, Utilities, and Real Estate) shows a good degree of resilience, rising by less than half a notch by 2020 and remaining comfortably below the ‘b+’ threshold.
    • The median credit score of the Retail and Consumer Products sectors, however, is severely impacted, breaching the high risk threshold (here defined at the ‘b-’ level).
    • The remaining industry sectors show various dynamics, but essentially remain within the intermediate risk band (here defined between the ‘b+’ and the ‘b-’ level).

Figure 3: “Soft Brexit” impact on the median credit risk level of EU private companies, by industry.

Source: S&P Global Market Intelligence. As of October 2018.

  • Country Analysis (Figure 4):
    • Although the median credit risk score may not change significantly in certain countries, the associated default rates need to be adjusted for the impact of the credit cycle.6 The “spider-web plot” shows the median PD values for private companies within EU countries, adjusted for the credit cycle. Here we include only countries with a minimum number of private companies within the Credit Analytics pre-scored database, to ensure a robust statistical analysis.
    • Countries are ordered by increasing level of median PD, moving clock-wise from Netherlands to Greece.
    • Under a soft Brexit scenario, the PD of UK private companies increases between 2018 and 2020, but still remains below the yellow threshold (corresponding to a ‘b+’ level).
    • Interestingly, Italian private companies suffer more than their Spanish peers, albeit starting from a slightly lower PD level in 2017.

Figure 4: “Soft Brexit” impact on the median credit risk level of EU private companies, by country.

Source: S&P Global Market Intelligence. As of October 2018.

“Hard Brexit” (Figure 5): This scenario is extracted from the 2018 Stress-Testing exercise of the European Banking Authority (EBA) and the Bank of England.7 Under this scenario, both the EU and UK may go into a recession similar to the 2008 global crisis. Arguably, this may seem a harsh scenario for the whole of the EU, but a recent report by the Bank of England warned that a disorderly Brexit may trigger a UK crisis worse than 2008.8

Figure 5: “Hard Brexit” macro scenario.9

Sources:”2018 EU-wide stress test – methodological note” (European Banking Authority, November 2017) and “Stress Testing the UK Banking system: 2018 guidance for participating banks and building societies“ (Bank of England, March 2018).

Also in this case, we apply the Macro-scenario model to analyze the evolution of the credit risk profile of EU companies over the same three-year period, by industry sector and by country:

  • Sector Analysis (Figure 6):
    • Despite all industry sectors being severely impacted, the Pharmaceuticals and Utilities sectors remain below the ‘b+’ level (yellow threshold).
    • Conversely, the Airlines and Energy sectors join Retail and Consumer Products in the “danger zone” above the ‘b-’ level (red threshold).
    • The remaining industry sectors will either move into or remain within the intermediate risk band (here defined between the ‘b+’ and the ‘b-’ level).

Figure 6: “Hard Brexit” impact on the median credit risk level of EU private companies, by industry.

Source: S&P Global Market Intelligence. As of October 2018.

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  • Country Analysis (Figure 7):
    • Under a hard Brexit scenario, the PD of UK private companies increases between 2017 and 2020, entering the intermediate risk band and suffering even more than its Irish peers.
    • Notably, by 2020 the French private sector may suffer more than the Italian private sector, reaching the attention threshold (here shown as a red circle, and corresponding to a ‘b-’ level).
    • While it is hard to do an exact like-for-like comparison, it is worth noting that our conclusions are broadly aligned with the findings from the 48 banks participating in the 2018 stress-testing exercise, as recently published by the EBA:10 the major share of 2018-2020 new credit risk losses in the stressed scenario will concentrate among counterparties in the UK, Italy, France, Spain, and Germany (leaving aside the usual suspects, such as Greece, Portugal, etc.).

Figure 7: “Hard Brexit” impact on the median credit risk level of EU private companies, by country.

Source: S&P Global Market Intelligence. As of October 2018.

In conclusion: In Europe, the private companies’ credit risk landscape does not yet signal a distinct turning point, however Brexit may act as a pivot point and a catalyst for a credit cycle inversion, with an intensity that will be dependent on the Brexit type of landing (i.e., soft versus hard).

1 S&P Global Ratings does not contribute to or participate in the creation of credit scores generated by S&P Global Market Intelligence.
2 Lowercase nomenclature is used to differentiate S&P Global Market Intelligence credit scores from the credit ratings issued by S&P Global Ratings.
3 Not shown here.
4 Measured via Gross Domestic Product (GDP) Growth, Long-term / Short-term (L/S) European Central Bank Interest Rate Spread, and FTSE100 or STOXX50 stock market growth, respectively.
5 Macroeconomic forecast for 2018-2020 (end of year) by economists at S&P Global Ratings; the baseline case assumes the UK and the EU will reach a Brexit deal (e.g. a “soft Brexit”).
6 When the credit cycle deteriorates (improves), default rates are expected to increase (decrease).
7 Source: “2018 EU-wide stress test – methodological note” (EBA, November 2017) and “Stress Testing the UK Banking system: 2018 guidance for participating banks and building societies”. (Bank of England, March 2018).
8 Source: “EU withdrawal scenarios and monetary and financial stability – A response to the House of Commons Treasury Committee”. (Bank of England, November 2018).
9 As a hard Brexit scenario, we adopt the stressed scenario included in the 2018 stress testing exercise and defined by the EBA and the Bank of England.
10 See, for example, Figure 18 in “2018 EU-Wide Stress Test Result” (EBA November 2018), found at:https://eba.europa.eu/documents/10180/2419200/2018-EU-wide-stress-test-Results.pdf

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2019 Credit Risk Perspectives: Is The Credit Cycle Turning? A Market-Driven View

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Credit Analysis
2019 Credit Risk Perspectives: Is The Credit Cycle Turning? A Market Driven View

Mar. 15 2019 — On November 20, 2018, a joint event hosted by S&P Global Market Intelligence and S&P Global Ratings took place in London, focusing on credit risk and 2019 perspectives.

Giorgio Baldassarri, Global Head of the Analytic Development Group, and I provided a review of the latest trends observed across non-financial corporate firms through the lens of S&P Global Market Intelligence’s statistical models.1 In particular, Giorgio focused on the analysis of the evolution of the credit risk profile of European Union companies between 2013 and 2017, and how this may change under various Brexit scenario; if you want to know more, you can visit here.

I started with an overview of key trends of the credit risk of public companies at a global level, before diving deeper into regional and industry sector-specific performance and pointing out some key drivers of country- and industry-level risk. Credit Analytics Probability of Default (PD) Market Signals model was used to derive these statistics. This is a structural model (enhanced Merton approach) that produces PD values for all public corporates and financial institutions globally. Credit scores are mapped to PD values, which are derived from S&P Global Ratings observed default rates (ODRs).

From January 2018 to October 2018, we saw an increase in the underlying PD values generated by PD Market Signals across all regional S&P Broad Market Indices (BMIs), as shown in Figure 1. For Asia Pacific, Europe, and North America, the overall increase was primarily driven by the significant shift in February 2018, which saw an increase in the PD between 100% to 300% on a relative basis. The main mover on an absolute basis was Latin America, which had a PD increase of over 0.35 percentage points.

Figure 1: BMI Benchmark Median credit scores generated by PD Market Signals, between January 1 and October 1, 2018.

Source: S&P Global Market Intelligence. As of October 2018.

Moving to the S&P Europe BMI in Figure 2, we can further isolate three of the main drivers behind the overall increase in PDs (this time measured on a relative basis), namely Netherlands, France, and Austria. Among these, the Netherlands had the most prominent increase between August and October. Again, one can identify the significant increase in the PDs in February, ranging from 150% to 230%, across all three countries. Towards July, we saw the spread between the three outliers shrink significantly. In August and September, however, the S&P Europe BMI began to decrease again, whilst all three of our focus countries were either increasing in risk (Netherlands, from a 150% level in the beginning of August to a 330% level at the end of September) or remaining relatively constant (France and Austria).

Figure 2: European Benchmark Median PD scores generated by PD Market Signals model, between January 1 and October 1, 2018.

Source: S&P Global Market Intelligence. As of October 2018.

In the emerging markets, Turkey, United Arab Emirates (UAE), and Qatar were the most prominent outliers from the S&P Mid-East and Africa BMI. As visible in Figure 3, the S&P Mid-East and Africa BMI saw less volatility throughout 2018 and was just slightly above its start value as of October. Two of the main drivers behind this increase were the PDs of the country benchmarks for Turkey and the UAE. Turkey, especially, stood out: the PD of its public companies performed in line with the S&P Mid-East and Africa BMI until mid-April, when it increased significantly and showed high volatility until October. On the other hand, the benchmark for Qatar decreased by over 60% between May and October.

Figure 3: S&P Mid-East and Africa BMI Median PD scores generated by PD Market Signals, between January 1 and October 1, 2018.

Source: S&P Global Market Intelligence. As of October 2018.

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We then looked at different industries in Europe. As shown in Figure 4, the main shift in S&P BMIs occurred in February, with most industries staying on a similar level for the remaining period. The main outliers were the S&P Industrials, Materials, and, in particular, Consumer Discretionary Europe, Middle East, and Africa (EMEA) BMIs. The S&P Energy BMI saw some of the highest volatility, but was able to decrease significantly throughout September. At the same time, the Materials sector saw a continuous default risk increase from the beginning of June, finishing at an absolute median PD level of slightly over 1% when compared to the start of the year.

Figure 4: S&P EMEA Industry BMI Median PD scores generated by PD Market Signals, between January 1 and October 1, 2018.

Source: S&P Global Market Intelligence. As of October 2018.

In conclusion, looking at the public companies, Latin America, Asia Pacific, and Europe pointed towards an increase of credit risk between January 2018 and October 2018, amid heightened tensions due to the current U.S. policy towards Latin-American countries, the U.S./China trade war, and Brexit uncertainty.

1 S&P Global Ratings does not contribute to or participate in the creation of credit scores generated by S&P Global Market Intelligence.

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2019 Credit Risk Perspectives: Is The Credit Cycle Turning? A Fundamentals View

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Capital Markets
Concentration and Cross Holdings of Chinese Banks

Highlights

This analysis was published by S&P Global Market Intelligence and not by S&P Global Ratings, which is a separately managed division of S&P Global. This is not investment advice or a stock suggestion.

Mar. 01 2019 — Do you know out of the Big Four state-owned commercial banks in China, only one has three US institutions ranked as its top five largest holders and it is not the one with the largest market cap?

Do you know Temasek Holdings, the Singapore sovereign wealth fund, is one of the top five holders of another Big Four Chinese banks?

Do you know BlackRock Inc., the largest asset management company globally, has its relative % of equity of Chinese Banks in reverse order to their respective market cap rankings?

Do you know Standard Life Aberdeen, a European asset manager, has allocated over 10% of its asset towards global banks and close to 5% towards Chinese companies but only 0.33% of that invested in Chinese banks?

In this blog, we leveraged the newly exposed Ownership content and other features of the S&P Global Market Intelligence platform to gain insights into the above by analyzing the concentration and cross-board holdings of the largest publicly-traded banks. We also demonstrated how one can use the same content from the S&P Global Market Intelligence platform to replicate such results.

As of February 12, 2019, there were 28 global banks with a market capitalization greater than USD $50 billion as seen in Figure 1 below:

Figure 1: Screening Criteria for Global Banks with Market Capitalization of more than USD $50 billion

Source: S&P Global Market Intelligence as of 12 Feb 2019. For illustrative purposes only.

The top 10 are dominated by Chinese and US banks with one UK bank, HSBC Holdings Plc, placed at #7, and one Canadian bank, Royal Bank of Canada, made the 11th spot (Table 1).

Table 1: Top 11 Global Banks by Market Capitalization

Source: S&P Global Market Intelligence as of 12 Feb 2019. For illustrative purposes only.

HSBC made news¹ last year when a Chinese asset management firm, Ping An Asset Management, became its largest holder with 7.1%, overtaking its 2nd largest holder, BlackRock, with 5.8% of its holdings. This was due to the additional purchase of 166 million shares by Ping An and the sale of 182 mil shares by BlackRock (Table 2).

Table 2: Top Two Shareholders of HSBC Holdings and Their Change in Shares

Source: S&P Global Market Intelligence as of 12 Feb 2019. For illustrative purposes only.

Grouping those 28 banks by country and sorting in descending order by their largest holder concentration, we can see that the BRIC countries (Brazil, Russia, India, China) have the highest holder concentration, followed by Spain and USA, with Canada and Japan having the lowest holder concentration as seen in Table 3.

Table 3: Top 28 Global Banks Grouped by Country, Sorted in Descending Order by Top Owner Concentration

Source: S&P Global Market Intelligence as of 12 Feb 2019. For illustrative purposes only.

Expanding on those seven Chinese banks, we see that Central Huijin Investment Ltd. is the largest holder for four of those banks and that the Ministry of Finance is either the largest or the second largest for three of those banks (Table 4). However, it is interesting to see these varied cross-board ownerships of selective Chinese banks.

  • Three US institutions, BlackRock, Vanguard Group, and Capital Research are the second, third, and fourth largest holders, respectively, of China Construction Bank. The combined ownership of these three institutions is 4.1%, and is not replicable for the remaining banks (Table 4).
  • With 19% ownership of Bank of Communication, HSBC is the second largest shareholder of a bank with one of the smallest market capitalization in the region (Table 4).
  • Temasek Holdings’ 2.0% share made it the fifth largest owner of the largest bank in terms of market cap, Industrial and Commercial Bank of China (not shown in table).


Table 4: Top Shareholders of the Seven Largest Chinese Banks

Source: S&P Global Market Intelligence as of 12 Feb 2019. For illustrative purposes only.

For other BRIC banks, we also noted cross-ownership. For example,

  • American institutions placed second to fourth in ownership of PAO Sberbank of Russia with a combined total of 6.6% after Central Bank of the Russian Federation’s 52% share.
  • JP Morgan was the top holder of the Indian HDFC Bank with its 17.4% share.
  • Standard Life Aberdeen’s 4.6% ownership of Banco Bradesco and Dodge & Cox’s 1.9% ownership of Itaúsa placed both respectively as the third largest holder of these Brazilian banks.


In addition to analyzing ownership of both public and private companies, the S&P Global Market Intelligence platform can also be used to look at holdings by institutions, filtering by additional factors like industry and region.

Here is an example of all the BlackRock holdings filtered just for US and Chinese banks in descending market value (Table 5). From this list, we can see that out of its top 10 holdings, three were Chinese banks: China Construction Bank, Industrial & Commercial Bank of China, and Bank of China, ranked as its 6th, 8th and 9th positions. However when filtered based on % of Shares Outstanding of those banks, the respective orders are reversed: 1.66%, 5.06%, and 7.09%.

Table 5: BlackRock, Inc’s Holdings of Public Chinese and US Banks

Source: S&P Global Market Intelligence as of 12 Feb 2019. For illustrative purposes only.

For an Asian sovereign wealth fund like Temasek Holdings, all of its holdings of public bank shares are non-US banks. Singapore’s DBS Group Holdings is its largest bank holdings followed by Industrial and Commercial Bank of China as its second largest bank holding (Table 6).

Table 6: Temasek’s Holdings of Public Global Banks

Source: S&P Global Market Intelligence as of 12 Feb 2019. For illustrative purposes only.

Lastly, we looked at ownership of a European asset manager, Standard Life Aberdeen Plc. China Construction Bank and Industrial and Commercial Bank of China are its 4th and 6th largest holdings among its Asian bank holdings (Table 7). The percentages are relatively small when compared to its holdings of banks in the ASEAN region:

Table 7: Standard Life Aberdeen Plc’s Holdings of Public Asia-Pacific Banks

Source: S&P Global Market Intelligence as of 12 Feb 2019. For illustrative purposes only.

… Or other sectors of the Chinese equity market:

Table 8: Standard Life Aberdeen Plc’s Holdings of Public Chinese Companies

Source: S&P Global Market Intelligence as of 12 Feb 2019. For illustrative purposes only.

In summary, based on the above Ownership and Holder analysis from the S&P Global Market Intelligence platform, we are able to obtain deeper insights by showing you examples on the holdings of specific asset managers, sovereign wealth fund, commercial banks on some of the largest publicly-traded banks in China. By using the same content from the platform, we also demonstrated how we can easily replicate such results by industries, geographies etc.

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¹Source: South China Morning Post: China’s Ping An Insurance overtakes BlackRock to become HSBC’s biggest shareholder, 7 Nov 2018

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Credit Analysis
Flying Into The Danger Zone; Norwegian Air Shuttle

Highlights

This analysis was published by S&P Global Market Intelligence and not by S&P Global Ratings, which is a separately managed division of S&P Global. This is not investment advice or a stock suggestion.

Feb. 13 2019 — The headwinds are picking up for Norwegian Air Shuttle ASA (“Norwegian”), the eighth largest airline in Europe. The carrier has been battling with rising fuels costs, increased competition from legacy carriers, and persistent aircraft operational issues. Norwegian’s problems are a continuation of what have been turbulent months for budget airlines in Europe resulting in a collapse of Primera Air, based in Denmark, near-default of WOW air, Iceland’s budget carrier, and most recently bankruptcy of Germania.

When we pull back the curtain and review the creditworthiness of European airlines to explore further some of the causes for Norwegian’s turbulent period, we see Norwegian’s business strategy and financial structure have made the carrier highly exposed. Coupled with the traditionally slow winter season, the airline may have to navigate through the storm clouds forming on the horizon.

A View From Above

S&P Global Market Intelligence has developed CreditModelTM Corporates 2.6 (CM2.6), a statistical model trained on credit ratings from our sister division, S&P Global Ratings. The model combines multiple financial ratios to generate a quantitative credit score and offers an automated solution to efficiently assess the credit risk of both public and private companies globally.1 Within CreditModel, the airline industry is treated as a separate global sub-model to better encompass the unique characteristics of this industry.

Figure 1 shows the overview of S&P Global Market Intelligence credit scores obtained using CreditModel for European airlines. Norwegian’s weak position translate into the weakest credit score among its competitors. The implied ‘ccc+’ credit score suggests that Norwegian is vulnerable to adverse business, financial, or economic conditions, and its financial commitments appear to be unsustainable in the long term. In addition to Norwegian, Flybe and Croatian Airlines rank among the riskiest carriers in Europe and share a similar credit risk assessment. The airlines with the best credit scores are also Europe’s biggest airlines (Lufthansa, Ryanair, International Airlines Group (IAG), and easyJet). The exception among the top five European airlines is Air France-KLM, which is crippled by labour disputes and its inability to reshape operations and improve performance.

Figure 1: Credit Risk Radar of European Airspace
Overview of credit scores for European airlines

Source: S&P Global Market Intelligence. For illustrative purposes only.
Note: IAG operates under the British Airways, Iberia, Vueling, LEVEL, IAG Cargo, Avios, and Aer Lingus brands. (January 3, 2019)

S&P Global Market Intelligence’s sister division, S&P Global Ratings, issued an industry outlook for airlines in 2019 noting that the industry is poised for stability.2 It stated the global air traffic remains strong and is growing above its average rate at more than 6% annually. The report also cited rising interest rates dampening market liquidity while increasing the cost of debt refinancing and aircraft leases. Oil prices are expected to settle, and any further gradual increases in oil prices are expected to be compensated by rising airfares and fees. The most significant risks for airlines are geopolitical. Potential downside scenarios include a crisis in the Middle East or other disruptions in oil, causing oil prices to spike. The possibility of trade wars and uncertainty surrounding the Brexit withdrawal agreement represent additional sources of potential disruption or weakening in travel demand.

Flying into the danger zone

Although Norwegian has so far dismissed any notion of financial distress as speculation, it has simultaneously implemented a series of changes to prevent further turbulence.3 The airline announced a $230mm cost-saving program that included discontinuing selected routes, refinancing new aircraft deliveries, divesting a portion of the existing fleet, and offering promotional fares to passengers to shore up liquidity.

In Figure 2, we rank Norwegian’s financial ratios within the global airline industry and benchmark them against a selected set of competitor European budget carriers (Ryanair, easyJet, and Wizz Air). Through this chart, we can conclude that Norwegian’s underlying problems are persistent and the company’s financial results are weak. Norwegian’s business model of rapid growth and a debt-heavy capital structure have resulted in severe stress for its financials. Norwegian ranks among the bottom 10% of the worst airlines in the industry on debt coverage ratios, margins, and profitability. This is in sharp contrast to other European budget carriers, which are often ranked among the best in the industry. On the flip side, Norwegian’s high level of owned assets represents its strong suit and gives the carrier some flexibility to adjust its operations and improve performance in the future.

Figure 2: Flying at Low Altitude
Norwegian’s financial ratios are among the worst in the industry

Source: S&P Global Market Intelligence. For illustrative purposes only. (January 3, 2019)
Note: Presented financial ratios are used in CreditModelTM Corporates 2.6 (Airlines) to generate quantitative credit score in Figure 1.

Faster, Higher, Farther

Norwegian has undergone a rapid expansion in recent years, introducing new routes and flying over longer distances. Between 2008 and 2018, the carrier quadrupled its fleet from 40 to 164 planes.4 This enabled it to fly more passengers and become the third largest budget airline in Europe, behind Ryanair and easyJet. However, unlike its low-cost rivals, Norwegian ventured into budget long-haul flights. After establishing its new base at London Gatwick, it started operating services to the U.S., South-East Asia, and South America.

As a result of this expansion, Norwegian’s capacity as measured by available seat kilometres (ASK) and traffic as measured by revenue passenger kilometres (RPK) grew nine-fold between 2008 and 2018, as depicted in Figure 3. By offering deeply discounted fares, the carrier was able to attract more passengers and significantly grow its revenues, which were expected to reach $5bn in 2018. However, to be able to support this rapid growth, Norwegian accumulated a significant amount of debt and highly increased its financial leverage. This rising debt is putting Norwegian under pressure to secure enough liquidity to repay maturing debt obligations.

Figure 3: Shooting for the Stars
Norwegian’s rapid growth propelled by debt

Source: S&P Global Market Intelligence. All figures are converted into U.S. dollars using historic exchange rates. Figures for 2018 are estimated based on annualized YTD 2018 figures. For illustrative purposes only. (January 3, 2019)

Norwegian’s strategy to outpace growing debt obligations by driving revenue growth is coming under pressure. The data tells us that expansion to the long-haul market and the undercutting of competitors to gain market share proved to be costly and negatively impacted Norwegian’s bottom line. Operational performance, measured as unit revenue (passenger revenue per ASK) and yield (passenger revenue per RPK), have been slipping continuously since 2008, as depicted in Figure 4. Negative free operating cash flow required Norwegian to continuously find new sources of capital to finance its operations, and profitability suffered. The carrier was able to ride a tailwind of low oil prices and cheap financing for a while, however, the winds seem to be turning.

Figure 4: Gravitational Pull
Slipping operational and financial performance

Source: S&P Global Market Intelligence, Norwegian Air Shuttle ASA: “Annual Report 2017”, Norwegian Air Shuttle ASA: “Interim report - Third quarter 2018”. Figures for 2018 are estimated based on annualized YTD 2018 figures. For illustrative purposes only. (January 3, 2019)

Norwegian’s plan to outrun a looming mountain of debt obligations is resulting in a turbulent flight. While growing its top line, the carrier has been unable to convert increased capacity and traffic into consistent profit. With a stable industry outlook and cost-cutting measures in place, Norwegian lives to fly another day. However, any additional operational issues or adverse macroeconomic developments could send Norwegian deep into the danger zone.

Learn more about S&P Global Market Intelligence’s Credit Analytics models.
Learn more about S&P Global Market Intelligence’s RatingsDirect®.

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 credit scores for single or multiple companies, and perform scenario analysis.

S&P Global Market Intelligence’s RatingsDirect® product is the official desktop source for S&P Global Ratings’ credit ratings and research. S&P Global Ratings’ research cited in this blog is available on RatingsDirect®.

1 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.
2 S&P Global Ratings: “Industry Top Trends 2019: Transportation”, November 14, 2018. https://www.capitaliq.com/CIQDotNet/CreditResearch/viewPDF.aspx?pdfId=36541&from=Research.
3 Norwegian Air Shuttle ASA, “Update from Norwegian Air Shuttle ASA”, press release, December 24, 2018 (accessed January 3, 2019), https://media.uk.norwegian.com/pressreleases/update-from-norwegian-air-shuttle-asa-2817995.
4 Norwegian Air Shuttle ASA: “Investor Presentation Norwegian Air Shuttle”, September 2018.

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