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Companies And Sectors Most Impacted By U.S.-Chinese Tariffs

C&I Loan Growth Pops In Q2, But Tax Reform’s Role Remains Unclear

Banking

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Peeking Into The Future Without Staring At A Crystal Ball: Brexit Scenarios And Their Impact On Energy Firms’ Credit Risk

Credit Analysis
Companies And Sectors Most Impacted By U.S.-Chinese Tariffs

Highlights

President Trump’s proposed tariffs impacted the short-term market perceived credit quality of U.S. firms more than Chinese ones

May. 07 2018 — Written by Camilla Yanushevsky, with analysis contributions from Paul Bishop and Jim Elder, Directors of Risk Services, Melissa Doscher, Senior Manager, Risk Services, and Chris Rogers, Panjiva Research Director.

Consumer confidence soared to an 18-year high in February, on the tailwinds of the passage of the most sweeping tax rewrite in over 30 years at the end of 2017. But now, with U.S. President Donald Trump’s ramp up of protectionist rhetoric and heightened concerns of a global trade war, the optimism has begun to diminish. The Conference Board Consumer Confidence Index declined to 127.7 in March, from the high of 130.0 in February, with many pointing to President Trump’s tariffs as playing a major role for the drop off. [i] Companies have already started to examine the potential impact to their supply chains and are reevaluating the way they conduct business. Although the implementation details of the President’s tariffs have yet to be provided, we went ahead and evaluated the levies’ potential market implications.

U.S. tariff announcements have occurred 31 times in the last 35 years, according to an S&P Global Market Intelligence analysis using Kensho, provider of next-generation analytics and data visualization systems, which was recently acquired by S&P Global. On a rolling quarterly basis, following the announcement, the S&P 500 increased, on average, by 2.79%, trading positively more than 78% of the time. Energy stocks tended to be the bottom performing among the S&P 500 sectors, while S&P 500 Information Technology and S&P 500 Consumer Discretionary companies posted slight positive returns for the quarter.

Figure 1: S&P 500 average return and percent of trades positive after U.S. tariff announcement
S&P 500 average return and percent of trades positive after U.S. tariff announcement

Following President Trump’s March 22, 2018 signing of an executive memorandum to impose regulatory tariffs on up to $60 billion in Chinese products belonging to the aerospace, information and communication technology, and machinery industries, among others, we examined and highlighted notable sector, industry, and company-level probability of default (PD) changes as indicated by our PD Market Signal Model, a structural model that calculates the likelihood of a company defaulting on its debt or entering bankruptcy protection over a one- to five-year horizon.[ii]

U.S. Financials, Energy companies among the biggest losers

Following the memorandum signing, the U.S. Financials sector saw the largest escalation in market-perceived credit risk. The sector’s PD increased 29.32% from 0.39% on March 21, 2018 to just under 0.50% on March 29, 2018, nearly crossing into a speculative grade equivalent (bb+) median credit score for the sector. [iii]

While not directly impacted by President Trump’s tariffs, diversified banks and investment banking and brokerage companies are reexamining their business investment and lending decisions due to the levies’ potential negative repercussions on economic growth.

According to an analysis conducted by the Tax Foundation: “$37.5 billion in tariffs would lower GDP and wages 0.1 percent, lower employment by the equivalent of 79,000 fewer full-time jobs in the long run, and make the US tax burden less progressive.” [iv] On such concerns, as well as the possibility of retaliation by other countries, fund managers have already begun to reduce their U.S. holdings and look for opportunities overseas. [v]

President Trump’s proposed tariffs also dealt a significant blow to the U.S. Energy sector, which relies heavily on steel and aluminum for various projects, including pipeline construction and wind and solar power installation. Following the announcement, the U.S. Energy’s PD jumped 25.15%, from 1.56% on March 21, 2018 to 1.95% on March 29, 2018.

President Trump’s proposed tax on steel and aluminum imports will not only raise the costs of these projects and drive up prices for consumers, but in the long run can also reduce the demand for clean energy, while harming the quest for ‘American energy dominance’ in the process.

Figure 2: U.S. 1-week median Market Signal Probability of Default change by GICS sector (%)

U.S. 1-week median Market Signal Probability of Default change by GICS sector (%)

Taking a deeper dive into subsectors, aluminum, a subset of Materials, saw the largest increase in PD of 120.71%. Copper, another subset of Materials, also saw a substantial incline in PD of 120.54%. Both these important industrial metals were singled out on the President’s proposed list of tariff targets. [ii]

Figure 3: U.S. largest increases in 1-week Market Signal Probability of Default by industry (%)

U.S. largest increases in 1-week Market Signal Probability of Default by industry (%)

China’s Consumer Discretionary sector takes a blow

Chinese consumer discretionary companies also are bearing the brunt of the looming trade war, with President Trump’s tariffs targeting a range of consumer goods from China including flat screen televisions, household appliances, and auto parts. Immediately following the announcement, the sector observed the largest market-perceived escalation in credit risk. The sector saw its PD increase 8.09% from 1.82% on March 21, 2018 to 1.96% on March 29, 2018.

President Trump’s tariffs also carry far-reaching implications on China’s property market, which after two stellar years of property sales and developer margins, is seeing a toughening of industry conditions — tighter lending rules, restrictive policies to control price appreciation, and intensifying competition. [vi]

Fears of faster-than-expected rate hikes and inflation growth spiraling from the tariff battle does not bode well for Chinese developers looking for capital overseas. Following the signing of the March 22, 2018 memorandum, China’s real estate sector observed a PD uptick of 6.4%, from 0.93% on March 21, 2018 to 0.99% on March 29, 2018.

Figure 4: China 1-week median Market Signal Probability of Default change by GICS sector (%)

China 1-week median Market Signal Probability of Default change by GICS sector (%)

On a subsector level, China’s property and casualty insurance, a subset of Financials, observed the largest one-week escalation in credit risk with its PD jumping 133.9% from 0.23% to 0.53%. The industry’s PD uptick is likely a ‘spillover’ of the tightening of the credit markets for property developers to the insurers offering project assurance.

Figure 5: China largest increases in 1-week Market Signal Probability of Default by industry (%)

China largest increases in 1-week Market Signal Probability of Default by industry (%)

Tariff headwinds hit both sides

On a company-level, roughly 65% of U.S. and 58% of Chinese publicly traded companies experienced an increase in their one-year PD the week following the announcement. U.S. companies saw a larger escalation in credit risk, with a median PD change of 13%, compared to China’s 3%. Companies with U.S./China cross-border exposure were also more likely to see an increase in credit risk.

Figure 6: 25 largest increases in 1-week Market Signal Probability of Default by U.S. S&P Global Market Intelligence-covered companies with exposure to China (%)

25 largest increases in 1-week Market Signal Probability of Default by U.S. S&P Global Market Intelligence-covered companies with exposure to China (%)

Figure 7: 25 largest increases in 1-week Market Signal Probability of Default by Chinese S&P Global Market Intelligence-covered companies with exposure to the U.S. (%)

25 largest increases in 1 week Market Signal Probability of Default by Chinese S&P Global Market Intelligence

Some U.S. companies uneasy over China tariff threat to supply chains

Considering the complexity of international supply chains, many market participants are on edge that new tariffs might have damaging unintended consequences. According to supply chain market intelligence firm Panjiva Inc., which was recently acquired by S&P Global:

“The targeting [striking] of China’s duties is significantly more focused than those introduced by the U.S., with 106 categories compared to 1333 in America’s section 301 duties. They are also more focused in terms of products, with the top three products accounting for 71.7% of total product coverage. Those include aircraft (HS 8802.40, worth $14.05 billion, or 26.3% of the total, soybeans (HS 1201.90 worth $13.96 billion) and midsize engine cars (8703.23, $10.32 billion).

The inclusion of soybeans is particularly notable given that the promotion of imports were a part of the package of trade enhancements announced when President Trump visited China in November 2017.” [vii]

Figure 8: Focused strike on politically important U.S. products

Focused-strike-on-politically-important-U.S.-products

In summary, our PD Market Signal model shows that President Trump’s proposed tariffs impacted the short-term market perceived credit quality of U.S. firms more than Chinese ones. While the trade penalties have yet to be implemented, we saw steep tariffs and protectionism policies spur declines in global trade in the 1930s, stifle economic growth, and contribute to the depth of the Great Depression. More recently, we saw trade fears trigger volatility in global equities. Likewise, President Trump’s tariffs will likely create similar supply and demand imbalances, while boosting prices for consumers, increasing costs for manufacturers, and potentially exacerbating trade tensions with other countries. Companies, as well as individuals, should be especially alert as the negotiations play out.

This report was updated on May 15, 2018 to add the last two columns, Implied Credit Score and S&P Rating/Outlook, to Figures 6 and 7, as well as to clarify that the companies listed have reported revenue exposure to China on a consolidated basis.

[i] The Conference Board Consumer Confidence Index Declined in March (March 27, 2018). Retrieved April 25, 2018, from https://www.conference-board.org/data/consumerconfidence.cfm

[ii] Notice of Determination and Request for Public Comment Concerning Proposed Determination of Action Pursuant to Section 301: China’s Acts, Policies, and Practices Related to Technology Transfer, Intellectual Property, and Innovation (n.d.). Retrieved April 25, 2018, from https://ustr.gov/sites/default/files/files/Press/Releases/301FRN.pdf

[iii] Mapping Letter Grade Score to Probability of Default Technical Reference Guide. Published November 2017.

[iv] Modeling the Impact of President President Trump’s Proposed Tariffs (April 12, 2018). Retrieved April 25, 2018, from https://taxfoundation.org/modeling-impact-president-President Trumps-proposed-tariffs/

[v] President Trump’s tariffs prompting some U.S. fund managers to look overseas. (March 9, 2018). Retrieved April 25, 2018, from https://www.reuters.com/article/us-usa-stocks-weekahead/President Trumps-tariffs-prompting-some-u-s-fund-managers-to-look-overseas-idUSKCN1GL1KV

[vi] China’s Developers Strengthen Defense for A Funding Crunch (April 22, 2018). Retrieved April 25, 2018, from S&P Global Ratings.

[vii] Four Facts About China’s $53 Billion President Trump Tariff Retaliation (April 5, 2018). Retrieved April 25, 2018, from Panjiva Inc.

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Banking & Financial Services
C&I Loan Growth Pops In Q2, But Tax Reform’s Role Remains Unclear

Jul. 31 2018 — Business loan growth popped in the second quarter, but bankers are hesitant to attribute the jump to tax reform or a broader turnaround in business spending.

The year-over-year increase in commercial-and-industrial loans increased to more than 5% for all banks in June, the highest figure in more than a year, according to Federal Reserve data. Smaller U.S. banks — defined by the Fed as those outside the 25 largest banks — posted double-digit growth for all three months of the second quarter.

Those numbers were artificially inflated by banks' acquisition of $24.9 billion of C&I loans from nonbanks. Accounting for those one-time acquisitions, organic C&I loan growth for smaller banks was still robust at 7% in June.

Ever since Republicans passed tax reform at the end of 2017, business optimism has been high and bankers have been hopeful the sentiment will trigger a rebound in business loan growth. C&I loan growth was less than 1% when tax reform passed.

Though C&I loan growth enjoyed a significant bounce in the second quarter, several bankers were not declaring victory. Numerous bank executives attributed the jump to an increase in merger-and-acquisition activity, not increased business spending.

M&T Bank Corp. said M&A activity was hurting its average loan growth, which declined by less than 1% on a quarter-over-quarter basis. The bank's CFO said businesses are selling significant assets and using the proceeds to pay down their loans.

One bank did say tax reform was boosting loan growth. SunTrust Banks Inc. reported an increase in the second quarter for its average performing loans figure, a turnaround from the first quarter when the figure declined on a linked-quarter basis.

"I think we are starting to see some of that [benefit from tax stimulus]," said Chairman and CEO William Rogers Jr. in the bank's earnings call.

But Rogers appeared to be in the minority. Several bankers said it was too early to tell whether tax reform was playing much of a role in the C&I loan growth. JPMorgan Chase & Co. reported a 3% quarter-over-quarter increase in its C&I loans in the second quarter and attributed the gain to M&A financing, not tax reform.

"We've yet to see the full effect of tax reform flow through into profitability and free cash flow," Lake said during the bank's earnings call.

Some bankers, including JPMorgan CEO Jamie Dimon, pointed to brewing trade wars as potential headwinds to loan growth.

Tariffs and trade-related issues are "probably the primary concern that we're hearing from customers right now," said Comerica Inc. President Curt Farmer.

Jeff Rulis, an analyst with D.A. Davidson, said he was not even sure the second-quarter C&I loan growth figures represented a notable change.

"I'm not convinced we're seeing a turnaround or significant pick-up. You have to take into account seasonal pick-up, and the first calendar quarter is generally slow," he said.

There is an argument that tax reform might actually be dampening loan growth. Rulis attributed high payoffs to the mixed results across the sector with some banks reporting robust loan growth by taking market share, contributing to others' more marginal results. Businesses are having an easier time making those payoffs thanks to tax reform, which freed up capital to pay down debt.

"One of the disadvantages of tax reform is you've both lowered the corporate tax rate and repatriated assets to the U.S. That's given more liquidity to the borrowers," said Peter Winter, an analyst with Wedbush Securities.

Year-over-year increases for total loans were up modestly, as weak commercial real estate loan growth moderated the gains from C&I. The 25 largest banks, in particular, reported soft commercial real estate loan growth with year-over-year declines in March, April and May — the first such drops since 2013. Several banks reported an intentional pullback from the sector due to credit quality concerns. Some pointed to nonbank competition as being particularly aggressive on both pricing and deal structure.

"I think banks, for the most part, are showing more credit discipline coming out of the financial crisis," Winter said. "Quite honestly, we're nine years into this recovery, so I think that's a prudent thing to do."

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Jul. 30 2018 — Joe Fenech, head of equity research at Hovde Group, discussed current bank stock valuations, the growing importance of deposits in valuing franchises and the market's increased skepticism toward M&A, including transactions that appear favorable for the buyer.

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Banking & Financial Services
Loans And Deposits Continue Uphill Climb At US Banks In June

Jul. 26 2018 — Average total loans and leases at U.S. commercial banks increased by $44.10 billion to $9.347 trillion in June, according to the Federal Reserve's July 13 H.8 report.

Loan growth was driven primarily by a $19.8 billion increase in commercial and industrial, a $9.4 billion jump in real estate and an $8.3 billion increase in commercial real estate.

Average loans and leases at large commercial banks increased $18.7 billion month over month, while average loans and leases at small commercial banks were up $21.7 billion. Loans and leases at foreign-related institutions increased by $3.4 billion.

Meanwhile, average total deposits at U.S. commercial banks increased by $56.4 billion in June, compared to a $35.4 billion increase in May. Total deposits were up $448.4 billion from June 2017.

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Credit Analysis
Peeking Into The Future Without Staring At A Crystal Ball: Brexit Scenarios And Their Impact On Energy Firms’ Credit Risk

Jul. 24 2018 — After so many years of living and working in London, two years ago I applied for, and was finally granted, British citizenship. Imagine my surprise when, a few weeks later, the UK European Union referendum took place and the majority of voters opted for Brexit!

As a dual national, both European and British, I feel twice the pain of an uncertain future and sometimes I wish I had a crystal ball.

While it is hard to predict how the whole separation process will pan out, S&P Global Market Intelligence offers a new statistical model that allows users to understand how firms’ credit risk on either side of the ocean may change under multiple exit scenarios. The Credit Analytics Macro-scenario model covers the United States, Canada and European Union countries plus the United Kingdom (EU27+1). In addition, the model can be run via the S&P Global Ratings’ Economists macro-economic multi-year forecasts, tailored for this specific model and updated on a quarterly basis.1

Figure 1 shows the Economists’ forecasts of the inputs used in our statistical model for EU27+1, for year-end 2018, 2019 and 2020.

Source: S&P Global Market Intelligence (as of June 2018). For illustrative purposes only. L/S ECB Interest rate spread is the spread between long-term and short-term ECB interest rates. Y-Axis is % of; GDP Growth, Stoxx50 Growth, Interest Rate Spread or FTSE100 Growth, depending on the correlating symbol as described in the key.

The expectation is for economic growth to slow-down in the EU27+1. This will be accompanied by progressive monetary policy tightening and a volatile performance of the stock market index growth. This view is aligned with the baseline scenario included in the European Banking Authority (EBA) and the Bank of England (BoE) 2018 stress testing exercise that “[…] reflects the average of a range of possible outcomes from the UK’s trading relationship with the EU”.2

Figure 2 shows the evolution of the median credit score of Energy sector (left panel) and Utility sector (right panel) large-revenue companies in EU27+1, obtained by running the economists’ forecasts via the Macro-scenario model.3 The median score for 2017 is generated via S&P Global Market Intelligence’s CreditModelTM 2.6 Corporates, a statistical model that uses company financials and is trained on credit ratings from S&P Global Ratings.4 The model offers an automated solution to assess the credit risk of numerous counterparties, globally. The scores are mapped to a numerical scale where, for instance, bb- (left panel, left scale) is mapped to 13.0; a deterioration by 1 notch corresponds to an increase of one integer on the numerical scales.

Figure 2: Evolution of the median credit score of Energy and Utility sector companies in EU27+1, based on S&P Global Economists’ macro-economic forecasts run via the Macro-scenario model.

Source: S&P Global Market Intelligence (as of June 2018). For illustrative purposes only.

Starting from 2017, we see a higher level of credit risk in the UK (red line) than in EU27 (blue line); in subsequent years, the median credit risk increases on both sides of the Channel but the “risk fork” between the UK and EU27 tends to widen up at the expenses of the UK, for both sectors.5

Despite the fact that the median credit score may not change sizably between 2017 and 2020, remaining below half a notch overall in all cases, it is worth keeping in mind that the probability of default (PD) associated with a credit score changes in line with the economic cycle, and thus increases (decreases) during periods of contraction (expansion).

In our model, we account for this effect by first mapping the credit score output to a long-run average PD; next we scale it via a “Credit Cycle Adjustment” (CCA) that looks at the ratio between the previous year and the long-run average default rate historically experienced in S&P Global Ratings’ rated universe.6 If we adjust the long-run average PD via the CCA, we can easily identify potential build-up of default risk pockets in different countries within the EU27+1 as time evolves, as shown in the animations within Figure 3. Green refers to a lower PD than 2017, orange refers to a higher PD than 2017, and red refers to a PD breaching a pre-defined threshold (4.5% for Energy Sector and 0.3% for Utilities sector).7

Figure 3: Potential pockets of default risk in Energy and Utility sector companies in EU27+1, based on S&P Global Economists’ forecast.

Energy Sector Utility Sector
Default Risk in Energy map Default Risk in Utilities map

Source: S&P Global Market Intelligence (as of June 2018). For illustrative purposes only. Green refers to a lower PD than 2017, orange refers to a higher PD than 2017, and red refers to a PD breaching a pre-defined threshold (4.5% for Energy Sector and 0.3% for Utilities sector).

With the Macro-scenario model, we aimed for a user friendly model, and took into account the strong economic ties within EU27+1, the existence of a common market and the circulation of a shared currency in the majority of the EU countries, in order to select a parsimonious yet statistically significant set of inputs (just imagine otherwise forecasting multiple macro-economic scenarios for 28 individual countries, over multiple years).8

Readers may wonder how the model differentiates the evolution of credit risk by country if it uses a limited set of aggregate macro-economic factors (e.g. EU28 GDP growth, etc.) across EU27+1. Nine separate sub-models were actually optimized, based on economic commonalities and historical evolution of the S&P Global Ratings transitions in those countries, to account for the existence of different EU “sub-regional” economies (for instance Nordic countries as opposed to Eastern European countries). For the UK, we went one step further, by explicitly including a market indicator, the FTSE100, as a precautionary measure given a potential “full decoupling” of EU27 and UK economies in the near future.

Well, so far so good, at least in the case of a “soft” Brexit! But what if we end up with a “hard” Brexit?

The EBA and the BoE 2018 stress testing exercise include a stressed scenario that “[…] encompasses a wide range of economic risks that could be associated with {hard} Brexit”.9 The scenario corresponds to a prolonged recessionary period, with negative GDP growth for several years and a generalized collapse of the stock markets, similar to what happened during the 2008 global recession. Unsurprisingly, the median credit score output by our macro-scenario model companies significantly deteriorates for both Energy and Utility sector. Figure 4 shows the build-up of potential default risk pockets and their evolution over time, under stressed economic conditions, depicting a bleak view over the length of time needed for a recovery of these sectors.10

Figure 4: Potential pockets of default risk in Energy and Utility sector companies in EU27+1, based on EBA’s and BoE’s 2018 stressed scenario.

Energy Sector Utility Sector
Default Risk in Energy map Default Risk in Utilities map

Source: S&P Global Market Intelligence (as of June 2018). For illustrative purposes only. Green refers to a lower PD than 2017, orange refers to a higher PD than 2017, and red refers to a PD breaching a pre-defined threshold (4.5% for Energy Sector and 0.3% for Utilities sector).

I do not have yet a crystal ball to predict the future, e.g. whether petrol will cost more or less, or whether I will be paying higher utility bills in the UK as opposed to (the rest of) the European Union, but S&P Global Market Intelligence’s Macro-Scenario allows gauging potential credit risk changes in individual countries, under a soft or a hard Brexit scenario. More in general, the Macro-Scenario model offers a quick, scalable and automated way to assess credit risk transitions under multiple scenarios, thus equipping risk managers at financial and non-financial corporations with a tool that enables them to make decisions with conviction.

Notes

1 The macro-economic forecasts will become available on the S&P Capital IQ platform from 2018Q4. S&P Global Ratings does not contribute to or participate in the creation of credit scores generated by S&P Global Market Intelligence.

2 Source: “Stress Testing Exercise 2018” available at http://www.eba.europa.eu/risk-analysis-and-data/eu-wide-stress-testing/2018. The baseline scenario is the consensus estimate among EU27+1 Central Banks.

3 The results of this analysis depend on the portfolio composition. In addition, other industry sectors may react differently from the Energy and Utility sectors.

4 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 scores from the credit ratings issued by S&P Global Ratings.

5 The 2017 median score for the Utility sector is better than the score for the Energy sector, due to the inherently higher risk of companies in the latter.

6 An optional market-view adjustment is available within the macro-scenario model. In our analysis, we did not include this adjustment, for the sake of simplicity.

7 4.5% (0.3%) is close to the historical long-run average default rate of companies rated B- (BBB-) by S&P Global Ratings.

8 This is also one of the reasons we found it unnecessary to include oil price for the modelling of credit risk of the energy sector in EU27+1, as we found the stock market growth was sufficient.

9 Source: “Stress Testing Exercise 2018” available at http://www.eba.europa.eu/risk-analysis-and-data/eu-wide-stress-testing/2018. The baseline scenario is the consensus estimate among EU27+1 Central Banks. Curly brackets refer to the author’s addition.

10 We adopt the same colour conventions as in Figure 3.

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