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50 Years Of Altman Z-score, And PD Model Fundamentals – Case Study General Motors

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

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Credit Analysis
50 Years Of Altman Z-score, And PD Model Fundamentals – Case Study General Motors

Jun. 11 2018 — The year 2018 marks the 50th anniversary of the Altman Z-score, which was designed to gauge credit strength of publicly traded manufacturing corporates. Until this day, the model has been used by financial practitioners to obtain a condensed picture of the financial strength of a company, and serves as a benchmark for credit risk assessment models.

As a part of providing data and tools for a comprehensive analysis of credit risk, S&P Global Market Intelligence has developed a family of PD Model Fundamentals (PDFN). The PDFN is a statistical model that produces probability of default (PD) values over a one- to more than thirty-year horizon for public and private banks and corporations of any size. The model maps the PD values to credit scores1 (i.e. ‘bbb’), based on historical observed default rates (ODRs) extracted from S&P Global Ratings’ database (available on CreditPro® ) PDFN also offers a global coverage of over 250 countries and more than 20 segments, regions, and industries.

PDFN incorporates both financial risk and business risk to generate the overall PD value. This innovative approach captures, in a statistical PD model, important credit risk drivers as identified by S&P Global Ratings’ extensive experience in corporate credit assessments, and provides users with a well-rounded measure of credit risk, where different sources can be easily identified.

We apply the credit assessment metrics to analyze one of the most publicized bankruptcy events in the last decade, the case of General Motors (General Motors Company, formerly General Motors Corporation). In Figure 1 we present the historical evolution of credit risk for General Motors (GM) from January 2005 to May 2018, accompanied by bankruptcy related Key Developments. We compare assessed credit score by PDFN, Altman Z-score, and corresponding S&P Global Ratings Issuer Credit Rating.

At the beginning of 2005, PDFN indicates a credit risk score of ‘bbb-‘, while the S&P Global Ratings Issuer Credit Rating is ‘BBB-‘. The credit risk score indicates that General Motors had adequate capacity to meet its financial commitments. However, adverse economic conditions or changing circumstances are more likely to lead to a weakened capacity of the obligor to meet its financial commitments. Likewise, the Z-score indicates a rather problematic financial situation, placing General Motors in distressed zone category.

In the following months, the credit quality of General Motors rapidly deteriorated. PDFN signals highly increased probability of financial distress already at the beginning of 2007, more than two years in advance. The implied ‘ccc’ credit score suggests high vulnerability to adverse business, financial, or economic conditions with at least a one-in-two likelihood of default. A few months before default, PDFN indicates a credit score of ‘cc’, thus expecting default to be highly likely. Similarly, the S&P Global Ratings Issuer Credit Ratings shows decaying credit quality, albeit the credit rating changes are more sporadic and have larger increments. The Z-score starts to show a significant deterioration of credit quality one year prior to default, but with a notable lag in comparison with PDFN.

After completion of the post-bankruptcy reorganization, creditworthiness of General Motors improved, and PDFN indicates a fairly stable credit risk profile with an implied score of ‘bbb’. In comparison, S&P Global Ratings Issuer Credit Rating initially shows a greater conservatism in light of the reorganization processes. Since then, the credit rating has improved steadily, converging with PDFN estimate. Z-score shows a somewhat steady estimate of credit risk, with a slight deterioration in the recent years.

Figure 1: Historical evolution of credit risk for General Motors (GM)

The shaded area denotes the period of reorganization between the bankruptcy announcement and reemergence of General Motors (GM) as a public company on the New York Stock Exchange (NYSE). Dashed vertical lines denote bankruptcy related Key Development (see corresponding numbers for details). The Z-score scale has been selected to match the credit score level at the beginning of the period.

Source: S&P Global Market Intelligence (as of May 30th, 2018). For illustrative purposes only.

General Motors (GM) – Key Developments:
(1) Nov 8, 2008: GM heads towards bankruptcy
(2) Dec 31, 2008: GM expects to receive $13.40 billion in funding from U.S. Department of The Treasury.
(3) Feb 14, 2009: GM contemplates bankruptcy
(4) Jun 1, 2009: GM filed for bankruptcy
(5) Nov 17, 2010: GM has completed an IPO and starts trading on NYSE

PDFN incorporates both financial and business risk dimensions to generate an overall PD value as well as an assessment of each individual dimension (financial and business risk). It also comes equipped with a useful analytic tool, the contribution analysis, which allows users to identify drivers of risk, in absolute or relative terms, to define potential paths to creditworthiness improvement or deterioration.

Figure 2 presents the current credit risk profile of General Motors as provided by the PDFN based on last twelve months of data. The contribution analysis indicates that overall business risk is strong, but the company’s financial position is aggressive and is currently the main driver of overall PD estimate. A deep dive analysis shows a weak total equity position which in addition to profitability (EBIT/Total Assets) and efficiency (EBIT/Revenues), resulting in limited financial flexibility (Retained Earnings/Total Assets), represent the risk factors with the largest driver for the assigned credit risk score for General Motors.

Figure 2: Credit risk profile of General Motors (GM)

Source: S&P Global Market Intelligence (as of May 30th, 2018). For illustrative purposes only.

This case study exemplifies the value of PD Model Fundamentals, in providing predictive insights into companies’ creditworthiness and dynamic estimates of PD value and mapped credit score. Our model was trained and calibrated on default flags and is able to signal deterioration of credit quality well in advance of the actual bankruptcy event. The combination of both financial risk and business risk enables a comprehensive overview of a company's creditworthiness, while also providing an in-depth review of a company's credit risk profile to identify and distinguish the main sources of risk. S&P Global Market Intelligence leverages leading experience in developing PD models to achieve a high level of accuracy and a robust out-of-sample model performance. The integration of PDFN into the S&P Capital IQ platform allows users to access a global pre-scored database with more than 45,000 public companies and almost 700,000 private companies, obtain PD values for single or multiple companies, and perform a scenario analysis.

1 S&P Global Ratings does not 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 uppercase credit ratings issued by S&P Global Ratings.

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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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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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