###### Assumptions Relating to the Evolution ...

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From the previous learning outcomes, we learned about the relative global systemic risks across industries, the CAMELS approach for analysis of banks, and other factors not addressed under the CAMELS approach. In this section, we delve into combining the various components of the CAMELS approach and other factors relevant to the analysis of a bank. We examine this by looking at the examples that follow:

Victoria Ivana is a financial sector analyst writing an industry report. Ivana discusses the relative systemic risk across industries. She refers to Industry A (international property and casualty insurance), Industry B (credit unions), and Industry C (global commercial banks).

According to Ivana’s report, Industry C, which represents global commercial banks, is *most likely* to have the highest level of global systemic risk. Global commercial banks have the highest proportion of cross-border business. Unlike banks, the overall insurance market has a smaller proportion of cross-border business. Also, insurance companies’ foreign branches are generally required to hold assets in a jurisdiction that are adequate to cover the related policy liabilities in that jurisdiction.

As an international property and casualty insurer, Industry A protects against adverse events related to autos, homes, or commercial activities; many of these events have local, rather than international, impact.

Industry B, credit unions, most likely has the lowest level of global systemic risk. Credit unions are depository institutions that function like banks and offer many of the same services. However, they are owned by their members rather than being publicly traded as many banks are.

Part of Ivana’s analysis focuses on Company ABC, which is a global commercial bank, its CAMELS rating, risk management practices, and performance. First, she examines the company’s capital adequacy based on the vital capital ratios in the following exhibit.

$$\small{\begin{array}{l|r|r|r} \textbf{At 31 December} & \bf{2019} & \bf{2018} & \bf{2017} \\ \hline\textbf{} & \text{\$m} & \text{\$m} & \text{\$m}\\ \hline\textbf{Regulatory capital} & \text{} & \text{} & \text{}\\ \hline\text{Common equity Tier 1 capital} & 125,588 & 121,531 & 116,264\\ \hline\text{Additional Tier 1 capital} & 1,803 & -393 & -3,236\\ \hline\text{Tier 2 capital} & 1,620 & 6,728 & 17,364\\ \hline\text{Total regulatory capital} & 129,011 & 127,866 & 130,392\\ \hline\textbf{Risk-weighted assets (RWAs) by risk type}&{}&{}&{}\\ \hline\text{Credit risk} & 939,927 & 968,803 & 947,764\\ \hline\text{Market risk} & 23,264 & 16,074 & 28,764\\ \hline\text{Operational risk} & 272,989 & 235,464 & 203,464\\ \hline\textbf{Total RWAs} & \textbf{1,236,180} & \textbf{1,220,341} & \textbf{1,179,992}\\ \end{array}}$$

Based on the exhibit, ABC’s capital adequacy over the three years, as measured by the three key capital ratios, signals mixed conditions. The ratios are calculated as follows:

$$\text{Common Tier 1 Capital Ratio}=\frac{\text{Total Common Equity Tier 1 Capital}}{\text{Total Risk Weighted Assets}}$$

$$\text{Common Equity Tier 1 Capital Ratio}_{2017} =\frac{116,264}{1,179,992} = 9.9\%$$

$$\text{Common Equity Tier 1 Capital Ratio}_{2018} = \frac{121,531}{1,220,341} = 10.0\%$$

$$\text{Common Equity Tier 1 Capital Ratio}_{2019} = \frac{125,588}{1,236,180} = 10.2\%$$

$$\text{Tier 1 Ratio}=\frac{\text{Common Equity Tier 1 Capital + Additional Tier 1 Capital}}{\text{Total Risk Weighted Assets}}$$

$$\text{Tier 1 Ratio}_{2017} =\frac{116,264+(-3,236)}{1,179,992} = 9.6\%$$

$$\text{Tier 1 Ratio}_{2018}= \frac{121,531+(-393)}{1,220,341}=9.9\%$$

$$\text{Tier 1 Ratio}_{2019} = \frac{125,588+1,803}{1,236,180}=10.3\%$$

$$\text{Total Capital Ratio} =\frac{\text{Total Capital}}{\text{Total Risk-Weighted Assets}}$$

$$\text{Total Capital Ratio}_{2017} = \frac{130,392}{1,179,992} =11.1\%$$

$$\text{Total Capital Ratio}_{2018} = \frac{127,886}{1,220,341}=10.5\%$$

$$\text{Total Capital Ratio}_{2019} = \frac{129,011}{1,236,180}=10.4\%$$

$$\begin{array}{l|r|r|r} {}& \textbf{2019} & \textbf{2018} & \textbf{2017}\\ \hline\text{Common equity Tier 1 capital ratio} & 10.2\% & 10.0\% & 9.9\%\\ \hline\text{Tier 1 capital ratio} & 10.3\% & 9.9\% & 9.6\%\\ \hline\text{Total capital ratio} & 10.4\% & 10.5\% & 11.1\%\\ \end{array}$$

Both the common equity Tier 1 capital ratio and the Tier 1 capital ratio strengthened from 2017 to 2019, but the total capital ratio weakened during that same period, signaling mixed conditions.

Ivana further investigates ABC’s asset quality using the information in the exhibit as follows:

$$ \textbf{Company ABC: Asset Composition} $$

$$\begin{array}{l|r|r|r} \textbf{At 31 December} & \textbf{2019} & \textbf{2018} & \textbf{2017}\\ \hline{}& \text{\$m} & \text{\$m} & \text{\$m}\\ \hline\text{Total liquid assets} & 240,378 & 233,270 & 235,469\\ \hline\text{Investments} & 313,470 & 246,372 & 211,675\\ \hline\text{Consumer loans} & 336,171 & 329,790 & 326,707\\ \hline\text{Commercial loans} & 378,861 & 332,197 & 282,272\\ \hline\text{Goodwill} & 6,148 & 5,984 & 5,160\\ \hline\text{Other assets} & 30,951 & 23,424 & 994\\ \hline\textbf{Total assets} & \bf{1,305,979} & \bf{1,171,037} & \bf{1,062,277}\\ \end{array}$$

From the exhibit, ABC’s liquid assets as a percentage of total assets declined each year since 2017, indicating declining liquidity. This is as demonstrated in the table that follows.

$$ \textbf{Percentage of Total Assets} $$

$$\begin{array}{l|r|r|r} \textbf{At 31 December} & \textbf{2019} & \textbf{2018} & \textbf{2017}\\ \hline\text{Total liquid assets} & 18.4\% & 19.9\% & 22.2\%\\ \hline\text{Investments} & 24.0\% & 21.0\% & 19.9\%\\ \hline\text{Consumer loans} & 25.7\% & 28.2\% & 30.8\%\\ \hline\text{Commercial loans} & 29.0\% & 28.4\% & 26.6\%\\ \hline\text{Goodwill} & 0.5\% & 0.5\% & 0.5\%\\ \hline\text{Other assets} & 2.4\% & 2.0\% & 0.1\%\\ \hline\textbf{Total assets} & \bf{100.0\%} & \bf{100.0\%} & \bf{100.0\%} \\ \end{array}$$

To assess ABC’s risk management practices, Ivana appraises the consumer loan credit quality profile in the following exhibit, which is as follows:

$$ \textbf{Company ABC: Consumer Loan Profile by Credit Quality} $$

$$\begin{array}{l|r|r|r} \textbf{At 31 December} & \textbf{2019} & \textbf{2018} & \textbf{2017}\\ \hline{}& \text{\$m} & \text{\$m} & \text{\$m}\\ \hline\text{Strong credit quality} & 317,251 & 305,648 & 298,643\\ \hline\text{Good credit quality} & 30,952 & 32,818 & 32,353\\ \hline\text{Satisfactory credit quality} & 29,427 & 33,614 & 34,712\\ \hline\text{Substandard credit quality} & 1,999 & 3,196 & 5,828\\ \hline\text{Past due but not impaired} & 1,302 & 793 & 537\\ \hline\text{Impaired} & 6,793 & 7,334 & 8,224\\ \hline\text{Total gross amount} & 387,724 & 383,403 & 380,297\\ \hline\text{Impairment allowances} & (3,100) & (2,100) & (1,600)\\ \hline\textbf{Total} & \bf{384,624} & \bf{381,303} & \bf{378,697}\\ \end{array}$$

Based on the above exhibit, the trend in impaired allowances is reflective of the changes in past due but not impaired assets. Impairment allowances have increased proportionately to the increases in the amount of past-due but not impaired assets, which may be in anticipation of these past due assets becoming impaired. However, impaired assets have decreased each year, while strong credit quality assets have increased each year. This suggests declining impairment allowances as a result of improving the credit quality of these financial instruments.

$$\begin{align*}\text{2017 to 2018 change in impaired assets} &=\frac{7,334}{8224}-1\\&=-10.8\%\end{align*}$$

$$\begin{align*}\text{2017 to 2018 change in strong credit quality assets} &=\frac{305,648}{298,683}-1\\&=2.3\%\end{align*}$$

$$\begin{align*}\text{2017 to 2018 change in past due but not impaired assets}&=\frac{793}{537}-1\\&=47.7\%\end{align*}$$

$$\begin{align*}\text{2017 to 2018 change in impairment allowances}&= \frac{(-2,100)}{(-1,600)}-1\\&=31.3\%\end{align*}$$

$$\begin{align*}\text{2018 to 2019 change in impaired assets}&= \frac{6,793}{7,334}-1\\&=-7.4\%\end{align*}$$

$$\begin{align*}\text{2018 to 2019 change in strong credit quality assets}&= \frac{317,251}{305648}-1\\&=3.8\%\end{align*}$$

$$\begin{align*}\text{2018 to 2019 change in past due but not impaired assets}&=\frac{1,302}{793} -1\\&=64.2\%\end{align*}$$

$$\begin{align*}\text{2018 to 2019 change in impairment allowances}&= \frac{(-3,100)}{(-2,100)} -1&\\&=47.6\%\end{align*}$$

$$\begin{array}{l|cc} \textbf{At 31 December} & \textbf{Percentage Change (Year-over-Year)}\\ \hline{}& \textbf{2019} & \textbf{2018}\\ \hline\text{Impaired assets} & -7.4\% & -10.8\%\\ \hline\text{Strong credit quality assets} & 3.8\% & 2.3\%\\ \hline\text{Past due but not impaired assets} & 64.2\% & 47.7\%\\ \hline\text{Impairment allowances} & 47.6\% & 31.3\%\\ \end{array}$$

Ivana also examines ABC’s loan loss profile to assess the bank’s risk management. The loan loss analysis data is as follows:

$$ \textbf{Company ABC: Loan Loss Analysis Data} $$

$$\begin{array}{l|r|r|r} \textbf{At 31 December} & \textbf{2019} & \textbf{2018} & \textbf{2017}\\ \hline\text{}& \text{\$m} & \text{\$m} & \text{\$m}\\ \hline\textbf{Consumer loans} &{} &{} &{}\\ \hline\text{Allowance for loan losses} & 10,670 & 11,170 & 12,670\\ \hline\text{Provision for loan losses} & 2,200 & 1,200 & 500\\ \hline\text{Charge-offs} & 3,122 & 3,006 & 3,370\\ \hline\text{Recoveries} & 610 & 449 & 417\\ \hline\text{Net charge-offs} & 2,512 & 2,557 & 2,953\\ \hline\textbf{Commercial loans} & {}& {}& {}\\ \hline\text{Allowance for loan losses} & 1,481 & 953 & 110\\ \hline\text{Provision for loan losses} & 1,041 & 383 & 36\\ \hline\text{Charge-offs} & 1,417 & 740 & 646\\ \hline\text{Recoveries} & 369 & 365 & 614\\ \hline\text{Net charge-offs} & 1048 & 375 & 32\\ \end{array}$$

Based on Exhibit 4, a loan loss analysis for the last three years indicates that the allowance for loan losses to net commercial loan charge-offs has been declining during the three years. This indicates that the cushion between the allowance and the net commercial loan charge-offs has deteriorated.

$$\begin{align*}\text{2017 Consumer}&=\frac{\text{Allowance for loan losses}}{ \text{Net loan charge-offs}}\\&=\frac{12,670}{2,953}\\&=4.29\end{align*}$$

$$\begin{align*}\text{2018 Concumer}&= \frac{\text{Allowance for loan losses}}{ \text{Net loan charge-offs}}\\&=\frac{11, 170}{2, 557}\\&=4.37\end{align*}$$

$$\begin{align*}\text{2019 Consumer}&=\frac{\text{Allowance for loan losses}}{ \text{Net loan charge-offs}}\\&=\frac{10, 670}{2, 512}\\&= 4.25\end{align*}$$

$$\begin{align*}\text{2017 Commercial}&=\frac{\text{Allowance for loan losses}}{ \text{Net loan charge-offs}}\\&=\frac{110}{32}\\&=3.44\end{align*}$$

$$\begin{align*}\text{2018 Commercial}&=\frac{\text{Allowance for loan losses}}{\text{Net loan charge-offs}}\\&=\frac{953}{375}\\&=2.54\end{align*}$$

$$\begin{align*}\text{2019 Commercial}&=\frac{\text{Allowance for loan losses}}{\text{Net loan charge-offs}}\\&=\frac{1,481}{1,048}\\&=1.41\end{align*}$$

$$\begin{align*}\text{2017 Consumer}&=\frac{\text{Provision for loan losses}}{\text{Net loan charge-offs}}\\&=\frac{500}{2,953}\\&=0.17\end{align*}$$

$$\begin{align*}\text{2018 Consumer}&=\frac{\text{Provision for loan losses}}{\text{Net loan charge-offs}}\\&=\frac{1,200}{2,557}\\&=0.47\end{align*}$$

$$\begin{align*}\text{2019 Consumer}&=\frac{\text{Provision for loan losses}}{\text{Net loan charge-offs}}\\&=\frac{2,200}{2,512}\\&=0.88\end{align*}$$

$$\begin{align*}\text{2017 Commercial}&=\frac{\text{Provision for loan losses}}{\text{Net loan charge-offs}}\\&=\frac{36}{32}\\&=1.13\end{align*}$$

$$\begin{align*}\text{2018 Consumer}&=\frac{\text{Provision for loan losses}}{\text{Net loan charge-offs}}\\&=\frac{383}{375}\\&=1.02\end{align*}$$

$$\begin{align*}\text{2019 Consumer}&=\frac{\text{Provision for loan losses}}{\text{Net loan charge-offs}}\\&=\frac{1,041}{1,048}\\&=0.99\end{align*}$$

To summarize this, look at the following table:

$$\small{\begin{array}{l|r|r|r} \textbf{At 31 December} & \textbf{2019} & \textbf{2018} & \textbf{2017}\\ \hline\text{Allowance for consumer loan losses to net loan charge-offs:} & 4.25 & 4.37 & 4.29\\ \hline\text{Allowance for commercial loan losses to net loan charge-offs} & 1.41 & 2.54 & 3.44\\ \hline\text{Provision for consumer loan losses to net loan charge-offs} & 0.88 & 0.47 & 0.17\\ \hline\text{Provision for commercial loan losses to net loan charge-offs} & 0.99 & 1.02 & 1.13\\ \end{array}}$$

Market risk often impacts the earnings performance and liquidity of a bank’s assets and liabilities. Ivana has been provided with the value at risk disclosure for ABC to assess the bank’s exposure to market factors. ABC has used a 99% confidence level to estimate the value at risk of a potential decline in the value of a position normal market conditions for an assumed single-day holding period. ABC uses a Monte Carlo simulation VaR model to capture material risk sensitivities of various asset classes/risk types. Its VaR includes positions that are measured at fair value but exclude investment securities classified as available-for-sale or held-to-maturity. The following exhibit is an excerpt from ABC’s 2019 VaR disclosure.

$$ \textbf{ABC Year-End and Average Trading VaR and Trading and Credit Portfolio VaR} $$

$$\small{\begin{array}{l|r|r|r|r} {}& \textbf{12/31/2019} & \textbf{Average 2019} & \textbf{12/31/2018} & \textbf{Average 2018}\\ \hline{}& \text{\$m} & \text{\$m} & \text{\$m} & \text{\$m}\\ \hline\text{Interest rate} & \$25 & \$23 & \$25 & \$32\\ \hline\text{Credit spread} & \$52 & \$51 & \$45 & \$58\\ \hline\text{Covariance adjustment} & (\$23) & (\$34) & (\$31) & (\$32)\\ \hline\text{Fully diversified interest rate and credit spread} & \$54 & \$40 & \$39 & \$58\\ \hline\text{Foreign exchange} & \$20 & \$12 & \$15 & \$22\\ \hline\text{Equity} & \$8 & \$9 & \$12 & \$12\\ \hline\text{Commodity} & \$22 & \$16 & \$12 & \$14\\ \hline \text{Covariance adjustment} & (\$82) & (\$70) & (\$65) & (\$77)\\ \hline\text{Total trading VaR—all market risk}\\ \text{factors, including general and specific}\\ \text{risk (excluding credit portfolios)} & \$22 & \$7 & \$13 & \$29\\ \hline\text{Specific risk-only component} & \$2 & \$6 & \$10 & \$5\\ \hline\text{Total trading VaR—general market risk}\\ \text{factors only (excluding credit portfolios)} & \$20 & \$1 & \$3 & \$24\\ \hline\text{Incremental impact of the credit portfolio} & \$14 & \$16 & \$16 & \$19\\ \hline\text{Total trading and credit portfolio VaR} & \$36 & \$23 & \$29 & \$48\\ \hline\textbf{VaR Effects on Earnings & Capital:} & {}&{} &{} & {}\\ \hline\text{Total trading and credit portfolio VAR} & 26 & 13 & 19 & 38\\ \hline\text{Net income from continuing operations} & \$10,002 &{} & \$12,355 &{}\\ \hline\text{Common equity} & \$184,280 &{} & \$183,552 &{}\\ \hline\textbf{Total VaR as % of:} & & & &\\ \hline\text{Net income from continuing operations} & 0.26\% & 0.13\% & 0.15\% & 0.31\%\\ \hline\text{Common equity} & 0.01\% & 0.01\% & 0.01\% & 0.02\%\\ \end{array}}$$

From the table:

ABC’s average trading VaR decreased from $29 million in 2018 to $7 million in 2019. The decline can mainly be due to changes in interest rate exposures from mark-to-market hedging activity. The average trading and credit portfolio VaR also dipped in 2019 to $23 million from $48 million in 2018. Although total trading and credit portfolio VaR increased from $19 million at the 2018 year-end to $26 million at the 2019 year-end, the magnitude of this worst-case single-day VaR is less than 1% of net income from continuing operations in both years, on either an end-of-period basis (0.26%) or an average basis (0.13%). The magnitude is even more minor when compared to equity, representing 0.01% on the end-of-period basis and less than 0.01% on an average basis.

Fundamentally, ABC’s VaR is a single-day measure of market shocks that can affect a company. Market dislocations can linger for days, weeks, and even longer. VaR is useful for measuring the effects of very short-term shocks; however, it does not solve the effects of longer-term market impacts.

Additionally, Ivana notes the following supplementary information from ABC’s annual report:

- Competition in the commercial loan space has become more aggressive, forcing ABC managers to pursue higher-risk strategies to increase market share.
- The net benefit plan obligation has steadily decreased over the three years.
- ABC awards above-average equity-based compensation to its top managers.

Based on the supplementary information, the net benefit plan obligation is consistent with a favorable assessment of ABC’s financial outlook. This is because the net benefit plan obligation has steadily decreased over the three years, indicating a lower degree of risk posed by the benefit plan. As previously highlighted, a bank that awards above-average equity-based compensation to its top managers possibly incentivizes risk-taking behavior and short-termism.

## Question

Johnson Smith, a finance analyst at a large wealth management firm, is analyzing three national banks based on their earnings quality. Smith collects data on these banks as follows:

$$ \textbf{Select Balance Sheet Data for National Banks—Trading: Contribution to Total Revenues} $$

$$\begin{array}{l|r|r|r|r} \textbf{Bank} & \textbf{2019} & \textbf{2015} & \textbf{2011} & \textbf{2007}\\ \hline\text{A Bank} & 2.97\% & 5.77\% & 8.87\% & 8.90\%\\ \hline\text{B Bank} & 7.07\% & 7.87\% & 15.77\% & 7.90\%\\ \hline\text{C Bank} & 3.77\% & 3.77\% & 10.67\% & 6.80\%\\ \end{array}$$

Based solely on the above data, which of the following statements is

most accurate?A. Trading represented a sustainable revenue source for C Bank between 2007 and 2015.

B. Relative to the other banks, A Bank has the highest quality of earnings in 2019

C. The quality of earnings for B Bank was the highest in 2011

## Solution

The correct answer is B.The quality of earnings is directly related to the level of sustainable sources of income. Trading income tends to be volatile and not necessarily sustainable. Higher-quality income would be net interest income and fee-based service income. Because A bank’s 2019 trading revenue contribution is the lowest relative to other banks, its quality of earnings would be considered the best of the three banks.

A is incorrect.As mentioned above, the quality of earnings is directly related to the level of sustainable sources of income. Trading income tends to be volatile and not necessarily sustainable.

C is incorrect.B Bank’s 2011 trading revenue contribution is the highest relative to other banks. However, its quality of earnings would be considered the worst of the three banks.

Reading 14: Analysis of Financial Institutions

*LOS 14 (e) Analyze a bank based on financial statements and other factors.*