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Articles Tagged with: CECL

Commercial Bank: CECL Model Validation

A commercial bank required an independent validation of its CECL models. The models are embedded into three platforms (Trepp, Impairment Studio and Evolv) and included the following:

  • Trepp Default Model (Trepp DM) is used by the Bank to estimate the PD, LGD and EL of the CRE portfolio
  • Moody’s ImpairmentStudio – Lifetime Loss Rate (LLR) Model is used to calculate the Lifetime Loss Rate for the C&I portfolio
  • EVOLV – Lifetime Loss Rate (LLR) model is used to calculate the Lifetime Loss Rate for Capital Call and Venture Capital loans within the Commercial and Industrial (C&I) segment, Non-rated Commercial loans, Consumer as well as Municipal loans
  • EVOLV – Base Loss Rate (BLR) model is used to calculate quantitative allowance for 1-4 Family commercial loans and Personal loans for commercial use within the C&I segment Residential loans, HELOC and Indirect vehicle.

The Solution

Because the CECL models are embedded into three platforms, RiskSpan conducted an independent, comprehensive validation of all three platforms.

Our validation included components typical of a full-scope model validation, focusing on a conceptual soundness review, process verification and outcomes analysis.

Deliverables 

RiskSpan was given access to the models’ platforms, and workpapers, along with the models’ development documentation, and weekly Q&A sessions with the model owners.

Our review evaluated:

i. the business requirements and purpose of the model, and the metrics that used by the developer to select the best model and evaluate its success in meeting these requirements will be judged.

ii. the identification and justification for

  (a) any theoretical basis for the model structure;

  (b) the use of specific developmental data;

  (c) the use of any statistical or econometric technique to estimate the model; and

  (d) the criteria used to identify and select the best model among alternatives.

iii. the reasonableness of model-development decisions, documented assumptions, data adjustments, and model-performance criteria as measured at the time of development.

iv. Process verification to determine the accuracy of data transcription, adjustment, transformation and model code.

RiskSpan produced a written validation report detailing its validation assessments, tests, and findings, and providing a summary assessment of the suitability of the models for their intended uses as an input to the bank’s CECL process, based upon the Conceptual Soundness Review and Process Verification.


Sample Size Requirements for CECL Modeling

Part One of a Two-Part Series on CECL Data Requirements

With CECL implementation looming, many bankers are questioning whether they have enough internal loan data for CECL modeling. Ensuring your data is sufficient is a critical first step in meeting the CECL requirements, as you will need to find and obtain relevant third-party data if it isn’t. This article explains in plain English how to calculate statistically sufficient sample sizes to determine whether third-party data is required. More importantly, it shows modeling techniques that reduce the required sample size. Investing in the right modeling approach could ultimately save you the time and expense of obtaining third-party data.

CECL Data Requirements: Sample Size for a Single Homogenous Pool

Exhibit 1: Required Sample Size

Let’s first consider the sample required for a single pool of nearly identical loans. In the case of a uniform pool of loans — with the same FICO, loan-to-value (LTV) ratio, loan age, etc. — there is a straightforward formula to calculate the sample size we need to estimate the pool’s default rate, shown in Exhibit 1.1 As the formula shows, the sample size depends on several variables, some of which must be estimated:

  • Materiality Threshold and Confidence Level: Suppose you have a $1 billion loan portfolio and you determine that, from a financial statement materiality standpoint, your ALLL estimate needs to be reliable to within +/- $2.5 million. Statistically, we would say that we need to be 95% confident that our loss reserve estimate is within an error margin of +/- $2.5 million of the true figure. The wider our materiality thresholds and lower our required confidence levels, the smaller the sample size we need.
  • Loss Severity: As your average loss severity increases, you need a greater sample size to achieve the same error margin and confidence level. For example, if your average loss severity is 0%, you will estimate zero losses regardless of your default rates. Theoretically, you don’t even need to perform the exercise of estimating default rates, and your required sample size is zero. On the opposite end, if your average loss severity is 100%, every dollar of defaulted balance translates into a dollar of loss, so you can least afford to misestimate default rates. Your required sample size will therefore be great.
  • Default Rates: Your preliminary estimate of default rate, based on your available sample, also affects the sample size you will require. (Of course, if you lack any internal sample, you already know you need to obtain third-party data for CECL modeling.) Holding dollar error margin constant, you need fewer loans for low default-rate populations.

Example: Suppose we have originated a pool of low-risk commercial real estate loans. We have historical observations for 500 such loans, of which 495 paid off and five defaulted, so our preliminary default rate estimate is 1%. Of the five defaults, loss severity averaged 25% of original principal balance. We deem ALLL estimate errors within 0.25% of the relevant principal balance to be immaterial. Is our internal sample of 500 loans enough for CECL modeling purposes, or do we need to obtain proxy data? Simply apply the formula from Exhibit 1: In this case, our internal sample of 500 loans is more than enough to give us a statistical confidence interval that is narrower than our materiality thresholds. We do not need proxy data to inform our CECL model in this case.

CECL Data Requirements: Sample Size Across an Asset Class

If we have an asset class with loans of varying credit risk characteristics, one way to determine the needed sample is just to carve up the portfolio into many buckets of loans with like-risk characteristics, determine the number of loans needed for each bucket on a standalone basis per the formula above, and then sum these amounts. The problem with this approach – assuming our concern is to avoid material ALLL errors at the asset class level – is that it will dramatically overstate the aggregate number of loans required. A better approach, which still involves segregating the portfolio into risk buckets, is to assign varying margins of error across the buckets in a way that minimizes the aggregate sample required while maintaining a proportional portfolio mix and keeping the aggregate margin of error within the aggregate materiality threshold. A tool like Solver within Microsoft Excel can perform this optimization task with precision. The resulting error margins (as a percentage of each bucket’s default rate estimates) are much wider than they would be on a standalone basis for buckets with low frequencies and slightly narrower for buckets with high default frequencies. Even at its most optimized, though, the total number of loans needed to estimate the default rates of multiple like-risk buckets will skyrocket as the number of key credit risk variables increases. A superior approach to bucketing is loan-level modeling, which treats the entire asset class as one sample but estimates loan-specific default rates according to the individual risk characteristics of each loan.

Loan-Level Modeling

 

Suppose within a particular asset class, FICO is the only factor that affects default rates, and we segregate loans into four FICO buckets based on credit performance. (Assume for simplicity that each bucket holds an equal number of loans.) The buckets’ default rates range from 1% to 7%. As before, average loss severity is 25% and our materiality threshold is 0.25% of principal balance. Whether with a bucketing approach or loan-level modeling, either way we need a sample of about 5,000 loans total across the asset class. (We calculate the sample required for bucketing with Solver as described above and calculate the sample required for loan-level modeling with an iterative approach described below.) Now suppose we discover that loan age is another key performance driver. We want to incorporate this into our model because an accurate ALLL minimizes earnings volatility and thereby minimizes excessive capital buffers. We create four loan age buckets, leaving us now with 4 × 4 = 16 buckets (again, assume the buckets hold equal loan count). With four categories each of two variables, we would need around 9,000 loans for loan-level modeling but 20,000 loans for a bucketing approach, with around 1,300 in each bucket. (These are ballpark estimates that assume that your loan-level model has been properly constructed and fit the data reasonably well. Your estimates will vary somewhat with the default rates and loss severities of your available sample. Also, while this article deals with loan count sufficiency, we have noted previously that the same dataset must also cover a sufficient timespan, whether you are using loan-level modeling or bucketing.) Finally, suppose we include a third variable, perhaps stage in the economic cycle, LTV, Debt Service Coverage Ratio, or something else.

Exhibit 2: Loan-Level Modeling Yields Greater Insight from Smaller Samples

Again assume we segregate loans into four categories based on this third variable. Now we have 4^3= 64 equal-sized buckets. With loan-level modeling we need around 12,000 loans. With bucketing we need around 100,000 loans, an average of around 1,600 per bucket. As the graph shows in Exhibit 2, a bucketing approach forces us to choose between less insight and an astronomical sample size requirement. As we increase the number of variables used to forecast credit losses, the sample needed for loan-level modeling increases slightly, but the sample needed for bucketing explodes. This points to loan-level modeling as the best solution because well-performing CECL models incorporate many variables. (Another benefit of loan-level credit models, one that is of particular interest to investors, is that the granular intelligence they provide can facilitate better loan screening and pricing decisions.)

CECL Data Requirements: Sample Size for Loan-Level Modeling

Determining the sample size needed for loan-level modeling is an iterative process based on the standard errors reported in the model output of a statistical software package. After estimating and running a model on your existing sample, convert the error margin of each default rate (1.96 × the standard error of the default rate estimate to generate a 95% confidence interval) into an error margin of dollars lost by multiplying the default rate error margin by loss severity and the relevant principal balance. Next, sum each dollar error margin to determine whether the aggregate dollar error margin is within the materiality threshold, and adjust the sample size up or down as necessary. The second part in our series on CECL data requirements will lay out the data fields that should be collected and preserved to support CECL modeling.


[1] https://onlinecourses.science.psu.edu/stat506/node/11


What CECL Means To Investors

Recent updates to U.S. GAAP will dramatically change the way financial institutions incorporate credit risk into their financial statements. The new method is called the Current Expected Credit Loss (CECL) model and will take effect over the next few years. For many institutions, CECL will mean a one-time reduction in book equity and lower stated earnings during periods of portfolio growth. These reductions occur because CECL implicitly double-counts credit risk from the time of loan origination, as we will meticulously demonstrate. But for investors, will the accounting change alter the value of your shares?

Three Distinct Measures of Value

To answer this question well, we need to parse three distinct measures of value:

1.      Book Value: This is total shareholders’ equity as reported in financial reports like 10-Ks and annual reports prepared in accordance with U.S. GAAP.

2.      Current Market Value (also known as Market Cap): Current share price multiplied by the number of outstanding shares. This is the market’s collective opinion of the value of your institution. It could be very similar to, or quite different from, book value, and may change from minute to minute.

3.      Intrinsic Value (also known as Fundamental Value or True Value): The price that a rational investor with perfect knowledge of an institution’s characteristics would be willing to pay for its shares. It is by comparing an estimate of intrinsic value versus current market value that we deem a stock over- or under-priced. Investors with a long-term interest in a company should be concerned with its intrinsic or true value.

How Does an Accounting Change Affect Each Measure of Value?

Accounting standards govern financial statements, which investors then interpret. An informed, rational investor will “look through” any accounting quirk that distorts the true economics of an enterprise. Book value, therefore, is the only measure of value that an accounting change directly affects.

An accounting change may indirectly affect the true value of a company if costly regulations kick in as a result of a lower book value or if the operational cost of complying with the new standard is cumbersome. These are some of the risks to fundamental value from CECL, which we discuss later, along with potential mitigants.

Key Feature of CECL: Double-Counting Credit Risk

The single-most important thing for investors to understand about CECL is that it double-counts the credit risk of loans in a way that artificially reduces stated earnings and the book values of assets and equity at the time a loan is originated. It is not the intent of CECL to double-count credit risk, but it has that effect, as noted by no less authorities than the two members of the Financial Accounting Standards Board (FASB) who dissented from the rule. (CECL was adopted by a 5-2 vote.)

Consider this simple example of CECL accounting: A bank makes a loan with an original principal balance of $100. CECL requires the bank to recognize an expense equal to the present value of expected credit losses[i] and to record a credit allowance that reduces net assets by this same amount. Suppose we immediately reserve our $100 loan down to a net book value of $99 and book a $1 expense. Why did we even make the loan? Why did we spend $100 on something our accountant says is worth $99? Is lending for suckers?

Intuitively, consider that to make a loan of $100 is to buy a financial asset for a price of $100. If other banks would have made the same loan at the same interest rate (which is to say, they would have paid the same price for the same asset), then our loan’s original principal balance was equal to its fair market value at the time of origination. It is critical to understand that an asset’s fair market value is the price which market participants would pay after considering all of the asset’s risks, including credit risk. Thus, any further allowance for credit risk below the original principal balance is a double-counting of credit risk.

Here’s the underlying math: Suppose the $100 loan is a one-year loan, with a single principal and interest payment due at maturity. If the note rate is 5%, the contractual cash flow is $105 next year. This $105 is the most we can receive; we receive it if no default occurs. What is the present value of the $105 we hope to receive? One way to determine it is to discount the full $105 amount by a discount rate that reflects the risk of nonpayment. We established that 5% is the rate of return that banks are requiring of borrowers presenting similar credit risk, so an easy present value calculation is to discount next year’s contractual $105 cash flow by the 5% contractual interest rate, i.e., $105 / (1 + 5%) = $100. Alternatively, we could reduce the contractual cash flow of $105 by some estimate of credit risk. Say we estimate that if we made many loans like this one, we would collect an average of $104 per loan. Our expected future cash flow, then, is $104. If we take the market value of $100 for this loan as an anchor point, then the market’s required rate of return for expected cash flows must be 4%. ($104 / (1 + 4%) = $100.) It is only sensible that the market requires a lower rate of return on cash flows with greater certainty of collection.

What the CECL standard does is require banks to discount the lower expected cash flows at the higher contractual rate (or to use non-discounting techniques that have the same effect). This would be like discounting $104 at 5% and calculating a fair market value for the asset of $104 / (1 + 5%) ≈ $99. This (CECL’s) method double-counts credit risk by $1. The graph below shows the proper relationship between cash flow forecasts and discount rates when performing present value calculations, and shows how CECL plots off the line.


Proper Valuation Combinations (—)


FASB Vice Chairman James Kroeker and Board member Lawrence Smith described the double-counting issue in their dissent to the standards update: “When performing a present value calculation of future cash flows, it is inappropriate to reflect credit risk in both the expected future cash flows and the discount rate because doing so effectively double counts the reflection of credit risk in that present value calculation. If estimates of future cash flows reflect the risk of nonpayment, then the discount rate should be closer to risk-free. If estimates of future cash flows are based on contractual amounts (and thus do not reflect a nonpayment risk), the discount rate should be higher to reflect assumptions about future defaults.” Ultimately, the revised standard “results in financial reporting that does not faithfully reflect the economics of lending activities.”[ii]

The Accounting Standards Update notes two tangential counterpoints to Kroeker and Smith’s dissent. The first point is that banks would find alternative methods challenging, which may be true but is irrelevant to the question of whether CECL faithfully reflects true economics. The second point is that the valuation principles Kroeker and Smith lay out are for fair value estimates, whereas the accounting standard is not intended to produce fair value estimates. This concedes the only point we are trying to make, which is that the accounting treatment deviates (downwardly, in this case) from the fundamental and market value that an investor should care about.

How CECL Affects Each Measure of Value

As noted previously, the direct consequences of CECL will hit book value. Rating agency Fitch estimates that the initial implementation of CECL would shave 25 to 50 bps off the aggregate tangible common equity ratio of US banks if applied in today’s economy. The ongoing impact of CECL will be less dramatic because the annual impact to stated earnings is just the year-over-year change in CECL. Still, a growing portfolio would likely add to its CECL reserve every year.[iii]

There are many indirect consequences of CECL that may affect market and true value:

1.      Leverage: The combination of lower book values from CECL with regulations that limit leverage on the basis of book value could force some banks to issue equity or retain earnings to de-leverage their balance sheet. Consider these points:

a.      There is a strong argument to be made to regulators that the capital requirements that pre-dated CECL, if not adjusted for the more conservative asset calculations of CECL, will have become more conservative de facto than they were meant to be. There is no indication that regulators are considering such an adjustment, however. A joint statement on CECL from the major regulators tells financial institutions to “[plan] for the potential impact of the new accounting standard on capital.”[iv]

b.      Withholding a dividend payment does not automatically reduce a firm’s true value. If the enterprise can put retained earnings to profitable use, the dollar that wasn’t paid out to investors this year can appreciate into a larger payment later.

c.       The deeper threat to value (across all three measures) comes if regulations force a permanent de-leveraging of the balance sheet. This action would shift the capital mix away from tax-advantaged debt and toward equity, increase the after-tax cost of capital and decrease earnings and cash flow per share, all else equal.

Because banks face the shift to CECL together, however, they may be able to pass greater capital costs on to their borrowers in the form of higher fees or higher interest rates.

d.      Banks can help themselves in a variety of ways. The more accurate a bank’s loss forecasts prove to be, the more stable its loss reserve will be, and the less likely regulators are to require additional capital buffers. Management can also disclose whether their existing capital buffers are sufficient to absorb the projected impact of CECL without altering capital plans. Conceivably, management could elect to account for its loans under the fair value option to avoid CECL’s double-counting bias, but this would introduce market volatility to stated earnings which could prompt its own capital buffers.

2.      Investor Perception of Credit Risk: Investors’ perception of the credit risk a bank faces affects its market value. If an increase in credit allowance due to CECL causes investors to worry that a bank faces greater credit risk than they previously understood, the bank’s market value will fall on this reassessment. On the other hand, if investors have independently assessed the credit risk borne by an institution, a mere change in accounting treatment will not affect their view. An institution’s true value comes from the cash flows that a perfectly informed investor would expect. Unless CECL changes the kinds of loans an institution makes or the securities it purchases, its true credit risk has not changed, and nothing the accounting statements say can change that.

3.      Actual Changes in Credit Risk: Some banks may react to CECL by shifting their portfolio mix toward shorter duration or less credit risky investments, in an effort to mitigate CECL’s impact on their book value. If underwriting unique and risky credits was a core competency of these banks, and they shift toward safer assets with which they have no special advantage, this change could hurt their market and fundamental value.

4.      Volatility: ABA argues that the inherent inaccuracies of forecasts over long time horizons will increase the volatility of the loss reserve under CECL.[vi] Keefe, Bruyette & Woods (KBW) goes the other way, writing that CECL should reduce the cyclicality of stated earnings.[vii] KBW’s point can loosely be understood by considering that long-term averages are more stable than short-term averages, and short-term averages drive existing loss reserves. Certainly, if up-front CECL estimates are accurate, even major swings in charge-offs can be absorbed without a change in the reserve as long as the pattern of charge-offs evolves as expected. While cash flow volatility would hurt fundamental value, the concern from volatility of stated earnings is that it could exacerbate capital buffers required by regulators.

5.      Transparency: All else equal, investors prefer a company whose risks are more fully and clearly disclosed. KBW reasons that the increased transparency required by CECL will have a favorable impact on financial stock prices.[viii]

6.      Comparability Hindered: CECL allows management to choose from a range of modeling techniques and even to choose the macroeconomic assumptions that influence its loss reserve, so long as the forecast is defensible and used firm-wide. Given this flexibility, two identical portfolios could show different loss reserves based on the conservatism or aggressiveness of management. This situation will make peer comparisons impossible unless disclosures are adequate and investors put in the work to interpret them. Management can help investors understand, for example, if its loss reserve is larger because its economic forecast is more conservative, as opposed to because its portfolio is riskier.

7.      Operational Costs: Complying with CECL requires data capacity and modeling resources that could increase operational costs. The American Bankers Association notes that such costs could be “huge.”[ix] Management can advise stakeholders whether it expects CECL to raise its operational costs materially. If compliance costs are material, they will affect all measures of value to the extent that they cannot be passed on to borrowers. As noted earlier, the fact that all US financial institutions face the shift to CECL together increases the likelihood of their being able to pass costs on to borrowers.

8.      Better Intelligence: Conceivably, the enhancements to data collection and credit modeling required by CECL could improve banks’ ability to price loans and screen credit risks. These effects would increase all three measures of value.

Conclusion

CECL is likely to reduce the book value of most financial institutions. If regulators limit leverage because of lower book equity or the operational costs of CECL are material, and these costs cannot be transferred on to borrowers, then market values and fundamental values will also sag. If banks react to the standard by pulling back from the kinds of loans that have been their core competency, this, too, will hurt fundamental value. On the positive side, the required investment in credit risk modeling offers the opportunity for banks to better screen and price their loans.

Bank management can provide disclosures to analysts and investors to help them understand any changes to the bank’s loan profile, fee and interest income, capital structure and operational costs. Additionally, by optimizing the accuracy of its loss forecasts, management can contain the volatility of its CECL estimate and minimize the likelihood of facing further limitations on leverage.


[i] The term “expected loss” can be confusing; it does not necessarily mean that default is likely. If you have a 1% chance of losing $100, then your “expected loss” is 1% × $100 = $1. As long as a loan is riskier than a Treasury, your expected loss is greater than zero.

[ii] FASB Accounting Standards Update 2016-13, p. 237 and p. 235 http://www.fasb.org/jsp/FASB/Document_C/DocumentPage?cid=1176168232528&acceptedDisclaimer=true

[iii] By the end of a loan’s life, all interest actually collected and credit losses realized have been reflected in book income, and associated loss reserves are released, so lifetime interest income and credit losses are the same under any standard.

[iv] Joint Statement on the New Accounting Standard on Financial Instruments – Credit Losses. https://www.federalreserve.gov/newsevents/press/bcreg/bcreg20160617b1.pdf

Modigliani, Franco and Miller, Merton H. (1963) Corporate Income Taxes and the Cost of Capital: A Correction. The American Economic Review, Vol. 53, No. 3, pp. 433-443. https://www.jstor.org/stable/1809167?seq=1#page_scan_tab_contents

[vi] Gullette, Mike. (2016) FASB’s Current Expected Credit Loss Model for Credit Loss Accounting (CECL). American Bankers Association.

[vii] Kleinhanzl, Brian, et al. FASB is About to Accelerate Loan Loss Recognition for the Financial Industry. Keefe, Bruyette & Woods.

[viii] Kleinhanzl, Brian, et al, p. 1.

[ix] Gullette, Mike. (2016), p. 4.


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