The new CECL and IFRS 9 accounting standards will require financial institutions to adjust loss allowances based on forward-looking expectations and calculate lifetime losses. In this article, we demonstrate the effect of the new allowance framework by quantifying allowances and credit earnings volatility for a sample portfolio. Our case study finds that along with a shift in the level of allowance, portfolio dynamics and concentrations play an increasingly important role in understanding and communicating expected performance and earnings.
A financial institution’s allowance for loan and lease losses (ALLL) is an important estimate with significant impacts on an organization’s overall earnings and capital. While this reserve calculation has always had the potential to be quite complex, the new accounting procedures brought by the current expected credit loss model (CECL) and International Financial Reporting Standard 9 (IFRS 9) change the important elements of the process. With these new regimes, allowances must be updated on every reporting date to reflect more than current credit conditions; credit quality will need to be measured from a forward looking perspective which, by definition, will vary through time. The resulting overall portfolio loss allowance, and thus earnings, can exhibit substantial volatility.
The industry has already had a taste of the potential impacts of using expected cash flows for allowances with acquisitions of distressed loans and purchase loan accounting. This fair value accounting on acquired loans exhibited incredible volatility when compared to other assets. In CECL and IFRS 9, this forward-looking approach applies to the entire institution, and the expected patterns will be much more sensitive to the economic cycles, portfolio composition, and calculation assumptions.
This shift in predictability of losses and earnings will demand significant time from senior management not only to explain differences period over period, but also to accurately and confidently communicate expected patterns given anticipated strategy choices and market conditions.
Determining Credit Earnings
There are two main decision types which drive the ability to accurately forecast allowances and overall earnings:
1. Framework and methodology choices – data granularity, a reasonable and supportable look-back period, scenario narrative, and a wide array of smaller elements
2. Business and strategy choices – loan structure, type, industry, and geographic distribution, as well as potential for clustered defaults and downgrades (concentration)
Clearly, there are methodology choices that impact overall results; however, it is also clear that the economic dynamics of the portfolio and its composition have an important effect on outcomes.
The predictability of losses is mostly driven by the economic relationships in the portfolio, which are best described by concentration effects (e.g., name, sector, product, and geography). Some of the dynamics are quite intuitive; for example, an institution heavily invested in California real estate would have
losses closely related to statewide housing prices as well as important commercial sectors in California. However, more diversified institutions will find a systematic approach helpful in fully understanding, anticipating, and communicating outcomes over time.
We find that a systematic approach using a simulation to determine credit earnings volatility provides a useful measure to help senior management anticipate what parts of the portfolio, management actions, and scenarios most impact predictability. This measure encapsulates the credit risk in earnings for the entire institution, as well as the contribution by portfolio segment, sector geography, etc.
Armed with an understanding of the dynamics within the portfolio,management can take actions to reduce portfolio credit earnings volatility and better communicate the anticipated volatility, given a market outlook or set of strategic choices. Figure 1 provides the basic formula for calculating credit earnings.
Case Study: Lifetime Expected Credit Loss
In the following study, we isolate the impact of shifting from a simple one-year expected credit loss (ECL) to the lifetime ECL allowance framework required by CECL for a sample global corporate loan portfolio created by Moody’s Analytics. While the true economics and performance of the portfolio remain the same, the study isolates the impact on the attractiveness of particular portfolio segments given a shift in calculation horizon. Figure 2
shows the top countries and industries represented in the sample portfolio.
longer periods of time, which encourages institutions to consider the relative benefit of an instrument or segment and look more closely at overall portfolio composition.
The relative benefits of certain sectors clearly change based on the required allowance horizon. We see in this analysis that the top ranked exposures when using 12-month ECL for allowances are different than the top-ranked exposures when considering lifetime allowances. In Figure 4, we see the patterns within the portfolio. It is important to remember that the economics of the portfolio are the same in both runs, so our simulation correctly reflects that
many of the best performers under 12-month allowances are the same under lifetime allowances. At the same time, there are clear cases where sectors are ranked significantly differently.
In our study, it becomes clear that interactions of various segments within the overall portfolio can play an important role in outcomes. For example, we see that the Swiss machinery and equipment segment is very attractive when looking over a single-year period; however, when we consider the full life of the loan, that segment becomes significantly less attractive due to the expected volatility of allowances in this category. Conversely, all of the real estate categories broadly increase in relative attractiveness when we evaluate our portfolio with a lifetime perspective.
We find that there is value in quantifying the risk and profitability of not only the portfolio as a whole, but also the interaction of individual elements within. Segment-level insights provide a quantitative basis for understanding dynamics, as well as hard numbers for reference when communicating strategy, expectations, and policy shifts to internal and external stakeholders. In our example, the analysis indicates a clear justification for increased investments in real estate in a lifetime allowance environment and decreasing focus (or shorter durations) in some industrial categories.
It is also worth noting that the above analysis is based on a benign credit environment. The impact of using a forward-looking default probability will have a significant impact in the negative part of the credit cycle. There will be even greater costs and uncertainty for organizations holding risky instruments, as a simple change in default probabilities alone will cause significant volatility in earnings.
As CECL rolls out across financial institutions in the US, and IFRS 9 takes effect for much of the world, managers must adopt new