Summary

Private credit markets continue to be an important source of funding within the leveraged loan universe. This is especially the case for smaller to middle-sized companies, those with EBITDA of roughly $10mln-$250mln. Non-bank lenders have emerged as a growing source to funding to these borrowers through a variety of platforms.

Examples of these alternative investments include direct lenders, closed-end funds, to a lesser extent, collateralized loan obligations (CLOs), which generally fund larger and broadly syndicated loans (BSL), and last but not least, business development companies (BDCs). We have recently turned our analytical attention to  BDCs as they provide both institutional and retail investors an opportunity to participate in the small and middle market lending asset class and as many are publicly traded.

Although BDCs typically invest in senior secured loans to smaller and middle-sized U.S. borrowers, the portfolio compositions can vary in many dimensions across BDCs, and in some cases quite significantly. Our analysis has highlighted key elements within the portfolios that would indicate the degree of correlation and hence effect the variance in the performance of a BDC’s returns (or losses) in two dimensions – issuers and industry.

These differences are particularly important for BDC investors (or lenders) as performance metrics would be further magnified due to the leverage that BDCs employ to finance themselves, similarly to CLOs, many closed-end funds and other leveraged investment vehicles. In essence, the relative risk/return profiles for these market participants can be more exposed to “fatter-tails” in the distribution of returns of the underlying portfolio due to less diversification (i.e., potential for greater volatility) – or conversely smoother and more predictable risk/return profiles due to greater diversification within the BDC.

Key performance drivers are related to the overall portfolio diversification of the BDC based on its concentrated exposure of investments made to unique borrower/issuers and industries/sectors, which are the focus of this paper, amongst other factors. We demonstrate through observations, which are based on publicly available information, that clearly indicate significant differences of diversification across BDC portfolios. However, this variation of portfolio composition also provides investors (or lenders) opportunities to better manage across the risk/return and portfolio diversification spectrums.

BDCs – brief overview

The US Congress initially established BDCs in the 1980s to offer an alternative source of funding to developing smaller and middle-sized companies. BDCs are generally subject to the same rules and regulations that apply to US regulated mutual funds – the Investment Company Act of 1940 and the Internal Revenue Code of 1986 for tax purposes.

BDCs are typically leveraged, subject to a maximum amount, with financing typically based on a combination of credit facilities and term debt. BDCs are subject to certain investment criteria (e.g., minimum percentage of investments consisting of smaller U.S. companies) and are required to pass through at least 90% of earned income.

The underlying portfolios typically comprise of first-lien senior secured loans to private corporate borrowers – other possible investment strategies may be around subordinated loans, mezzanine debt, special situations, and distressed debt, amongst others.

The investments are managed by asset managers with extensive expertise and operations capacity in this asset class where, in many cases, the manager may already participate outside of the BDC as direct lenders, private equity sponsors and/or manage other types of vehicles (e.g., CLOs, private accounts, closed-end funds).

In a previous analysis, we highlighted some key differences between private credit and BSL as well as factors for investors to consider. BDCs are generally substantially weighted towards middle market and smaller company lending although there are some BDCs with notable CLO/BSL exposure.

Those BDCs that are publicly traded not only offer liquidity to investors but also a great amount of transparency. For example, like other public companies, they are required to provide quarterly and annual reports. These reports make available to the public numerous detailed information related to the underlying portfolio and is the primary source of information our observations are based on.

It should be acknowledged that these are point-in-time snapshots of the portfolio and could change over time as BDCs are going concerns and actively managed. However, they do provide valuable insight into the manager’s lending/investment philosophy, and we believe our conclusions and findings are generally valid across time. Market participants should view this as an example of portfolio variation across the public BDC spectrum at a given point-in-time.

Comparing diversification across BDCs – key observations

We selected a sample of 32 BDCs to compare amongst the 40+ that are publicly traded (see Appendix). These examples were chosen to clearly demonstrate how the composition of the portfolios can vary significantly across BDCs. In this case, the focus is with respect to investment diversification as reflected by the distribution of borrower/issuer and industry/sector concentration levels. Other diversification benchmarks, which is outside the scope of this paper, could include investment strategy, credit risk, geography, currency, seniority of debt, asset class, level of affiliation, etc.

See Exhibits 1-4 below illustrating various concentration statistics followed by noteworthy observations. Note that the percentages and amounts of the respective portfolios are based on fair market value in USD as reported, in most cases as of the most recent calendar year-end. In addition, we excluded any investments that were in cash (or its equivalents) or had a fair market value of zero.

Borrower/issuer concentration

Exhibit 1: Top Seven Borrower/Issuer Concentrations vs. Average Concentration

The distribution of credit and investment exposures to unique borrowers/issuers is perhaps the best indicator to begin an assessment of the level of portfolio diversification. A BDC manager may allocate several investments to a single borrower/issuer, for example, based on a combination of loans/debt and/or equity. As such, we combined all investments to a unique borrower/issuer for purposes of determining concentration. We noted that on average there were around 1.5 investments per unique issuer with ranges of close to 1 and a third of our sample in excess of 2.0.

In many instances, portfolios tend to be fairly concentrated within the largest few exposures, but relatively granular with the remainder. See Exhibit 1 above illustrating that the largest seven borrowers/issuers range from approximately 10% to about half of a given portfolio.

Also, BDC portfolios can consist of less than 30 investments allocated to unique borrowers/issuers to more than 300. The exhibit also illustrates the level of average borrower/issuer concentration in the portfolio that provides additional insight into the degree of granularity across the portfolio.

It is important to note that, in several BDCs, aside from significant exposures to general corporate borrowers, some of the largest investments are allocated to pools of investments (or  ‘funds’). These funds can consist of an underlying portfolio of credits financing private corporate borrowers, real-estate, or equipment as examples.

They can be managed by the same manager of the BDC, a joint venture with the BDC manager, or another fund altogether (e.g., CLO, another BDC, closed-end fund). The latter would likely be managed by a separate entity.

In some cases, these funds can be around 10% or even greater than 20% of the BDC portfolio. Obviously, one can argue that these investment allocations indicate more diversification than one made to a single corporate borrower.

However, the degree of diversification would depend on exactly what investments underly the fund and its individual exposures versus the rest of the BDC portfolio (e.g., borrower overlap). In addition, for an investment in a fund that is levered, like a CLO, it would also depend on what seniority position the investment is within the capital structure (e.g., senior, mezzanine, equity).

Industry/sector concentration

Comparing industry/sector exposures across BDC portfolios is a bit of a challenge since there is no standardized industry reporting classification taxonomy used across the BDC market. Some BDCs classify industries more broadly across their investments while others classify them on a more granular level.

To further complicate matters, as mentioned earlier, certain BDCs have investments that are in funds. It is more difficult to determine industry concentrations in those cases unless one can look through to the underlying investments within those portfolios. For example, it may be unlikely that a CLO portfolio has any significant borrower overlap with the rest of the BDC portfolio since they typically comprise of BSL and that they are often managed independently.

However, this may not necessarily be the case with respect to industries/sectors. In which case, market participants would need to rely on their own assessment based on information made available. Nonetheless, determining diversification across funds or CLO portfolios within a BDC portfolio is beyond the scope of this paper as it can depend on many factors (comparable to those presented here).

Therefore, to make the BDCs more comparable in this respect, with judgement, we mapped the industry classifications that each BDC reported for each investment to a standard group of industry categories.

Exhibit 2: Top Three Industry/Sector Concentration vs. Average Concentration

Initial observations indicate that BDC portfolios are generally concentrated amongst several industries. See Exhibit 2 above illustrating that the largest three exposures typically exceed 40% and in some cases exceed 70% of the overall portfolio. Additionally, the total number of industries represented across a portfolio range from less than 10 to more than 20.

Again, note our earlier comments as some of the top industry classifications may include investments in funds consisting of underlying portfolios. As with Exhibit 1, the exhibit also illustrates the level of average industry/sector concentration in the portfolio providing additional insight as to  the granularity across the portfolio.

Exhibit 3: Common Industry/Sector Concentrations Across BDCs

It is also noteworthy that several industries commonly appear across the BDCs (see Exhibit 3 above). Specifically, these industries are related to high-tech, healthcare & pharmaceuticals, business services, and finance. Furthermore, in many cases, these industries are within the top three most concentrated (see Exhibit 2).

This is a particularly important factor for those investors (or lenders) that have exposures to a portfolio of BDCs when assessing the correlation across their investments as these industries represent some of the largest investments across the universe of BDC portfolios, in terms of both the number of individual investments and fair market value.

Of note, however, these industries could be diversified in their own right into finer industries that would imply lower correlation – for example, within ‘high-tech’ there are companies that focus on educational software, construction technology, cyber, electronics, green technology, health-tech, manufacturing technology, etc.

Not surprisingly, however, the most concentrated BDC portfolios can be less correlated with others since they may have minimal exposure to (or exclude) many industries. This is obviously another key factor for those market participants that allocate (or monitor) investments across BDCs as it can provide an opportunity for additional diversification and/or targeted allocations to certain industries in the private markets.

Asset type/seniority concentration

While we won’t delve into detail for this article, but may be explored further in future Alter Domus research, it is worthwhile noting that the type of and/or seniority level of the investments can vary across BDCs. See Exhibit 4 below for concentration levels across various asset classes as reported by the BDC. We grouped certain types together as they can be considered as other and with relatively small exposures with the exception of CLOs (particularly the most subordinated), which can be significant.

Exhibit 4: Asset type/seniority concentration

Exhibit 4 shows that debt is commonly the most significant portion of the portfolios. This is to be expected as BDCs typically extend credit to corporate borrowers usually in the form of senior secured loans. Within the debt spectrum, these can vary from senior secured loans that are on a first or second-lien basis to those that are unsecured or in the form of notes.

As mentioned earlier, BDCs can offer financing to a single borrower across the capital structure (e.g., senior secured, mezzanine, equity) depending on the BDC manager’s financing strategy. The combination may explain some of the significant equity exposure. The equity exposure can be an investment in a levered fund as well.

Conclusion

Non-bank lenders, within the private credit market, have been an important alternative source of loan funding for smaller to middle-sized U.S. companies through various platforms (e.g., direct lenders, closed-end funds, BDCs). BDCs are particularly worthy of attention since they offer a spectrum of investors an opportunity to participate in this lending asset class and as many are publicly traded.

Portfolio compositions can vary quite significantly across BDCs even though BDCs typically consist of leveraged senior secured loans extended to smaller and middle-sized U.S. borrowers. These differences across BDCs offer investors a menu of risk/return profiles to suit their needs.

We presented our observations on variations in diversification across BDC portfolios based on an evaluation of elements that are key indicators to the overall level of diversification, with an emphasis on concentrations to unique borrower/issuers and industries/sectors.

In conclusion, our analysis indicates significant differences of diversification across BDC portfolios, which provides investors opportunities to better manage across the risk/return and portfolio diversification spectrums.

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Appendix: List of BDCs

BDC Name	Trading Symbol
Ares Capital Corp	ARCC
FS KKR Capital Corp	FSK
Blue Owl Capital Corp	OBDC
Blackstone Secured Lending Fund	BXSL
Prospect Capital Corp	PSEC
Golub Capital BDC, Inc	HTGC
Main Street Capital Corp	MAIN
Hercules Capital Inc	HTGC
Sixth Street Specialty Lending	TSLX
Oaktree Specialty Lending	OCSL
Barings BDC, Inc	BBDC
MidCap Financial Investment Corp	MFIC
Bain Capital Specialty Finance, Inc	BCSF
SLR Investment Corp.	SLRC
CION Investment Corp	CION
BlackRock TCP Capital Corp.	TCPC
Crescent Capital BDC Inc.	CCAP
PennantPark Investment Corp.	PNNT
PennantPark Floating Rate	PFLT
Trinity Capital Inc	TRIN
Capital Southwest Corporation	CSWC
Fidus Investment Corp	FDUS
Stellus Capital Investment Corp	SCM
Saratoga Investment Corp	SAR
Gladstone Investment Corp	GAIN
Horizon Technology Finance Corp	HRZN
Portman Ridge Finance Corp	PTMN
Monroe Capital	MRCC
Oxford Square Capital Corp	QXSQ
Great Elm Capital Corp	GECC
Loan Ridge Finance Corp	LRCF
PhenixFin Corporation	PFX

The challenge

Getting a clear picture of your net asset value, or “NAV” as it’s known, is a crucial calculation for every investment company. Essentially, a company’s NAV is its total assets minus its total liabilities, and as the number of funds and assets accumulate, the task of calculating this becomes ever more arduous – especially when using legacy tools such as Excel.  We were approached by one of our key clients to see if we could create a solution to this labor-intensive task.

Their team faced the challenge of having to manually consolidate up to 11 Excel files to create one final NAV dashboard for each of their funds. What is of course crucial is that as the volume of data and manual work increases, the greater the possibility for data inaccuracies and calculation errors. On average, this entire process took days for their team to complete.

The solution

To address this challenge, the team proposed and designed a solution that involves the user uploading the 11 Excel files, including trial balances, bank balances, and FX rates, into a tool that consolidates them into a single NAV dashboard. Once the consolidation is complete, the user receives an email containing the new dashboard.

How the process and AI bot works (per each fund)

STEP 1:

• Alter Domus team logs into the Web Apps Portal and runs our NAV tool

STEP 2:

Alter Domus team uploads the 11 Excel sheets received from client, including:

• Trail balance, bank balance, collateral abacus & eFront
• FX rates, loan & credit facilities, accruals listing
• Loan request, ICAS previous & current period, hedging

STEP 3:

• Bot opens each Excel sheet. It copies and pastes, and completes lookups from the column data in the various spreadsheets
• Bot cleans data where required

STEP 4:

• Significant reduction of the time and effort required by client’s team to complete this task: what once took days now takes minutes
• Enhanced data and calculation accuracy
• Client’s team is freed up to focus on less manually intensive, more strategic tasks
• The bot is able to handle increasing volumes of this work, eliminating the need for the client to hire additional staff or reallocate internal resources 

Implementation time

• 2 months to develop the bot
• 1 month in UAT
• 1 month in control production

Contact us today to find out how you, too, could benefit from our use of artificial intelligence in delivering impactful technology solutions.

After more than 36 years of existence, the London Interbank Offered Rate (LIBOR) was discontinued in July 2023 in favor of alternative references rates, such as SOFR (Secured Overnight Financing Rate). As mentioned in previous Alter Domus reports, we discussed the need to migrate away from LIBOR and provided updates on the transition as the cessation date grew closer.

Now that LIBOR is no longer published, we report on the impact it continues to have, albeit declining, in the U.S private debt market and address any lingering considerations.

Announcement of LIBOR’s future discontinuation in 2017 posed an intimidating feat, like an impending storm, as LIBOR was pegged to almost all contracts accounting for billions of dollars of debt. Six years later, though, market participants made it through the storm quite successfully.

Overall, the LIBOR transition was, as the AARC[1] puts it, “smooth and uneventful.”[2] Much of the success is attributed to the careful planning and coordination between agents, like Alter Domus. borrowers, lenders and financial regulators.

Moreover, as the June 30,2023 deadline approached, there was a predictable uptick in credit agreement amendments and triggering of existing ones. As reported by the LevFin Insights, amendments spiked in June, nearly totaling 300, representing almost 3 times the amount of amendments in May and April[3].

The same can be inferred with respect to the direct lending space, as we turn to publicly available BDC (Business Development Companies) data as a proxy. From a sample of 13 BDC’s registered with the SEC, representing over $33bn in portfolio valuations or about a quarter of the publicly traded BDC market, we observe from the 2023Q2 SEC filings that the average decrease in loans tied to LIBOR jumped to 27% during 2023Q2 from 10% in 2023Q1.

The graph and table that follow clearly illustrate the declining relevance of LIBOR and rising relevance of SOFR in the last four quarters – with SOFR beginning to equal and surpass LIBOR in Q1’23. Over the past 9 months, SOFR-pegged loans have gone from 28.5% of total floating loans to 75% for the loans in the underlying sample portfolios.

LIBOR Declines While SOFR Grows (sample of 13 BDCs)

Count of Floating-Rate Investments – Sample of 13 BDCs over the past 4 quarters

Base Rate	Q3’22	Q4’22	Q1’23	Q2’23
LIBOR-based floaters	1,232	1,117	986	454
Non-LIBOR-based floaters	550	769	1,005	1,555
Total Floaters	1,782	1,886	1,991	2,009
LIBOR as a % of Total Floaters	69.1%	59.2%	49.5%	22.6%
SOFR-based floaters	507	715	942	1507
SOFR as a % of Non-LIBOR	92.2%	93.0%	93.7%	96.9%
SOFR as % of Total Floaters	28.5%	37.9%	47.3%	75.0%

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We’ve currently discussed the recent increased migration efforts that led to a successful migration away from LIBOR, but does that mean all loans refer to an alternative rate? Not quite. Currently, in the same BDC sample as of the end of 2023Q2, 75% of loans reference SOFR.

With regards to the leveraged loans and the CLO market, the results are similar, as 76% of loans reference SOFR (as of August), per LSTA.[4] The reason why these numbers are not 100% is due to contracts that were opened prior to the June 30,2023 deadline that still refer to a LIBOR reference rate.

Since most contracts are 3-months, we should expect to see a considerable amount of loans automatically migrating to SOFR (via the LIBOR act or through the triggering of an amendment). We will continue to track this migration as it nears completion.

Overall, it appears that the transition has gone quite smoothly, thanks to years of thoughtful preparation. Loans that are currently tied to LIBOR will eventually point to an alternative rate once the contracts next renew – post June 30.

The AARC, which was tasked to determine the proper alternative rates to transition to, will slowly[5] wind down its work, emphasizing that the SOFR should be deemed the best option as the alternative rate given its robust tie to the transaction-heavy treasury market.

Going forward, we will keep readers updated on the status of the remaining loans that still reference LIBOR and hope to look past the migration with a greater sense of triumph.

Sample of BDCs in our study:

Audax Credit BDC Inc.
BlackRock Private Credit Fund
BlackRock TCP Capital Corp.
Capital Southwest Corp.
CION Investment Corp.
Fidus Investment Corp.
Great Elm Capital Corp.
Oaktree Strategic Credit Fund
Oaktree Specialty Lending Corp
Blue Owl Capital Corp.
Oxford Square Capital Corp.
PennantPark Floating Rate Capital Ltd.
Prospect Capital Corp.

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[1] Alternative Reference Rates Committee

[2] https://www.newyorkfed.org/medialibrary/Microsites/arrc/files/2023/ARRC-Readout-July-31-2023-Meeting

[3] https://www.lsta.org/news-resources/libor-transition-one-two-last-hurrahs/

[4] https://www.lsta.org/news-resources/libor-cessation-t1-month/

[5] https://www.newyorkfed.org/medialibrary/Microsites/arrc/files/2023/ARRC-Readout-July-31-2023-Meeting

Alter Domus previously demonstrated through a concise example how total credit losses from an underlying collateralized loan obligation (CLO) portfolio would equate to the total credit losses across the CLO’s notes, including the CLO equity tranche. This significant analysis considers various factors such as spreads, rate, and debt.

In that same research Alter Domus pointed out, however, that CLOs typically include structural features where excess interest, which would otherwise have been distributed in traditional portfolios, such as bank balance sheets or insurance company general accounts, can be diverted to provide credit support for CLO tranches upon an economic downturn.

This diversion of excess interest income means that total portfolio credit losses are reduced by the amount of excess interest diverted. In particular, the formula in estimating CLO tranche credit losses, as a function of the portfolio credit losses, is: Losses Allocated to CLO Tranches = Total Portfolio Credit Losses – Excess Interest

In this article, we expand our previous research by analyzing the impact of the relative benefit of excess interest, which is highly dependent on the overcollateralization (OC) test. This test translates to reduced expected loss (EL) of the underlying loan portfolio and increased investment value.

Specifically, the purpose of our research is to estimate the ‘Excess Interest’ from the formula above. As expected, we find that excess interest can mitigate credit losses in the underlying portfolio, but the extent to which is sensitive to several variables, such as the average credit quality of the portfolio, the available amount of excess interest over the life of the CLO, and portfolio weighted average life (WAL).

The results of our research indicate that a reasonable range of the reduction in portfolio EL can be from 6%-14%, with an 11% average, depending on the level of ‘stress’ one wants to apply in their analysis. CLO managers need to align their strategies to maximize performance while observing market changes. Other factors, such as prevailing market conditions, could also impact this range. Based on our findings, we could rewrite the formula above as follows:

• Low Value: Losses Allocated to CLO Tranches = 86%* Total Portfolio Credit Losses 
• High Value: Losses Allocated to CLO Tranches = 94%* Total Portfolio Credit Losses 
• Average Loss: Losses Allocated to CLO Tranches = 89%* Total Portfolio Credit Losses 

A review of the OC test

CLO market observers understand that a critical feature in CLOs is the benefit of excess interest generated by the underlying CLO portfolio to provide credit support for the CLO tranches. But the benefit of excess interest in reducing the EL for CLO tranches is highly dependent on several variables.

A critical structural feature in CLOs that helps ‘trap’ this excess interest to support the CLO tranches is the OC test (note that CLO structures will typically have several OC tests at various points in the waterfall – in this paper the singular term ‘OC Test’ refers to all OC Tests collectively).

With no OC Test, the benefit of excess interest will be limited because most of the excess interest will leak out to the equity investors – this would mirror to a certain extent the treatment of excess interest for a portfolio of loans on a bank’s balance sheet or an insurance company’s general account, where the income could be used for other purposes by those institutions and not trapped and secured for the benefit of the CLO noteholders.

OC tests are generally incorporated within the CLO cashflow waterfall. Upon a breach of such test, they are intended to divert available interest proceeds first, then principal proceeds if needed, to pay down the notes in order of priority until cured. Typically, there is a test for each class of notes except for the class that was initially the senior most. In that case, any proceeds diverted are at the expense of the equity including the relevant junior notes depending on which test is breached.

The OC test is usually calculated based on the ratio of the total par amount of the underlying portfolio (plus principal proceeds) over the outstanding amount of the relevant class of notes (including those that are senior to it). However, there are adjustments in the form of haircuts that are commonly applied to the portfolio.

These typically include those that are classified to be in default or purchased at a deep discount as well as the total amount of those rated Caa/CCC in excess of a predefined limit. CLO managers must meticulously monitor collateral quality to enhance the performance of CLOs. OC tests are more likely to be breached upon credit losses and/or excessive negative credit migration.

It is important to note however that the ultimate benefit of this feature can depend on many factors as CLOs are complex and the behavior of the economic environment is uncertain. Some examples of these factors include:

• Available excess interest (e.g., weighted average spread of underlying loans, cost of debt across the CLO notes, behavior of reference rates)
• Amortization profile, prepayments (i.e., introduces reinvestment risk)
• Timing of defaults (e.g., front loaded defaults may trigger tests earlier)
• ‘Tightness’ of OC test trigger levels (e.g., lower trigger levels may reduce effectiveness of tests)
• Portfolio manager trading behavior (e.g., can increase – or decrease – par amount of portfolio through substitutions particularly when an OC test is failing marginally)

Assessing the benefit of the OC tests – a simple analysis 

We have just described the basic mechanics of how OC tests work and highlighted a few examples that can impact their benefit, several of which are quite uncertain and challenging to forecast. Therefore, we choose to control for most of those variables and start from a simple stylized CLO as the intention of our analysis is to demonstrate the economic benefit of excess interest.

The benefit from this form of potential credit enhancement can be explained as to the reduction of the underlying portfolio EL. The analysis will also include an examination of the sensitivity while changing several variables that are straightforward.

To begin, we created a stylized CLO by reviewing a sample of recent CLO structures. The objective of this step is simply to assess how much excess interest is generated in a CLO structure. However, it is safe to say that excess interest is a prerequisite for any CLO structure.

Although the amount of excess interest would change over time based on market conditions, we could generalize that a CLO structure would not be issued when market conditions do not offer sufficient excess interest. See Tables 1-3 for assumptions made for our hypothetical CLO.

As we had noted in previous research, the collateral quality test matrix, typically incorporated in CLOs, is a clear indicator of the benefit of excess interest – the EL of the CLO notes would not be expected to materially change even as the credit risk of the portfolio increases because of the reduction in losses resulting, for example and most commonly, from an increase in excess interest.

The Matrix Scenarios provided in Table 3 were calibrated so that the tranche ELs of our hypothetical CLO were relatively consistent in each of the designed scenarios. CLO matrices typically provide many more options than the modeled scenarios presented here.

The portfolio is assumed to be static, consisting entirely of floating rate first-lien senior secured loans. Recoveries are also assumed to be realized immediately upon a default.

Table 1: Hypothetical Capital Structure

Table 2: Subset of Collateral Quality Tests

Table 3: Collateral Quality Test Matrix

With this stylized CLO, we assumed that the reference rate is 0%. This assumption allowed us to get a clearer picture of the excess spread that could be applied to reduce the portfolio EL. As reference rates rise, the amount of excess interest, depending on a given scenario of losses, can increase thus potentially offering greater credit enhancement.

For our analysis, we ran our model for each matrix scenario under a range of different stresses. We also ran various combinations of default rates, recovery rates and portfolio WAL for each of the matrix scenarios. The results of the various combinations are shown in Tables 4 and 5.

For each of the four matrix combinations, we ran the CLO cash flows assuming various levels of default rates (historical averages based on ‘idealized’ levels) and stressed default rates. We also ran two different recovery rate scenarios – again stressed and closer to historical averages.

We also ran two combinations of portfolio WAL. This broad range of assumptions allowed us to study the sensitivity of the excess interest in reducing portfolio EL. The column labeled ‘% Reduction of Portfolio EL – Note Paydowns Only’ represents the probability-weighted amount of interest proceeds that are diverted to pay down the notes, excluding any payments to the first-loss equity tranche. We believe that this number is a reasonable estimate of the portfolio EL reduction attributed to excess interest.

Table 4: Portfolio WAL of 7 years

As we mentioned earlier, the collateral quality matrix offers the portfolio manager to choose amongst different combinations of portfolio characteristics (e.g., average rating, WAS, diversification) while maintaining the credit quality of the notes. In other words, and as we had noted in our previous research, the collateral quality matrix is the proof and our guide in trying to assess the reduction in portfolio EL resulting from excess interest.

In Table 4 we note that for the four different scenarios, the average reduction in Portfolio EL attributed to the excess interest ranges from 6.4% to 17.1%. As expected, the percent reduction of the portfolio EL increases as the portfolio EL increases within each set of matrix scenarios. Indeed, this result is also indicated by the percent of the total interest proceeds that is diverted to pay down the notes due to the OC test. In the matrix scenarios presented here, the ‘required’ WAS, an indicator of excess interest, of the portfolio increases as the average credit rating decreases.

It is also worthwhile noting that the proportion of excess interest that is distributed between the equity and what is diverted due to the OC Test over the life of a CLO is subject to uncertainty, some examples of which we highlighted earlier. However, it can be said that holding all else equal, a greater proportion would be expected to be diverted, the higher the OC trigger level. In which case, it would lead to a further enhancement against the portfolio EL.

Table 5: Portfolio WAL of 5 years

Comparing the results within Table 5 show a similar behavior to Table 4. However, when comparing the results between the two, notice that both the percent reduction to the EL and the percent of the total interest proceeds that are diverted are lower. This can generally be explained by a combination of lower portfolio ELs from lower probabilities of default and that the expected amount of available excess interest over the life of the CLO is lower, both due to the shorter duration of the portfolio. The range of portfolio EL reduction due to excess interest is 5.1%-10%.

Nonetheless, the results from the tables above are compelling in demonstrating the case that excess interest reduces the portfolio EL and provides our first estimate of the ‘Excess Interest’ in terms of reduction of the portfolio EL.

Based on the assumptions above, and for the broad range of different model inputs, we found that excess interest can reduce the portfolio EL in the range of approximately 6%-14% with an average portfolio EL reduction of about 11%. We derived this range by simply averaging and rounding the ranges of the two different tables.

We have also shown that it is quite challenging to derive a precise point estimate of the benefit of excess interest given the complexity of the analysis, the various assumptions that could be made and changing market conditions. As a result, we provide our views of a reasonable range of the benefit of excess interest in reducing portfolio losses. Essentially, we attempt to answer the following question:

‘What is the benefit from Excess Interest based on the formula below?’

Losses Allocated to CLO Tranches = Total Portfolio Credit Losses – Excess Interest

We believe that, based on our example, a reasonable range could be somewhere from 86% to 94% of the gross portfolio EL as compared to assuming no excess interest in the CLO. 

A few notes

We have noted that our analysis included certain assumptions in order to demonstrate the impact of excess interest in reducing portfolio EL. Beyond the assumptions that have already been noted, other considerations that could impact our results include:

a. We assumed that none of the equity distributions reduce portfolio EL. This is a conservative assumption but given the complexity in trying to  estimate the composition of equity distributions between interest and principal are beyond the scope of this paper. Either way, the equity tranche assumes the bulk of the portfolio EL anyway – as demonstrated in our previous research paper.

b. Our hypothetical CLO – see Tables 1-3 – was created based on relatively recent market conditions. Hence, the relatively large spreads on the notes (and to a certain extent, the portfolio). Under different market conditions, the results of our findings could be different. But generally, with narrower spreads on the notes, the benefit of excess interest increases and thus further reducing portfolio EL.

c. We used a limited number of matrix combinations. Using a broader and finer range of matrix combinations may show different results under different collateral quality test combinations.

Structural Dynamics of CLOs: Insights from OC Test Breaches

In this article, we have assessed and demonstrated the credit enhancing benefit of excess interest within CLOs as they typically incorporate a key structural feature, the OC test. In the event the OC test is breached, excess interest, which would have been distributed to equity, would be available to pay down the most senior note until cured.

Our analysis was based on several assumptions applied to a simple hypothetical CLO structure. Several scenarios were applied to observe the relative impact while changing several variables: the average credit quality of the portfolio, the available amount of excess interest over the life of the CLO, and the portfolio WAL.

The metric used to measure the relative benefit was based on the reduction in the EL of the underlying loan portfolio. As expected, we found that excess interest proceeds generated from the portfolio reduces the EL on the CLO tranches in all cases.

The results show an average reduction in portfolio EL of about 11%, though this amount is subject to a limited range of assumptions.

Although the results indicate that the impact can be sensitive, it is nonetheless clear and not surprising that this key feature of trapping excess interest, at a minimum, can provide additional credit enhancement.

Exploring options and best practices

The London Interbank Offered Rate (LIBOR), a controversial cornerstone of the financial system, will be discontinued on June 30,2023. In a previous article published in December 2022, we discussed the need to migrate away from LIBOR and the migration status for the U.S private debt market[1].

As LIBOR will be discontinued in less than 30 days, we will provide updates on the current migration status and answer key questions: what happens if no explicit migration plan exists before LIBOR is discontinued and what are the current industry trends regarding credit adjustment spreads[2]?

Current Status of migration for U.S. Private Debt Market

According to the LSTA, in reference to the JPM index as of April 2023, roughly 40% of private loans are currently on SOFR[3]. With respect to the direct lending space, we turn to publicly available BDC (Business Development Companies) data as a proxy. From a sample of 12 BDC’s registered with the SEC, we observe from the 2023Q1 SEC filings that on average 49% of loans are pegged to SOFR.

For the same sample, 2022Q3 and 2022Q4 SEC filings report SOFR loan percentages of 23% and 35%, respectively, showing an increased effort to transition away from LIBOR as the cessation date approaches. While industry trends suggest a concerted effort to convert loans to SOFR, we would likely to expect these figures to be close to 100% as we have to count days and not months until LIBOR is discontinued.

However, data from Covenant Review reveals that over 90% of syndicated loans have language in their contracts or amendments that determine the alternative rates at the time of LIBOR cessation[4]. Moreover, as discussed in the previous paper, industry trends point in the larger private debt space point to a larger effort to add amendments to contracts to trigger immediatley after LIBOR cessation date rather than transition earlier. Therefore, although the numbers are not nearly close to 100% as we would expect, there are already contractual agreements in place for a vast majority of loans to migrate away from LIBOR once it is discontinued.

But what about the contracts that are not in the “vast majority” that do not have an alternative rate in place come June 30,2023? What will those loans be tied to if not LIBOR? The Federal Reserve Board, in December 2022, provided its final set of guidelines for such contracts[5].

Fallback options for contacts without alternative rate transition plans prior to June 30, 2023

Final rule regarding the implementation of LIBOR ACT

Widely expected, the Federal Reserve Board in December 2022 published its final guidance to implement the LIBOR act[6]. This guidance is to explicitly provide a benchmark replacement for contracts governed by U.S law that refer to either the overnight, 1-month,3-month, 6-month and 12-month LIBOR tenors and do not have a clearly defined benchmark to transition to after June 30,2023. 

In general, the guidance determines if there is a “determining person” in the contract. If so, that person should determine the replacement contract by the LIBOR cessation date. If the person does not, or if there is no “determining person” in the contract, the contract must transition to the corresponding Term SOFR plus the prescribed credit adjustment spread based on ISDA/AARC fallback protocols[7][8].

As Term SOFR is typically lower than LIBOR (as it does not involve credit risk), the credit adjustment spread is added to the current spread/margin to reflect a number closer to LIBOR. For clarity, if a loan has locked in a 6-month LIBOR rate on June 15, 2023, it will still be valid until the contract ends on December 15, 2023. After the contract ends on December 15, however, the loan must be tied to an alternative rate. The table below summarizes the final guidance for the current published LIBOR tenors:

In short, if there is no transition plan, the guidance will provide one. The guidance provides a particular credit adjustment spread depending on the tenor (1-Month, 2-Month, etc.), but how were those spreads determined and how are market participants adapting to them?

Credit Adjustment Spreads: ISDA/AARC Recommendations and Trends

Because private debt loan amounts generally range between 10s or 100s of millions of dollars, it is no surprise that both borrower and lenders are highly sensitive to change in spreads and thus haggle to achieve a mutual agreed upon credit adjustment spread[9]. For example, in a loan of $100,000,000, an increase in 20 basis points to the spread results in an increase of roughly $50,000 in interest payments every quarter. Given a tense negotiating environment, are borrowers and lenders trying to negotiate their own credit adjustment spreads or adopt the ISDA/AARC recommendations? Recent evidence points to the latter.

According to the LSTA, 65% of tracked amendment fallbacks used the recommended credit adjustment spreads in April 2023, up from roughly 57% in March 2023[10]. Although there is no clear trend when looking at January and February, it is likely that the strong percentage will persist in May as the transition deadline approaches and the room for negotiation narrows.

Because the recommended spreads are 5-year median differences (March 2016-March 2021), the values should in theory be stable and subject to less litigation in the near future[11].

Conclusion

As the LIBOR cessation date nears, loan migration away from LIBOR picked up considerably in 2023. Thanks to the final guidelines published by the Federal Reserve Board in December 2022, there is clear guidance to ensure a (relatively) smooth transition away from LIBOR.

It is up to market participants to act proactively to ensure the proposed credit adjustment spreads are economically viable and confirm the final transition plans for each contract. As LIBOR is currently pegged to trillions of dollars, it is paramount for market participants to adequately forecast the financial impacts resulting from LIBOR migration.

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Footnotes:

[1]For purposes of this paper, ‘private debt’ refers to broadly syndicated loans and private direct lending markets. See existing Alter Domus report for further details.
[2]The credit adjustment spread added to the current spread/margin on a loan to compensate the lender for switching to an alternative rate, which is typically lower than LIBOR.
[3]https://www.lsta.org/news-resources/libor-transition-45-days/
[4]https://www.lsta.org/news-resources/usd-synthetic-libor-exactly-as-expected/
[5]https://www.federalreserve.gov…
[6]https://www.congress.gov/bill/…
[7]https://www.govinfo.gov/conten…
[8]The credit adjustment spread added to the current spread/margin on a loan to compensate the lender for switching to an alternative rate, which is typically lower than LIBOR.
[9]https://www.bloomberg.com/prof…
[10]https://www.lsta.org/news-reso…
[11]https://www.newyorkfed.org/med..

The leveraged corporate loan market has grown considerably over the past decade. Though a large portion of loan issuance over the years has been to those that are broadly syndicated (“BSL”) – a market that exceeds $1.5tn – growth in the non-syndicated loans issuance market (i.e. private debt) has been equally noteworthy. Estimates indicate total private debt issuance has exceeded $1tn. Both of these markets have been significant contributors to funding the growth of private companies. Both financing methods play crucial roles in supporting business operations among varying company sizes, from large-scale corporations to those in the private sector.

Rudolph Bunja, Head of Portfolio Credit Risk at Alter Domus, draws attention to these two important areas of the market and touches upon some of the key differences between the two funding sources. In this commentary, he provides a high-level comparison of the two based on a variety of characteristics, and shares examples of certain factors for investors to consider during the valuation of alternative investments.

A comparison of private debt and broadly syndicated loan characteristics

BSLs, or broadly syndicated loans, are sponsored by a primary agent and syndicated across a group of commercial banks and specialist loan investors, primarily Collateralized Loan Obligations (“CLOs”). This is different than private debt, which is typically sponsored by non-bank lenders. They are commonly included in a variety of vehicles such as middle-market CLOs, BDCs (more common in the US than in Europe), and funds managed by direct lenders. In contrast, most of the BSL market is held by commercial banks, CLOs, and other types of funds (e.g., mutual, ETFs, closed-end funds). These are important distinctions that affect the two instruments to the point that they can be seen as almost two different, albeit similar, asset classes.

Private debt loans are expected to be held until maturity as they are relatively illiquid while broadly syndicated loans can be bought and sold with greater ease. The trading of syndicated loans enhances liquidity and allows investors easy access to exit their positions when needed. Given the complexity and bespoke nature of these arrangements, loan agency support plays a key role in facilitating administration and communication between borrowers and lenders. Private debt issuers have few lenders, are typically unrated, and often have higher leverage characteristics compared to BSLs. However, private debt is generally expected to have higher comparable yields to a BSL holding all else equal.

It is also noteworthy that private debt lenders tend to have greater flexibility when negotiating loan terms such as covenants and which can lead to a faster time of execution. At the same time, BSLs are part of a larger group of participants, which can extend the process of execution. Private debt lenders, especially in sponsor-backed transactions, will often be able to perform even more thorough due diligence of their borrowers and could be more active in arranging for operational support, business advice, and future funding. This is not surprising given the smaller lender base and the longer-term holding period for private debt lenders compared to BSL lenders.

Considerations

As expected, a major factor influencing an investor’s decision to invest in a loan fund (e.g., CLO, direct lending fund) is an evaluation of the credit expertise of the collateral manager or direct lender, including their overarching strategy. This includes a review of their credit selection process when constructing the portfolio. Equally important is the manager’s diligence in credit surveillance considering there could be substantial downside risk, with limited upside, assuming they are not investing in assets at a significant discount (e.g., distressed debt funds). For the private debt market, given the stability of the lender group, investors should also consider the lender group, especially the lead lender, to assess the motivation and ability of the group to offer support for the borrower – whether it is operational or financial support. This feature may not be as important in BSLs since the lender base will often change given the ability of BSL investors to trade in and out of their holdings.

A single lender (or small group) managing a group of credits across a portfolio of private debt may be more adaptable to varying economic conditions based on their strategy, as they were closely involved during the underwriting process. This adaptability is particularly vital when signs of credit impairment appear or in the event of a workout process upon default, allowing more responsive decision-making.

Private debt is relatively illiquid and expected to be held until maturity. Therefore, investors need to carefully consider the lender’s in-house expertise to be proactive with the borrower in distressed credit situations. That expertise may also include the ability to provide business and operational support to the borrower. Lenders, especially sponsors, will often have in-house expertise to support their portfolio companies around business, technology and other critical functions. Lenders may also have access to a wide network of third-party experts that they can offer to the benefit of their borrowers.

In contrast with a BSL, the lenders would generally be minimally involved in providing business advice/support to borrowers. Trading strategies in the BSL market may also involve leveraging market conditions to optimize returns for institutional investors. Typically, in a distressed situation, a BSL manager can lose leverage when negotiating terms upon a restructuring as there can be competing interests among the underlying lenders/investors within the BSL. For example, CLO mangers may be subject to certain constraints when voluntarily agreeing to a maturity amendment of a distressed loan. However, BSL managers do have greater flexibility to dispose of a loan when signs of credit impairment exist as reliable loan prices are more readily observable and managers may be more likely to dispose of the loan at some point following a potential credit event or default as opposed to going through a period of credit risk volatility or even a workout process.

The ability of lenders to monitor their credit portfolio rests strongly on their ability to access, analyze, and secure financial and other types of relevant data about their borrowers. BSL data is often readily available in many cases and potentially with some standardization, though it’s still challenging to manage all the data. Private debt lenders also have access to significant amounts of data – often more data, though typically with almost no standardization.

With demands for even more data, such as ESG data, the pressure on lenders to ensure a proper data and analytics infrastructure becomes even more important. The ability to access and analyze that data is no easy feat for both BSL and private debt lenders and building these capabilities can provide them with a competitive advantage. In either case, it is an essential part of the surveillance process and lenders should carefully assess their ability to manage the data challenges surrounding private debt.

Conclusion

Leveraged corporate loans, which largely consist of those that are broadly syndicated or private, continue to be an important source of funding in the private corporate markets. These two segments, which have experienced significant growth and offer investors strong financial risk return performance, are supported by robust corporate services that help manage and optimize loan structures.

They can also present distinct challenges, for example, when there is potential credit impairment of the borrower that requires proactive diligence and the ability to closely track and monitor performance. In this regard, managers continue to reinforce and expand their capabilities while private debt investors need to carefully consider the credit expertise of the manager within their respective market as a key factor during their investment evaluation.