Multi-Asset Credit (‘MAC’) strategies have become very popular with pension funds due to their flexible, dynamic and diversified approach. But, how can allocators to these products anticipate their behaviour? Can a ‘jack of all trades’ approach ensure compliance with strategic objectives? Is there a better way to meet the end goal? These are questions we asked ourselves and prompted us to share our thoughts with you.
MAC is a difficult ‘asset class’ to define precisely. The difficulty arises largely because the term does not refer to a specific asset class. Rather, it’s a reference to an approach to investing in a series of them. There are numerous MAC products available in the market with varying risk and return characteristics. The reasons for such a plethora of funds are:
- The vast opportunity set (in terms of those credit strategies that managers decide to blend in their funds), and
- The approach to each individual asset class.
Redington’s definition of MAC differs from the wider market and can be summarised as the preferred way of accessing leveraged finance. Hence, the adoption of the term Multi-Class Credit (‘MCC’). In our view, MCC is an approach that provides exposure to high yield, leveraged loans, and credit instruments with primarily a sub-investment grade rating. The reason for this differentiated definition of MCC stems from the problems we have observed in wider market. The most serious of these are:
- Overdiversification. These funds tend to blend a wide variety of asset classes and asset allocation remains static.
- Lack of conviction and accountability. This is a second order effect of overdiversification. Putting together a wide mix of asset classes with different risk/return characteristics and delegating the decision-making to “sleeve” portfolio managers historically, has led to large portfolios with benchmark-like behaviour.
- Mix of spread and rates. There are a lot of MAC funds which combine bottom-up credit selection with a significant macro or directional rates view. In the context of pension funds, we believe that a directional rate view is an unrewarded risk.
Opportunity Set Analysis
Our narrower definition when compared to the “jack of all trades” portfolios we see in the market might raise questions around the size of available opportunity set and the manager’s ability to build an efficient portfolio. However, a closer examination of the high yield and leveraged loans markets in the US and Europe tells a different story.
US H/Y and Lev. Loan issuer crossover
The US Leveraged loan market has a higher number of issuers compared to the High Yield market, and issuers have more than one loan in the index. The overlap of issuers in the US market ranges between 12-17% of the total number of issuers in both markets.
European H/Y and Lev. Loan issuer crossover
The European Leveraged loan market, just as its US counterpart, has a higher number of issuers compared to the High Yield market. Interestingly, there is a strikingly low issuer crossover in both the US and European markets, ranging between 4-8% of total issuers.
The correlation between US and European High Yield is high, but the underlying opportunity sets are structured differently (i.e. US High Yield has a higher weight to Energy-related issuers). Similarly, the credit quality breakdown of the two High Yield markets is different. The European market has a greater percentage of fallen angels (formerly investment grade borrowers) which has contributed to the bias towards a higher rating (US H/Y: B+ vs EUR H/Y: BB-).
The Investor base is also different at the asset class level. The high yield bond market is more fragmented and better diversified but also dominated by dedicated bond funds, ETFs, and insurers. Syndicated loan demand is mostly driven by managers of collateralized loan obligations (CLOs) and institutional investors.
Across geographies, the investor bases also differ with liquidity and regulation being the key drivers of this. For example, the participation of retail investors in the US leveraged loan market is circa 25%. In Europe, however, regulations limit retail investment in the leveraged loan market.
Quantitative Analysis
Having mentioned above the structural differences between US and European high yield and leveraged loans we wanted to examine how these come into play at the benchmark level by looking at the correlations across benchmarks[1].
As can be seen from the table above correlation between leveraged loans in the US and Europe is high. The same applies for high yield. However, correlation drops slightly -as expected- when we compare different asset classes. We repeated the same analysis for a shorter time period and the was no significant statistical difference.
The next step of our analysis was to examine the correlation of excess return of MCC managers. We used the 5-year excess returns over the managers’ respective benchmark while making sure that managers included in our sample had delivered positive excess return. Failing to do so would have led to strong negative correlation but for the wrong reason -underperformance of benchmark-. The results are presented in the table below:
Correlation of XS Return (5yrs)
The key takeaway from this sample of rated managers, is the low correlation of excess returns. This shows significant differentiation of alpha sources across managers and is consistent with our qualitative view, and the different opportunity sets the managers can exploit. We did the same analysis using the 3-year returns and there was no significant change in correlations.
The opportunity set and correlation analysis suggest, that Multi-Class Credit managers with a geographic focus (US, Europe or Global) are able to deliver diversifying alpha to each other. Hence, the size of the high yield and leveraged loan opportunity sets offer enough differentiation to merit a global Multi-Class Credit approach where the manager is responsible for making asset allocation decisions both in terms of sectors, asset classes, but geographies as well.
What does this mean for investors?
Having discussed the pitfalls across most MAC approaches and after analysing the opportunity sets, Redington’s Multi-Class Credit approach provides an answer to these problems by:
- Focusing on a smaller opportunity set but large enough to allow managers to express their top-down views. This way, we can better evaluate managers’ asset allocation decisions over time.
- Focusing on spread as a driver of returns. Specifically, we have a strong preference for managers with strong bottom-up credit selection capabilities to identify attractive individual opportunities and ‘earn’ the relevant spread over time, avoiding defaults. We do not see rate views as a sustainable source of alpha in MCC.
- Offering flexibility. The managers in this space have the freedom to use additional asset classes in the portfolio construction where, in our view, there is clear evidence of specialization and therefore, a competitive advantage over peers.
- Aligning the internal model of expected returns with how managers run the portfolio ensures that the Strategic Asset Allocation is fulfilling its purpose, and a lower budget can be allocated to governance.
MCC funds are not the panacea to all problems but they are suitable vehicles to allow managers to express high conviction in individual, specific opportunities given the absence of a benchmark at the security and asset class level and allocate to less liquid and more complex credits such as CLOs, RMBS, and subordinated financials. Investors need to keep in mind that such funds will have drawdowns, but they should expect the predictability of “credit like” returns. Hence, investors need to focus on holistic portfolio construction and long-term strategic objectives.
[1] Benchmarks used: CS Leveraged Loans, CS Western European Leveraged Loans, ICE BofA ML Euro High Yield (HE00) and ICE BofA ML US High Yield (H0A0) as at end of July, 2019
