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EDGE - Global Fixed Income Opportunities (GFO)

A systematic approach to fixed income investing

The EDGE Global Fixed Income Opportunities fund (GFO) is a total return-focused fund targeting a return of cash + 3-4% annually across the busi-ness cycle. To achieve these returns, the fund invests across the rating spectrum from US treasuries to high yield bonds and actively manages the duration exposure. To mitigate the drawdowns associated with high yield and emerging market bonds when the business cycle turns and the growth outlook deteriorates, the fund uses behavioural finance inspired machine learning algorithms in the asset allocation process. This invest-ment methodology also aims at protecting against duration driven sell-offs, such as those seen in 2013, 2016 and in late 2020 and early 2021.

Central bank intervention across US and Europe has led to a global yield compression and most high-grade bonds now have yields below 2.0%. These bonds have typically delivered negative returns since the middle of 2020. The market situation leaves fixed income investors looking for yield above inflation no choice, but to buy bonds with either significant credit or liquidity risk. Such bonds typically deliver very attractive long-term re-turns (the black line in figure 1 on the next page). However, as highlighted by the events of 2007-09, 2015 and 2020, investment grade corporate bonds, high yield bonds and emerging market bonds all carry significant drawdown risk.

To address these challenging market conditions, we developed the Global Fixed Income Opportunities strategy and are now introducing the EDGE – GFO fund following this strategy. We manage the portfolio risk according to the market and macro-outlook, shifting the allocation from a “Positive” portfolio of liquid high yield and emerging market bond ETF`s with shorter duration of 2-4 years to a “Defensive” allocation with only high-grade bond ETF`s and longer duration of 4-7 years (see figure 2 on the next page). To manage these shifts and help us divest from the risky part of the fixed income market when risk increases substantially, we ana-lyse the global macro development via a system of behavioural finance-driven machine learning algorithms. One such algorithm is shown in figure 2 below to the right. When the algorithm hits predefined thresholds, we change the allocation between the “Defensive”, “Balanced” and “Positive” portfolios illustrated on the left in figure 2. As seen in figure 1 we hold the

Key Facts - Flexible exposure to the full

spectrum of opportunities across

global fixed income markets.

- Active management of both

duration and credit risk in fixed

income portfolios, securing that

we only invest where there is the

best chance of getting a positive

return.

- A systematic investment process

with large asset allocation shifts

supported by machine learning

algorithms.

- Significant reduction in volatility

and maximum drawdown across

the business cycle.

- An investment process with the

freedom and opportunity to

capture the upside in credit

markets after recessions.

- Solid total returns and

outperformance across 3, 5 and

10-years and in years of credit

market sell-offs (2015, 2018 and

2020).

Launch date: 5th July 2021

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“Positive” portfolio most of the time. Only when both macro and market conditions dictate it, do we move into the “Balanced” or “Defensive” allocation. Avoiding losses and drawdowns associated with recessions and market selloffs delivers a more pleasant investment experience. This is illustrated by the simulation in Figure 3 showing smaller drawdowns for the GFO portfolio than for the correspond-ing “benchmark” portfolio consisting of a mix of high yield and high-grade bonds.

An early exit from the riskier part of the credit market can also set up a portfolio for a better return as the starting point for renewed investment into such high return assets as and when the recession even-tually ends. As seen in figure 1, the effect of this active management of the long-term return can be substantial and accumulates to significant gains in the invested capital over moderate time frames.

Figure 1: GFO Performance vs. Benchmark and vs. CHF Corporate bonds

Figure 2: GFO Allocation Levels and Composition

Figure 3: Simulated maximum Drawdown

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