A systematic approach to total return credit portfolios
The Global Fixed Income Opportunities portfolio (GFO) is a total return-focused fixed income fund targeting a return of 4-6% annually across the business cycle. To achieve these returns, the fund invests across the rating spectrum from US treasuries to high yield bonds and uses active management of duration exposure. To manage risk and mitigate the drawdowns associated with high yield and emerging market bonds, the fund applies large allocation changes managed via behavioural finance inspired machine leaning algorithms. This investment methodology also helps protect against duration driven selloffs, such as those seen in 2013, 2016 and which are likely to appear again when the economic recovery gains traction.
- Fund Launch Date: March 11th 2019
- Globally diversified exposure to the full credit spectrum of emerging and developed market bonds.
- Active management of both duration and credit risk in fixed income portfolios based on a systematic investment process supported by machine learning algorithms.
- Solid total returns and outperformance across 3, 5 and 10-years and in years of credit market sell-offs (2015, 2018).
- Significant reduction in volatility and maximum drawdown across the business cycle.
Central bank intervention across US and Europe has led to a global yield compression and most high-grade bonds now have yields below 3%, with most government bond yields below 1%. This leaves fixed income investors looking for yield above inflation no choice but to buy bonds with significant credit and liquidity risk. These bonds typically deliver very attractive long-term returns (the green line in figure 1 below). However, as highlighted by the events of 2015 and 2020, investment grade corporate bonds, high yield and emerging market bonds all pose significant risk in terms of liquidity and drawdowns.
To address these challenges, we manage the portfolio risk according to the market and macro outlook, shifting the allocation from a “Positive” portfolio of high yield and emerging market bonds with shorter duration of 3-4 years to a “Defensive” allocation with only high grade bonds and longer duration of 4-6 years (see figure 2 on the next page). To manage these shifts and secure that we leave the risky part of the fixed income market in time, we analyse the global macro development and financial market conditions via a system of behavioural finance-driven machine learning algorithms to decide when the portfolio allocation is changed.
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 on the previous page, we hold the “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 these losses and drawdowns associated with recessions and market sell-offs helps the investment experience as one does not have to be nervous during and after declines in the asset value. An early exit It also set up the portfolio for a better return as the starting point for renewed investment into high return assets. As seen in figure 1, the effect of this active management of the long-term return can be substantial amounting and accumulates to significant gains.