Explore Aviva Investors’ views on how investors can unlock additional sources of returns other managers might miss.
- We believe investment-grade managers can effectively design a portfolio built to weather all types of market environments if they successfully overcome certain behavioural blind spots. Some of the common biases lead to managers maintaining an overexposure to riskier segments of the market and anchoring a portfolio to an inefficient benchmark.
- It is our view that there are structural inefficiencies in the investment-grade credit market that can be exploited. It is not beneficial for managers to slavishly follow the benchmark or to endeavour to reduce tracking error at all costs.
- Many fixed-income managers overlook the fact that the portfolio construction process can be a unique source of alpha. Rethinking traditional sector classifications, managing to a volatility target, and embedding downside-risk constraints can be helpful when it comes to enhancing returns.
This paper will examine common behavioural blind spots that could hamper a manager’s ability to outperform consistently. We will also present ideas to help investors select managers who are better positioned to achieve strong risk-adjusted returns in all phases of the market cycle.
Don’t let benchmarking limit your potential
We consider tracking error, the deviation from a portfolio’s benchmark, as a misused accepted risk metric that investors use for risk management. Deviations from the benchmark is seen as taking on active risk or constituting higher tracking error. However, what if the benchmark has built-in inefficiencies? In our view, managers should be aware of tracking error, but they should not use it as a goalpost for evaluating risk. Simply matching the benchmark leaves growth opportunities on the board. We believe there are good reasons why deviations from the benchmark should be embraced and managers should not anchor themselves to a notoriously flawed index.
Aiming for the lowest tracking error can be a poor practice
For example, aiming for the lowest tracking error can be a poor practice when managers own securities they do not particularly favour solely because the issue has a significant weight in the index. Not owning such a security would create higher tracking error in the portfolio, so in order to reduce tracking error a manager might instead choose to hold a smaller allocation of it. In this situation, we feel it is undesirable to minimize tracking error by way of following the benchmark. Managers should not feel obligated to invest in issues in which they have very little conviction.
In our view, a better portfolio outcome would be allowing managers the flexibility to avoid disfavoured securities or even entire sectors; to increase tracking error; and to reallocate that capital to other investments in which the managers have higher conviction with a similar level of risk. In this case, we believe that adding tracking error is warranted because it could potentially improve portfolio performance. In our investment-grade portfolios, we allow our portfolio managers the flexibility to implement their best ideas, even if that means they are taking on additional tracking error.
As an example, during our portfolio construction process we prefer to focus our attention on each investment’s impact on the portfolio’s total volatility, and on downside risks, rather than to allow tracking error to play an outsize role. We believe each security’s risk contribution to the portfolio’s total volatility is just as relevant as its return potential. If we ignore this relationship, we may overburden our portfolios with riskier assets that may not pay off. Our best ideas are usually the bonds that give us the maximum returns for given levels of volatility while also satisfying downside-risk constraints. In other words, we accept deviations from the benchmark if that means we can effectively reduce risks or enhance returns while maintaining portfolio volatility and beta that are similar to those of the index.
Don’t follow the herd
The desire to outperform has driven many credit analysts to follow the crowd and search for yields toward the lower end of the investment-grade quality range. These analysts are also often overly optimistic about their ability to forecast returns, thus further reinforcing their herd-like behaviour into the riskier, higher-yielding BBB issues. This is evident in sell-side analysts’ exuberance toward riskier assets. In Figure 1, our analysis shows sell-side analysts have issued nearly six times as many “outperform” recommendations for lower-quality BBB issuers as they have for single-A and above.
Most credit portfolios are over-risked
Given the popularity of these “outperform” recommendations, it is not surprising that most credit portfolios are over-risked. While this approach can be profitable at most points in the investment cycle, it can also backfire when spreads widen rapidly, liquidity dries up, and managers find themselves in a difficult position in terms of unwinding their underperforming BBB bonds. During periods of market stress, managers with an over-risked portfolio tend to greatly underperform their benchmark and experience more severe drawdowns and higher portfolio volatility. In comparison, managers without a reliance on credit beta to drive alpha may be in a better position to produce excess returns that have low correlation to the direction of spread movements.
Figure 1. “Outperform” recommendations on BBBs compared with those issued for higher-quality bonds
The reality is that no one has a crystal ball. There is plenty of credit-spread volatility throughout a given investment cycle, and it is challenging to forecast the change in spread movements accurately. So how can credit managers avoid this bias? In our view, portfolio managers need to have the processes and tools that help establish the relative value of owning lower-risk versus higher-risk bonds. At Aviva Investors, our portfolio construction process carries this out in multiple ways.
Most credit managers have a portfolio construction process that seeks to achieve a targeted active-risk profile. However, many managers do not design their processes to explicitly control for portfolio volatility that is similar to that of the index, or to protect on the downside during market downturns. We believe in using a wide-angle lens to fully capture and evaluate the collective risks inside a portfolio versus letting unintended risk exposures stay hidden. Once risk exposures are uncovered, we leverage our portfolio construction process to unlock additional sources of potential alpha generation. Below are a couple of ways we do this.
- Proprietary risk-allocation framework — As seen in Figure 2 below, we break away from benchmark defined sectors by reclassifying sectors based on risk characteristics. Building these custom sectors allows our portfolio managers to have a more transparent view of risk regardless of the traditional sector that it resides. These custom sectors further enable our managers to allocate efficiently the sectors that offer the best return for a given level of volatility.
- Scenario Analysis — Another key tenet of our portfolio construction process is understanding our portfolios' resilience under different market environments, especially when it comes to those characterized by high market stress. We do this by conducting a robust scenario analysis of the changes in key factors (i.e., changes in interest rates and credit-spread conditions) that can have a meaningful impact on portfolio returns. Our goal is to achieve a positive excess return in all scenarios.
Figure 2. Custom sectors are at the heart of our risk-allocation process
Integrating our proprietary risk-allocation framework and scenario analyses into the portfolio construction process helps us build portfolios that can weather all types of market conditions. By targeting levels of total risk and beta that are similar to those of the benchmark, and embedding downside-risk constraints to avoid adverse outcomes, we strive to provide a smoother path of returns for clients.
As seen in Figure 3, we have consistently outperformed the benchmark, independent of the direction of spread movements. Our US Long Government Credit Strategy, for example, continued to deliver excess returns even when credit spreads increased from April 2014 through April 2016, in late 2018, and again in early 2020. Moreover, the portfolio was still able to deliver positive excess returns during a credit-spread-tightening environment from April 2016 through April 2018. As a full-cycle manager, we have delivered alpha with a low or negative correlation with that of our peers and provided strong downside protection.
Figure 3. Seeks consistent and repeatable excess returns in all market conditions
In summary, our benchmark unconstrained, volatility focused portfolio construction process helps us perform well in various credit cycles. In Figure 4 below, we demonstrate our strategy’s performance versus that of our largest competitors during both spread-widening and spread-tightening situations. We calculate a “capture ratio” for spread-widening and tightening periods, as defined below, to evaluate how well each manager has performed against the benchmark. We believe this metric allows us to tease out credit risk taken by investment-grade credit managers. Additionally, we calculate a third metric called the “smart capture ratio,” which shows us the relationship between a manager’s widening- and tightening-capture ratios.
- Widening Capture Ratio = average return of the manager / average return of the benchmark during periods when the change in OAS > 0.
- Tightening Capture Ratio = average return of the manager / average return of the benchmark during periods when the change in OAS <0.
- Smart Capture = Tightening Capture Ratio X Widening Capture Ratio.
Figure 4 shows that our US Long Credit Strategy's monthly returns performed 204% better than the Barclays US Long Credit Index during a spread-widening environment over the past nine years. More specifically, from January 2012 through the end of June 2021 — when credit spreads widened — the average monthly return of the Barclays US Credit Index was 0.21% whereas our strategy’s average monthly return was nearly twice that at 0.40%, resulting in a widening-capture ratio of 1.8x. In contrast, when spreads are tightening, we perform in similar fashion to the index with a tightening-capture ratio close to 97%. It is worth noting that portfolio managers will have a smart capture ratio greater than 1 when their performance in spread-tightening situations more than compensates for their down-market performance as spreads widen, or vice versa.
Figure 4. Performance capture in credit spread tightening and widening markets
Behavioural biases in credit research and portfolio construction, and misconceptions in risk analysis are challenges faced by most fixed-income managers. To overcome these obstacles, managers need to be cognizant of these blind spots and be deliberate in designing tools and processes that help turn these impediments into advantages. It is our view that managers can successfully deliver persistent excess returns over a full investment cycle and enjoy strong downside protection if they harness the portfolio construction process as another source of alpha, avoid common behavioural biases, and think about portfolio risk in a multidimensional way.