Baseball games, barbecues, and beach parties—these are a few of my favorite things! As we enter the heart of summer, with vacation time wanting, sunny afternoons beckoning, and all of us looking for a welcome distraction, I am thrilled to introduce you to the real summer blockbuster (Jurassic World eat your heart out)—The Summer of Attribution!
Over the remainder of the summer, I will perform a deep dive into the intricacies of fixed income attribution across asset types with a simple goal in mind. By breaking out commonly used effects, sample reports, and tools for interpretation, we can improve the quality of our attribution analysis, regardless of the investment mandate.
The journey starts today, where we will analyze that most common of fixed income strategies: Core Investment Grade. Future posts will address High Yield, Emerging Market, Multi-Asset, and Liability-Driven Investment (LDI) strategies.
Fishing for Investment Grade Basis Points
So, what is a core investment grade bond strategy anyway? Almost without fail, our clients define this as an attempt to outperform some broad industry benchmark while seeking current income and capital appreciation via primarily investment-grade securities. Typically, duration tuning, sector allocation, and credit selection are used in these mandates.
This sounds simple enough. Yet in practice we can find thousands of funds globally that fit the bill (the eVestment US Core Fixed Income Universe contains 505 alone). Surely not all of these products are all managed the same way! With that in mind, how do we most effectively tell the portfolio management story for these assets?
Recall our starting point in figure 1, where we break total return into yield curve and excess return components:
This allows us to roughly gauge the impact of our duration, sector allocation, and security selection decisions. However, given the intricacies of portfolio construction and the sheer number of competing funds out there, we must adapt this model to fit our investment process to properly convey the value add. Specifically, we want to enhance the story told by our interest rate exposure and winnow down the excess return through the addition of a few optional effects.
Yield Curve Effect = Shift Effect + Twist Effect
Rates go up, bond prices go down. It’s the fixed income equivalent of “buy low, sell high,” a cornerstone of how the asset class works. And yet, relative to a benchmark, it tells us very little about our investment process. Sure, it is informative. A portfolio with an effective duration of five is going to be less impacted by rising rates than a benchmark with an effective duration of six. The impact of a parallel interest rate move given the portfolio and benchmark sensitivities is a back-of-the-envelope calculation. That said, this initial view ignores portfolio construction techniques like bond laddering or a barbell approach, as well as the observation that yield curves don’t only move in a parallel fashion.
The adjustment that we must consider is how to reflect this real-world view in the attribution model. This can be accomplished in two ways. We can adjust the twist point based on the mandate or we can expose key rate durations. Adjusting the twist point can make sense if we are running a variation of a core strategy (e.g. short or long duration) or seek to maintain a duration neutral approach (where the twist point becomes the benchmark’s effective duration). Exposing key rate durations makes sense when the investment process considers relative attractiveness across the curve. For example, say that we dislike short rates because of Fed activity; however, we are neutral the long end and are bullish on the belly of the curve. This allows us to more granularly identify how the portfolio’s interest rate exposure differs from the benchmark—it’s a more effective story.
Expanding the Model
The other half of our model is a residual consisting of allocation and selection. While there is a place for this in our analysis, I find that it is lacking that explanatory “wow” factor. If you refer to the initial post, you’ll recall that we have a host of optional attribution effects that can be added to tell the story with greater impact. The power of a model is not the sheer number of effects, but rather how easily the relevant effects can be incorporated into the analysis and the detail that they provide.
In my opinion, adding carry, spread, and income provide for that extra explanatory power in the core investment grade space. By incorporating the carry effect, we get a sense of how the passage of time has helped or hurt the portfolio’s relative performance. Carry is made up of accretion and roll down components. In terms of accretion, what we mean is how any discounts or premiums on a security impact the price return over the measurement period. This is purely an accounting construct derived from the purchase of a security. By roll down, we refer to the price impact of a security moving X periods closer to maturity or literally rolling down the curve. This is also an accounting construct. Both carry components are most meaningful in a low turnover environment.
The addition of the spread effect details our spread management ability. Did we see greater spread movement relative to the benchmark, where spread is defined here using the Option Adjusted Spread for comparability across asset types? Or were we taking greater spread risk, represented here by spread duration? This addresses the capital appreciation aspect of the mandate and is a natural complement to any rich-cheap or relative value analysis that is being performed. We can also more clearly define the impact of a sector versus a security by expanding the spread effect to a sector spread and security spread component.
The final effect we want to add, income effect, is also the easiest to understand. Did the portfolio clip a higher coupon than the benchmark? If so, we would expect a positive income effect. Since accruals and coupons are observable, it is natural to break this out explicitly from any residual return.
The model wraps up with inclusion of the allocation and selection effects. Here allocation is a function of the groups/partitions we choose to apply, while selection is a function of underwriting/avoidance.
When all is said and done, we should have dramatically enhanced the quality of our analysis of core investment grade over- or underperformance. In the next post, we will shift focus to another popular mandate, high yield. Don’t forget your sunscreen!
Vice President, Sales Manager, Fixed Income, FI Analytics
Pat Reilly has 15 years of experience within the investment management industry focusing on fixed income. Prior to joining FactSet, Pat started his professional path as a Credit Manager at Wells Fargo and then as an Investment Services Analyst at Pacific Life. He holds a Bachelor of Science, Finance from the Eller College of Management at the University of Arizona and a Master of Business Administration from the Marshall School of Business at the University of Southern California.