Let’s frame this at a high level first: What is performance attribution? Performance attribution is an integral part of the investment process that helps to close the feedback loop by explaining the drivers of benchmark relative over- or under-performance.
Drilling down a bit: Why is performance attribution important? This is not a trick question. Attribution analysis is used in several ways across firms and their clients. Portfolio managers use it in support/defense of their investment thesis. Sales and Marketing teams use it in asset gathering and retention. Clients rely on it to determine how their investment selections are subscribing to given mandates and how they are performing against passive alternatives. These are just the common use cases.
Diving in even deeper: How should I use performance attribution for (insert asset class here)? This brings us to the crux of the matter. While attribution has a definite science behind it, it is also an art. One size does not fit all!
With equity portfolios most us are happy to run with a standard Brinson approach, where we view the performance return as the combined result of changes in the values of the portfolio’s assets, grouped by sector, country, or some fundamental value (e.g., market cap, P/E quintile). We are happy to explain the portfolio’s performance in terms of whether we were overweight or underweight each group or partition relative to the benchmark, and in terms of whether each group performed better in the portfolio or benchmark. This is intuitive and easy to understand.
Fixed income tends to be a different beast though, where taking the same approach as we do with equities doesn’t work. This is because the drivers of total return for fixed income instruments are more complex than in the equity space. Fixed income attribution has to go further than identifying how much asset values change over time; it should try to identify the reasons for these changes.
While fixed income attribution has evolved dramatically over the past decade or so, two commonly held perceptions remain: 1) it’s hard to understand and 2) it’s hard to implement. These perceptions are often cited to explain why fixed income attribution is a less successful and under-used technique relative to equity attribution. Let’s address each of these in turn.
It’s Hard to Understand
There is some truth to this, but hear me out as to why I believe this is becoming less of an issue. When I first got started with fixed income attribution, there was no generally accepted model or approach as is seen with equities. Fast forward to today. While nuances across providers persist, a general approach of looking at rate and curve exposure plus excess return has become a common starting point. Empirically it is accepted that the level of duration and key rate exposure typically represent the lion’s share of a fixed income instrument’s total return.
Once we account for this, the excess return becomes an exercise in matching the attribution model components with the investment process. There is still not a “one size fits all” approach across providers or the industry, but I would posit that this demand for flexibility allows for more meaningful communication around the investment process and corresponding results.
It’s important to not overcomplicate the analysis or make the overriding assumption that every effect is necessary. In the case of the latter, take the application of inflation effects to a portfolio of nominal bonds.
We can also simplify the vocabulary to make it easier to understand. For example, instead of talking about rolldown effect, let’s talk about how much of the change in the prices of the portfolio’s bonds resulted from the fact that as bonds get closer to the date on which they will be repaid, their market price gets closer to the amount that will be repaid. Over a calendar quarter, the bonds in a portfolio move three months closer to their maturity date and so their market prices will move closer to the redemption amounts. This will contribute to the portfolio’s performance over the measurement period. Conceptually that is much easier to understand than talking about a rolldown effect.
By aligning the attribution effects used with the investment process, we create an opportunity to facilitate a more compelling story between all parties in the investment process.
It’s Hard to Implement
I think that this is a bit of a misrepresentation. The models should be readily applicable out of the box. The difficulty comes in tying together the requirements of broad asset types for use in a scalable solution. Holdings, transactions, analytics, and data management all present potential landmines, but it doesn’t have to be this way.
The difficulty arises from two main factors. First, accessing the raw data inputs surprisingly remains a challenge across firms of all sizes. Applying expertise in data integration across asset types is critical, especially in unconstrained and/or global mandates. If you can’t get a correct format for path dependent securities, like RMBS, CMBS, and ABS, or if you misrepresent the price of a local conventional or inflation-linked security, downstream analytics, returns, and attributions will be incorrect. OTC derivatives increase the complexity here due to inconsistencies across inputs (i.e. , Firm A may store security master data for an interest rate swap in a completely different fashion than Firm B, but both can be correct).
Second, fixed income attribution models rely on daily analytics to generate meaningful output. As data quality issues arise, the selected attribution model may be prone to distortion via blown out effects that occur on a single day for a single security. Leveraging technology to proactively identify and correct security level inputs paves the way for a smoother implementation and interpretation. The level of integrity in the data input to fixed income attribution systems has to be aligned with the level of complexity of the specific attribution model that is being implemented.
It is when these factors are not taken into consideration during implementation planning for fixed income attribution that the risk of project failure is highest.
It’s Time for a Fresh Look at Fixed Income Attribution
Increased client sophistication, an evolving investment process, and the creation/inclusion of additional asset types/mandates are all drivers for an increased demand of fixed income attribution. It’s certainly not going the way of the dodo bird. Given that, it is imperative that we pull back the curtain and show that fixed income attribution is explainable, implementable, and deserves a place in your portfolio lifecycle toolkit.
Mr. Pat Reilly is Senior Vice President, Senior Director of FactSet’s Analytics solutions for the Americas. In this role, he focuses on providing content, analytics, and attribution solutions to clients across equities, fixed income, and multi-asset class strategies. Prior to this role, Mr. Reilly headed the Fixed Income Analytics team in EMEA and began his career at FactSet managing the Analytics sales for the Western United States and Canada. Before joining FactSet, he was a Credit Manager at Wells Fargo and an Insurance Services Analyst at Pacific Life. Mr. Reilly earned a degree in Finance from the University of Arizona and an MBA from the University of Southern California and is a CFA charterholder.
The information contained in this article is not investment advice. FactSet does not endorse or recommend any investments and assumes no liability for any consequence relating directly or indirectly to any action or inaction taken based on the information contained in this article.