Throughout the summer, I have been performing 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 third event of the summer cannonballs into the deep end of the emerging market pool. If you are just joining us, it may be worth looking at my pieces on investment grade and high yield as a primer.
Taking the Emerging Market Plunge
On initial review, emerging markets (EM) should be simple to integrate into an attribution framework. The investible universe is a standard set of instrument types: vanilla government bonds, linkers, corporate bonds, money market instruments, etc. Sure, there are some local conventions in play, and in local currency debt, accessing terms and conditions can be an adventure depending on the market. But in general, there just aren’t that many surprises left.
So why do clients belly flop with attribution when it comes to emerging markets when it should be as disruptive as a pencil dive? Frankly, the main issue I see is confusion around what story to tell. This confusion stems primarily from combining hard currency with local currency across asset types (government, linker, corporate, etc.). Even just grouping or partitioning a simple contribution report lacks consistency for the same reasons.
Avoiding the Splash Zone
I posit that emerging market mandates are incredibly easy to run through either an absolute or benchmark relative attribution model. As in my previous posts, we will start with our basic attribution framework and combine or add effects based on the investment process. The differentiator here is in how we slice and dice the portfolio to most effectively tell the story.
Many clients initiate exposure to emerging markets through vanilla hard currency government bonds. Relative value to U.S. treasuries across countries is the play here, where underwriting is looking at spread levels, policy decisions, historical behavior, and macroeconomic indicators to determine creditworthiness (a.k.a. the “Four C’s of Credit” – character, capacity, conditions, capital).
The basic model is more than enough to handle this. However, we can expand the twist effect to incorporate partial durations or add in optional effects like spread, income, and carry if we’re so inclined. Applying a region or country grouping is standard practice, and it allows for some interesting visualization (Figure 1). That said, if we are being honest, there are more exciting strategies in the EM space to consider.
Figure 1. Geographical Heat Map of Contribution to Return
One of those strategies is incorporating hard or local currency corporate debt into the mix. When this occurs, we must rethink the entire approach. Depending on the turnover of the portfolio (buy and hold versus actively managed), the addition of the carry effect, income, and spread effect become more meaningful. If turnover is high, we may even want to try and capture the impact of our trades via the transaction effect. The complexity increases here when multiple currencies enter the fray.
The initial challenge revolves around how to group this type of report. This is where I see divergence across clients. Typically, country, sector, and currency are seen in some order, with a rating, spread, or coupon partition occurring at the most granular level (Figure 2). The difficulty is often purely data related – are country and sector defined internally or via the benchmark? How do you align the portfolio with the benchmark if these definitions are internal? How do you know that you are using analytics calculated off the appropriate local curve when multiple currencies are in play?
Figure 2. Basic Fixed Income Attribution Model with Currency Effect
With meta data like country and sector, it is common to use attempt to align the portfolio with the benchmark, either via mapping or further upstream in a data warehouse. Currency, rating, spread, and coupon are easily accounted for. In terms of analytics and attribution, automating the analytics generation so that curve selection occurs automatically is ideal. Beyond the previously discussed effects, an attribution model that is designed to capture local effects as well as a currency effect becomes a must-have.
What starts out as a complex problem is easily resolved, so it is easy to maintain some semblance of normalcy in the work flow while continuing to leverage out-of-the-box functionality.
The Currency Lazy River
The other aspect that we need to think about as the product mix expands in emerging market strategies is how to handle currency. Is currency risk “naïve” in the sense that it is merely derived from the holdings? Or is it hedged entirely back to the portfolio base currency? Is some level of exposure acceptable based on the mandate or investor policy statement? Is currency exposure treated as a return-seeking asset?
These are all instrumental questions in determining the format and output of the analysis. Where currency risk is hedged out completely, it may tell a more effective story to group currency forwards within the asset currency buckets to determine hedge effectiveness. Alternatively, where active currency risk is taken, maintaining the flexibility to analyze the contribution of currency forwards independent of the cash assets is paramount.
The lazy river comparison fits here because regardless of your approach to currency, it is possible to implement the desired view to complete the story seamlessly, allowing you float on from emerging markets towards our next subject, liability-driven investments (LDI).
Emerging markets are easy to analyze in a fixed income attribution framework using readily available methodologies. Clients tend to struggle with telling an effective story in this space, however, this challenge can be solved using an adaptable approach in visualization, slicing and dicing, and optimizing the effects in use.
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 earned 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.