Examining Recent Winners and Losers in the Nontraditional Bond Fund Category

The recent investment climate has been a challenging one for nontraditional bond funds, strategies that generally trade rate risk for credit risk. Confounding the chorus calling for the end of the 35-year bond bull market, U.S. Treasurys have rallied, credit spreads have widened and emerging market debt has faced heavy selling pressure. Due to market […]

August 31, 2015

The recent investment climate has been a challenging one for nontraditional bond funds, strategies that generally trade rate risk for credit risk. Confounding the chorus calling for the end of the 35-year bond bull market, U.S. Treasurys have rallied, credit spreads have widened and emerging market debt has faced heavy selling pressure. Due to market activity that has defied the “rate rise consensus”, combined with the wide dispersion of returns and behavior exhibited by nontraditional bond funds, understanding the differences in factor exposures between those leading the category from those lagging the pack provides important insights for investors.

These increasingly popular funds1 are more challenging to understand and analyze than core bond fund strategies with recognizable benchmarks. The products in the category, carrying labels such as unconstrained, absolute return, long/short, strategic income and flexible income, are generally able to “go-anywhere” across the fixed income spectrum – sector, duration (including negative) and geography – in a quest for income while limiting interest rate risk. As Reuters’ investments team recently highlighted, the hedge-fund-like mutual funds comprising the nontraditional bond fund category are difficult to analyze based on positions alone, which are reported with a lag. The funds make significant use of derivatives and futures that are not easily netted or understood. There is also some subjectivity from manager to manager in the way derivatives are disclosed and valued.

This all makes it difficult to understand how a given fund from this category may actual behave and, from a risk management perspective, how a placement with a nontraditional bond fund interacts with an investor’s greater fixed income allocation and portfolio as a whole. For investors performing due diligence on the category and/or reviewing their manager allocation(s), advanced quantitative analysis of these liquid alternatives provides valuable insight and can serve as the foundation for manager research, augmented by holdings-based and qualitative analysis.

These funds have been an area of keen interest for our clients, so we sought to gain insight on the differences exhibited by the best and worst performing nontraditional bond funds over the past years. To do so, we performed a returns-based style analysis (RBSA) on the top 5 and bottom 5 performing funds2, assessing market factors that best explain the groups’ averaged returns over the past two years3 and how they contributed to performance4.

To start, we note that the difference in annualized return between the average of the top 5 and average of the bottom 5 nontraditional bond funds is relatively high, 9.15% in the last two years, compared to intermediate-term bond funds, which showed an annualized difference of 6.36% over the period. Such a relatively wide range is not surprising in a diverse category lacking a common benchmark and is indicative of the care required when conducting manager research in the nontraditional universe. Using quantitative analysis, we then assessed the primary reasons for the wide dispersion in returns. From a returns-based performance attribution standpoint, three reasons account for the differences, factor return (also known as “style return”), market timing and securities selection. The below chart shows average annualized performance for the average of the top 5 and bottom 5 funds along with the returns-based performance attribution for each5.

Performance attribution

Our model shows the attribution from Average Factor Return – the return that comes from average bond sector exposures over the entire time period – contributed more than half the 6.22% annualized positive performance of the top 5, while for the bottom 5, which lost -2.93% annualized, it is basically flat. Timing return – the difference in return between the groups’ changing factor exposures and a static portfolio of the average exposures over the period – was the second largest contributor to performance of the top 5 and the second largest detractor for the bottom 5. Selection return – the portion of the groups’ return that we are unable to attribute to market exposures (i.e., betas) – represents the smallest positive return component for the top 5 funds, yet it was the largest detractor for the bottom 5.

Average Factor Exposures (Style): The below chart shows the average exposures to general fixed income factors of the top and bottom 5 funds over the recent two-year period.

Average Factor Exposures

As we see in the above chart, winning funds had several key differences from losing funds. First is the magnitude of short Treasury futures exposure (values below “0”); the short interest rate exposure amongst the bottom 5 funds is greater than double that displayed by the top 5. 10-year Treasury futures have returned 4.46% annually over the past two years (and nearly 1% in August through 8/21/15 alone). This cost the bottom 5 funds significantly, as seen in the chart below showing contribution of factors to the funds’ annualized returns. Our analysis indicates that the top 5 funds were also more in the market than the bottom 5, whose large cash exposure limited upside in the period6. Also, the top 5 showed a significant long exposure to bank loans and commercial mortgage-backed securities (CMBS), sectors that were not detected in our results of the bottom 5, which have returned 2.93% and 7.58% annualized, respectively.

Factor Contribution

Market Timing: To assess the attribution of market timing to the groups’ returns, we compared the performance of the funds’ exposures to a static portfolio of their average exposures over two years. The below chart displays the average factor exposures of the top and bottom 5 funds dynamically as they adjust in the model through the period.

Dynamic Factor Exposures

We see that the winning funds displayed effective leverage (exhibited by short cash exposure) from the beginning of the examined period into Q1 2014, allowing leveraged long exposure, particularly in credit segments, including bank loans and short duration high yield. We see that the modeled exposure to short and long duration high yield and bank loans is pared (and replaced by cash-like exposure) through the back half of 2014 to present, corresponding with the cooling of performance in those segments over the past year. The exposure to CMBS amongst the top funds has remained relatively constant. Illustrating timing errors (with the benefit of hindsight, of course), losing funds show a near doubling of short exposure to interest rate futures, persistent high cash-like exposure and sustained exposure to segments of the high yield sector.

The below chart shows quarterly market timing returns of the top and bottom 5 funds. Winning funds exhibited savvy timing in late 2013 and 2014. The apparent reduction in risk hurt the top performers in the first half of the year but looks to be rewarding them anew in the current quarter amidst heightened volatility and selling pressures.

Quarterly Timeing Performance

Securities Selection: High performers generated a positive selection return of 1.08% and low performers had a loss of -2.13%. In our analysis “securities selection” return refers to the portion of a fund’s return that we are unable to attribute to systematic factors (market exposures). In the case of nontraditional bond funds, selection return is typically thought to come from a few sources: (1) good selection of individual bonds that outperform the market index; (2) missing market factors from our analysis (that result in unexplained behavior); and 3) heavy use of derivatives or other exotic instruments by a fund. While quantitative models are unable to capture every nuance of such complex and non-linear exposures, they can still provide significant insight and accuracy over positions-based modeling, which is notoriously difficult, if not impossible, for most investors to conduct in such complex fixed income strategies.

Conclusion: Behaviorally, nontraditional bond funds remain a heterogeneous group. This is illustrated by the significant differences in the average factor exposures (style), market timing and selection attribution of the top and bottom performing funds of the category over the recent two-year period.

Well-timed fluctuations in credit, bank loan and currency exposure, and a relatively modest short Treasuries exposure were the most significant contributors to the performance of the top nontraditional bond funds. Contributing to the poor performance of the bottom 5 funds were the relatively aggressive and increasing short exposure to interest rate futures, poorly timed currency exposures, relatively low market exposure (particularly high yield) early in the period and a sustained exposure to credit through the recent pain. High performers also pared exposure to riskier segments prior to the recent volatility and selling. Selection return also aided top funds, while it proved a challenge, and the largest source of losses, for the lowest performing funds.

Investors should always be wary of the conventional market wisdom. Market consensus is not infallible and it is possible for new paradigms to arise in the bond market, especially during such historic central bank activity. Interest rate spikes have failed to materialize and – in light of the rising U.S. dollar and global impacts of slower growth in China – are still not guaranteed. The increasingly uncertain outlook is particularly relevant for nontraditional bond funds, a category that has vigorously gathered assets as investors of all stripes worried about the impact of the end of the 35-year bond bull market on their portfolios.

Investors monitoring and/or performing due diligence on nontraditional bond funds should seek to understand the exposures that have contributed to managers’ returns. This is especially so with regards to a manager’s outlook for rates, how that view is implemented in the portfolio, how it has contributed to the performance of a fund and what risks might be associated with the strategy. As such, investors should give extra care to the quantitative due diligence processes and ongoing monitoring they employ when analyzing sophisticated nontraditional bond strategies, as well as the role of such funds within their asset allocation framework and portfolio setting. Holdings alone are rarely sufficient to understand these products nor are they practical to pore through.


  • 1The category represents one of the great asset gathering stories of the post-Financial Crisis era. According to Morningstar, assets in the category rose from $3 billion in January 2009, to $103 billion in August ’13 and $151 billion currently.
  • 2Funds were equal weighted to better identify the consensus.
  • 3We use this short period because of 1. The anticipation of tightening monetary policy pervasive over from 2013’s Taper Tantrum through the current expectation that the Fed will raise rates in coming meetings, 2. The relatively short lives of many funds within the category and 3. To illustrate the nimbleness and wide investment mandate of funds in the category that necessitates such close monitoring.
  • 4DISCLAIMER: MPI conducts returns-based analyses and, beyond any public information, does not claim to know or insinuate what the actual strategy, positions or holdings of the funds discussed are, nor are we commenting on the quality or merits of the strategies. This analysis is purely returns-based and does not reflect insights into actual holdings. Deviations between our analysis and the actual holdings and/or management decisions made by funds are expected and inherent in any quantitative analysis. MPI makes no warranties or guarantees as to the accuracy of this statistical analysis, nor does it take any responsibility for investment decisions made by any parties based on this analysis.
  • 5The funds within the top and bottom groups are equal weighted to seek commonality in exposures and limit skewness towards the largest funds.
  • 6Cash may not always be interpreted literally; it may also represent either reduced volatility (in the case of estimated long positions) or excess volatility (in the case of estimated short positions) or else some other form of leverage.
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