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Summary­

  • In this commentary we explore tactical credit strategies that switch between high yield bonds and core fixed income exposures.
  • We find that short-term momentum signals generate statistically significant annualized excess returns.
  • We use a cross-section of statistically significant strategy parameterizations to generate an ensemble strategy.Consistent with past research, we find that this ensemble approach helps reduce idiosyncratic specification risk and dramatically increases the strategy’s information ratio above the median underlying strategy information ratio.
  • To gain a better understanding of the strategy, we attempt to determine the source of strategy returns. We find that a significant proportion of returns are generated as price returns occurring during periods when credit spreads are above their median value and are expanding.
  • Excluding the 2000-2003 and 2008-2009 sub-periods reduces gross-of-cost strategy returns from 2.9% to 1.5%, bringing into question how effective post-of-cost implementation can be if we do not necessarily expect another crisis period to unfold.

There is a certain class of strategies we get asked about quite frequently but have never written much on: tactical credit.

The signals driving these strategies can vary significantly (including momentum, valuation, carry, macro-economic, et cetera) and implementation can range from individual bonds to broad index exposure to credit default swaps.  The simplest approach we see, however, are high yield switching strategies.  The strategies typically allocate between high yield corporate bonds and core fixed income (or short-to-medium-term U.S. Treasuries) predominately based upon some sort of momentum-driven signal.

It is easy to see why this seemingly naïve approach has been attractive.  Implementing a simple rotation between –high-yield corporates– and –core U.S. fixed income– with a 3-month lookback with 1-month hold creates a fairly attractive looking –tactical credit– strategy.

Source: Tiingo.  Calculations by Newfound Research.   Tactical Credit strategy returns are hypothetical and backtested.  Returns gross of all management fees and taxes, but net of underlying fund fees.  HY Corporates represents the Vanguard High-Yield Corporate Fund (VWEHX).  Core Bonds is represented by the Vanguard Total. Bond Market Index Fund (VBMFX).  Returns assume the reinvestment of all distributions.

Visualizing the ratio of the equity curves over time, we see a return profile that is reminiscent of past writings on tactical and trend equity strategies. The tactical credit strategy tends to outperform core bonds during most periods, with the exception of periods of economic stress (e.g. 2000-2002 or 2008).  On the other hand, the tactical credit strategy tends to underperform high yield corporates in most environments, but has historically added significant value in those same periods of economic stress.

Source: Tiingo.  Calculations by Newfound Research.   Tactical Credit strategy returns are hypothetical and backtested.  Returns gross of all management fees and taxes, but net of underlying fund fees.  HY Corporates represents the Vanguard High-Yield Corporate Fund (VWEHX).  Core Bonds is represented by the Vanguard Total. Bond Market Index Fund (VBMFX).  Returns assume the reinvestment of all distributions.

This is akin to tactical equity strategies, which have historically out-performed the safety asset (e.g. cash) during periods of equity market tailwinds, but under-performed buy-and-hold equity during those periods due to switching costs and whipsaw. As the most aggressive stance the tactical credit strategy can take is a 100% position in high yield corporates, it would be unrealistic for us to expect such a strategy to out-perform in an environment that is conducive to strong high yield performance.

What makes this strategy different than tactical equity, however, is that the vast majority of total return in these asset classes comes from income rather than growth.  In fact, since the 1990s, the price return of high yield bonds has annualized at -0.8%.  This loss reflects defaults occurring within the portfolio offset by recovery rates.1

This is potentially problematic for a tactical strategy as it implies a significant potential opportunity cost of switching out of high yield.  However, we can also see that the price return is volatile.  In years like 2008, the price return was -27%, more than offsetting the 7%+ yield you would have achieved just holding the fund.

Source: Tiingo.  Calculations by Newfound Research.   Returns gross of all management fees and taxes, but net of underlying fund fees.

Like trend equity, we can think of this tactical credit strategy as being a combination of two portfolios:

  • A fixed-mix of 50% high yield corporates and 50% core bonds; and
  • 50% exposure to a dollar-neutral long/short portfolio that captures the tactical bet.

For example, when the tactical credit portfolio is 100% in high yield corporates, we can think of this as being a 50/50 strategy portfolio with a 50% overlay that is 100% long high yield corporates and 100% short core bonds, leading to a net exposure that is 100% long high yield corporates.

Thinking in this manner allows us to isolate the active returns of the portfolio actually being generated by the tactical signals and determine value-add beyond a diversified buy-and-hold core.  Thus, for the remainder of this commentary we will focus our exploration on the long/short component.

Before we go any further, we do want to address that a naïve comparison between high yield corporates and core fixed income may be plagued by changing composition in the underlying portfolios as well as unintended bets.  For example, without specifically duration matching the legs of the portfolio, it is likely that a dollar-neutral long/short portfolio will have residual interest rate exposure and will not represent an isolated credit bet.  Thus, naïve total return comparisons will capture both interest rate and credit-driven effects.

This is further complicated by the fact that sensitivity to these factors will change over time due both to the math of fixed income (e.g. interest rate sensitivity changing over time due to higher order effects like convexity) as well as changes in the underlying portfolio composition.  If we are not going to specifically measure and hedge out these unintended bets, we will likely want to rely on faster signals such that the bet our portfolio was attempting to capture is no longer reflected by the holdings.

We will begin by first evaluating the stability of our momentum signals.  We do this by varying formation period (i.e. lookback) and holding period of our momentum rotation strategy and calculating the corresponding t-statistic of the equity curve’s returns.  We plot the t-statistics below and specifically highlight those regions were t-statistics exceed 2, a common threshold for significance.

Source: Tiingo.  Calculations by Newfound Research.

It should be noted that data for this study only goes back to 1990, so achieving statistical significance is more difficult as the sample size is significantly reduced. Nevertheless, unlike trend equity which tends to exhibit strong significance across formation periods ranging 6-to-18 months, we see a much more limited region with tactical credit. Only formation periods from 3-to-5 months appear significant, and only with holding periods where the total period (formation plus holding period) is less than 6-months.

Note that our original choice of 63-day (approximately 3 months) formation and 21-day (approximately 1 month) hold falls within this region.

We can also see that very short formation and holding period combinations (e.g. less than one month) also appear significant.  This may be due to the design of our test.  To achieve the longest history for this study, we employed mutual funds.  However, mutual funds holding less liquid underlying securities tend to exhibit positive autocorrelation. While we adjusted realized volatility levels for this autocorrelation effect in an effort to create more realistic t-statistics, it is likely that positive results in this hyper short-term region emerge from this effect.

Finally, we can see another rather robust region representing the same formation period of 3-to-5 months, but a much longer holding length of 10-to-12 months.  For the remainder of this commentary, we’ll ignore this region, though it warrants further study.

Assuming formation and holding periods going to a daily granularity, the left-most region represents over 1,800 possible strategy combinations.  Without any particular reason for choosing one over another, we will embrace an ensemble approach, calculating the target weights for all possible combinations and averaging them together in a virtual portfolio-of-portfolios configuration.

Below we plot the long/short allocations as well as the equity curve for the ensemble long/short tactical credit strategy.

Source: Tiingo.  Calculations by Newfound Research.   Tactical Credit strategy returns are hypothetical and backtested.  Returns gross of all management fees and taxes, but net of underlying fund fees.  Returns assume the reinvestment of all distributions.

Note that each leg of the long/short portfolio does not necessarily equal 100% notional.  This reflects conflicting signals in the underlying portfolios, causing the ensemble strategy to reduce its gross allocation as a reflection of uncertainty.

As a quick aside, we do want to highlight how the performance of the ensemble compares to the performance of the underlying strategies.

Below we plot the annualized return, annualized volatility, maximum drawdown, and information ratio of all the underlying equity curves of the strategies that make up the ensemble.  We also identify the –ensemble approach–.  While we can see that the ensemble approach brings the annualized return in-line with the median annualized return, its annualized volatility is in the 14thpercentile and its maximum drawdown is in the 8thpercentile.

Source: Tiingo.  Calculations by Newfound Research.   Tactical Credit strategy returns are hypothetical and backtested.  Returns gross of all management fees and taxes, but net of underlying fund fees.  Returns assume the reinvestment of all distributions.

By maintaining the median annualized return and significantly reducing annualized volatility, the ensemble has an information ratio in the 78thpercentile.  As we’ve demonstrated in prior commentaries, by diversifying idiosyncratic specification risk, the ensemble approach is able to generate an information ratio significantly higher than the median without having to explicitly choose which specification we believe will necessarily outperform.

Given this ensemble implementation, we can now ask, “what is the driving force of strategy returns?”  In other words, does the strategy create returns by harvesting price return differences or through carry (yield) differences?

One simple way of evaluating this question is by evaluating the strategy’s sensitivity to changes in credit spreads.  Specifically, we can calculate daily changes in the ICE BofAML US High Yield Master II Option-Adjusted Spread and multiply it against the strategy’s exposure to high yield bonds on the prior day.

By accumulating these weighted changes over time, we can determine how much spread change the strategy has captured.  We can break this down further by isolating positive and negative change days and trying to figure out whether the strategy has benefited from avoiding spread expansion or from harvesting spread contraction.

In the graph below, we can see that the strategy harvested approximately 35,000 basis points (“bps”) from 12/1996 to present (the period for which credit spread data was available). Point-to-point, credit spreads actually widened by 100bps over the period, indicating that tactical changes were able to harvest significant changes in spreads.

Source: St. Louis Federal Reserve.  Calculations by Newfound Research.  

We can see that over the full period, the strategy predominately benefited from harvesting contracting spreads, as exposure to expanding spreads had a cumulative net zero impact.  This analysis is incredibly regime dependent, however, and we can see that periods like 2000-2003 and 2008 saw a large benefit from short-exposure in high yield during a period when spreads were expanding.

We can even see that in the case of post-2008, switching to long high yield exposure allowed the strategy to benefit from subsequent credit spread declines.

While this analysis provides some indication that the strategy benefits from harvesting credit spread changes, we can dig deeper by taking a regime-dependent view of performance. Specifically, we can look at strategy returns conditional upon whether spreads are above or below their long-term median, as well as whether they expand or contract in a given month.

Source: St. Louis Federal Reserve.  Calculations by Newfound Research.  Tactical Credit strategy returns are hypothetical and backtested.  Returns gross of all management fees and taxes, but net of underlying fund fees.  Returns assume the reinvestment of all distributions.

Most of the strategy return appears to occur during times when spreads are above their long-term median. Calculating regime-conditional annualized returns confirms this view.

Above

Below

Expanding

10.88%

-2.79%

Contracting

1.59%

4.22%

 

The strategy appears to perform best during periods when credit spreads are expanding above their long-term median level (e.g. crisis periods like 2008).  The strategy appears to do its worst when spreads are below their median and begin to expand, likely representing periods when the strategy is generally long high yield but has not had a chance to make a tactical switch.

This all points to the fact that the strategy harvests almost all of its returns in crisis periods.  In fact, if we remove 2000-2003 and 2008-2009, we can see that the captured credit spread declines dramatically.

Source: St. Louis Federal Reserve.  Calculations by Newfound Research.  

Capturing price returns due to changes in credit spreads are not responsible for all of the strategy’s returns, however.

Below we explicitly calculate the yield generated by the long/short strategy over time.  As high yield corporates tend to offer higher yields, when the strategy is net long high yield, the strategy’s yield is positive.  On the other hand, when the strategy is net short high yield, the strategy’s yield is negative.

This is consistent with our initial view about why these sorts of tactical strategies can be so difficult.  During the latter stages of the 2008 crisis, the long/short strategy had a net negative yield of close to -0.5% per month.2   Thus, the cost of carrying this tactical position is rather expensive and places a larger burden on the strategy accurately timing price return.

Source: Tiingo.  Calculations by Newfound Research.  Tactical Credit strategy returns are hypothetical and backtested.  Returns gross of all management fees and taxes, but net of underlying fund fees.

From this graph, we believe there are two interesting things worth calling out:

  • The long-run average yield is positive, representing the strategy’s ability to capture carry differences between high yield and core bonds.
  • In the post-crisis environments, the strategy generates yields in excess of one standard deviation of the full-period sample, indicating that the strategy may have benefited from allocating to high yield when yields were abnormally large.

To better determine whether capturing changes in credit spreads or carry differences had a larger impact on strategy returns, we can explicitly calculate the –price– and –total return– indices of the ensemble strategy.

Source: St. Louis Federal Reserve.  Calculations by Newfound Research.  Tactical Credit strategy returns are hypothetical and backtested.  Returns gross of all management fees and taxes, but net of underlying fund fees.  Total return series assumes the reinvestment of all distributions.

The –price return– and –total return– series return 2.1% and 2.9% annualized respectively, implying that capturing price return effects account for approximately 75% of the strategy’s total return.

This is potentially concerning, because we have seen that the majority of the price return comes from a single regime: when credit spreads are above their long-term median and expanding.  As we further saw, simply removing the 2000-2003 and 2008-2009 periods significantly reduced the strategy’s ability to harvest these credit spread changes.

While the strategy may appear to be supported by nearly 30-years of empirical evidence, in reality we have a situation where the vast majority of the strategy’s returns were generated in just two regimes.

If we remove 2000-2003 and 2008-2009 from the return series, however, we can see that the total return of the strategy only falls to 0.7% and. 1.6% annualized for –price return– and –total return– respectively.  While this may appear to be a precipitous decline, it indicates that there may be potential to capture both changes in credit spread and net carry differences even in normal market environments so long as implementation costs are kept low enough.

Source: Tiingo.  Calculations by Newfound Research.  Tactical Credit strategy returns are hypothetical and backtested.  Returns gross of all management fees and taxes, but net of underlying fund fees.  Total return series assumes the reinvestment of all distributions.

Conclusion

In this commentary, we explored a tactical credit strategy that switched between high yield corporate bonds and core fixed income.  We decompose these strategies into a 50% high yield / 50% core fixed income portfolio that is overlaid with 50% exposure to a dollar-neutral long/short strategy that captures the tactical tilts.  We focus our exploration on the dollar-neutral long/short portfolio, as it isolates the active bets of the strategy.

Using cross-sectional momentum, we found that short-term signals with formation periods ranging from 3-to-5 months were statistically significant, so long as the holding period was sufficiently short.

We used this information to construct an ensemble strategy made out of more than 1,800 underlying strategy specifications.  Consistent with past research, we found that the ensemble closely tracked the median annualized return of the underlying strategies, but had significantly lower volatility and maximum drawdown, leading to a higher information ratio.

We then attempted to deconstruct where the strategy generated its returns from.  We found that a significant proportion of total returns were achieved during periods when credit spreads were above their long-term median and expanding.  This is consistent with periods of economic volatility such as 2000-2003 and 2008-2009.

The strategy also benefited from harvesting net carry differences between high yield and core fixed income.  Explicitly calculating strategy price and total return, we find that this carry component accounts for approximately 25% of strategy returns.

The impact of the 2000-2003 and 2008-2009 periods on strategy returns should not be understated.   Removing these time periods reduced strategy returns from 2.9% to 1.6% annualized. Interestingly, however, the proportion of total return explained by net carry only increased from 25% to 50%, potentially indicating that the strategy was still able to harvest some opportunities in changing credit spreads.

For investors evaluating these types of strategies, cost will be an important component.  While environments like 2008 may lead to opportunities for significant out-performance, without them the strategy may offer anemic returns.  This is especially true when we recall that a long-only implementation only has 50% implicit exposure to the long/short strategy we evaluated in this piece.

Thus, the 2.9% annualized return is really closer to a 1.5% annualized excess return above the 50/50 portfolio.  For the ex-crisis periods, the number is closer to 0.8% annualized.  When we consider that this analysis was done without explicit consideration for management costs or trading costs and we have yet to apply an appropriate expectation haircut given the fact that this analysis was all backtested, there may not be sufficient juice to squeeze.

That said, we only evaluated a single signal in this piece.  Combining momentum with valuation, carry, or even macro-economic signals may lead to significantly better performance.  Further, high yield corporates is a space where empirical evidence suggests that security selection can make a large difference.  Careful selection of funds may lead to meaningfully better performance than just broad asset class exposure.

 


 

  1. Fixed income indices tend to have high levels of turnover, leading to other sources of gains and losses including shifts in the yield curve and credit spreads, realized roll in the yield curve or credit spreads, and other sources of additions and deletions such as bond callability.
  2. Note that this does not account for actual borrowing costs. However, as these tactical strategies are implemented as long only, our “short” is implicit, not explicit, and therefore borrowing costs are not relevant.

Corey is co-founder and Chief Investment Officer of Newfound Research, a quantitative asset manager offering a suite of separately managed accounts and mutual funds. At Newfound, Corey is responsible for portfolio management, investment research, strategy development, and communication of the firm's views to clients. Prior to offering asset management services, Newfound licensed research from the quantitative investment models developed by Corey. At peak, this research helped steer the tactical allocation decisions for upwards of $10bn. Corey holds a Master of Science in Computational Finance from Carnegie Mellon University and a Bachelor of Science in Computer Science, cum laude, from Cornell University. You can connect with Corey on LinkedIn or Twitter. Or schedule a time to connect.