The Research Library of Newfound Research

Category: Term

Yield Curve Trades with Trend and Momentum

This post is available as a PDF download here.

Summary­

  • Yield curve changes over time can be decomposed into Level, Slope, and Curvature changes, and these changes can be used to construct portfolios.
  • Market shocks, monetary policy, and preferences of different segments of investors (e,g. pensions) may create trends within these portfolios that can be exploited with absolute and relative momentum.
  • In this commentary, we investigate these two factors in long/short and long/flat implementations and find evidence of success with some structural caveats.
  • Despite this, we believe the results have potential applications as either a portable beta overlay or for investors who are simply trying to figure out how to position their duration exposure.
  • Translating these quantitative signals into a forecast about yield-curve behavior may allow investors to better position their fixed income portfolios.

It has been well established in fixed income literature that changes to the U.S. Treasury yield curve can be broken down into three primary components: a level shift, a slope change, and a curvature twist.

A level change occurs when rates increase or decrease across the entire curve at once.  A slope change occurs when short-term rates decrease (increase) while long-term rates increase (decrease).  Curvature defines convexity and concavity changes to the yield curve, capturing the bowing that occurs towards the belly of the curve.

Obviously these three components do not capture 100% of changes in the yield curve, but they do capture a significant portion of them. From 1962-2019 they explain 99.5% of the variance in daily yield curve changes.

We can even decompose longer-term changes in the yield curve into these three components.  For example, consider how the yield curve has changed in the three years from 6/30/2016 to 6/30/2019.

Source: Federal Reserve of St. Louis.

We can see that there was generally a positive increase across the entire curve (i.e. a positive level shift), the front end of the curve increased more rapidly (i.e. a flattening slope change) and the curve flipped from concave to convex (i.e. an inverted bowing of the curve).

Using the historical yield curve changes, we can mathematically estimate these stylized changes using principal component analysis.  We plot the loadings of the first three components below for this three-year change.

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

We can see that –PC1– has generally positive loadings across the entire curve, and therefore captures our level shift component.  –PC2– exhibits negative loadings on the front end of the curve and positive loadings on the back, capturing our slope change.  Finally, –PC3– has positive loadings from the 1-to-5-year part of the curve, capturing the curvature change of the yield curve itself.

Using a quick bit of linear algebra, we can find the combination of these three factors that closely matches the change in the curve from 6/30/2016 to 6/30/2019.  Comparing our model versus the actual change, we see a reasonably strong fit.

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

So why might this be useful information?

First of all, we can interpret our principal components as if they are portfolios.  For example, our first principal component is saying, “buy a portfolio that is long interest rates across the entire curve.”  The second component, on the other hand, is better expressed as, “go short rates on the front end of the curve and go long rates on the back end.”

Therefore, insofar as we believe changes to the yield curve may exhibit absolute or relative momentum, we may be able to exploit this momentum by constructing a portfolio that profits from it.

As a more concrete example, if we believe that the yield curve will generally steepen over the next several years, we might buy 2-year U.S. Treasury futures and short 10-year U.S. Treasury futures.  The biggest wrinkle we need to deal with is the fact that 2-year U.S. Treasury futures will exhibit very different sensitivity to rate changes than 10-year U.S. Treasury futures, and therefore we must take care to duration-adjust our positions.

Why might such changes exhibit trends or relative momentum?

  • During periods where arbitrage capital is low, trends may emerge. We might expect this during periods of extreme market shock (e.g. recessions) where we might also see the simultaneous influence of monetary policy.
  • Effects from monetary policy may exhibit autocorrelation. If investors exhibit any anchoring to prior beliefs, they might discount future policy changes.
  • Segmented market theory suggests that different investors tend to access different parts of the curve (e.g. pensions may prefer the far end of the curve for liability hedging purposes). Information flow may therefore be segmented, or even impacted by structural buyers/sellers, creating autocorrelation in curve dynamics.

In related literature, Fan et al (2019) find that the net hedging or speculative position has strong cross-sectional explanatory power for agricultural and currency futures returns, but not in fixed income markets.  To quote,

“In sharp contrast, we find no evidence of a significant speculative pressure premium in the interest rate and fixed income futures markets. Thus, albeit from the lens of different research questions, our paper reaffirms Bessembinder (1992) and Moskowitz et al. (2012) in establishing that fixed income futures markets behave differently from other futures markets as regards the information content of the net positions of hedgers or speculators.  A hedgers-to-speculators risk transfer in fixed income futures markets would be obscured if agents choose to hedge their interest rate risk with other strategies (i.e. immunization, temporary change in modified duration).”

Interestingly, Markowitz et al. (2012) suggest that speculators may be profiting from time-series momentum at the expense of hedgers, suggesting that they earn a premium for providing liquidity.  Such does not appear to be the case for fixed income futures, however.

As far as we are aware, it has not yet been tested in the literature whether the net speculator versus hedger position has been tested for yield curve trades, and it may be possible that a risk transfer does not exist at the individual maturity basis, but rather exists for speculators willing to bear level, slope, or curvature risk.

Stylized Component Trades

While we know the exact loadings of our principal components (i.e. which maturities make up the principal portfolios), to avoid the risk of overfitting our study we will capture level, slope, and curvature changes with three different stylized portfolios.

To implement our portfolios, we will buy a basket of 2-, 5-, and 10-year U.S. Treasury futures contracts (“UST futures”).  We will assume that the 5-year contract has 2.5x the duration of the 2-year contract and the 10-year contract has 5x the duration of the 2-year contract.

To capture a level shift in the curve, we will go long across all the contracts.  Specifically, for every dollar of 2-year UST futures exposure we purchase, we will buy $0.4 of 5-year UST futures and $0.20 of 10-year UST futures.  This creates equal duration exposure across the entire curve.

To capture slope change, we will go short 2-year UST futures and long the 10-year UST futures, holding zero position in the 5-year UST futures.  As before, we will duration-adjust our positions such that for each $1 short of the 2-year UST futures position, we are $0.20 long the 10-year UST futures.

Finally, to capture curvature change we will construct a butterfly trade where we short the 2- and 10-year UST futures and go long the 5-year UST futures.  For each $1 long in the 5-year UST futures, we will short $1.25 of 2-year UST futures and $0.25 of 10-year UST futures.

Note that the slope and curvature portfolios are implemented such that they are duration neutral (based upon our duration assumptions) so a level shift in the curve will generate no profit or loss.

An immediate problem with our approach arises when we actually construct these portfolios.  Unless adjusted, the volatility exhibited across these trades will be meaningfully different.  Therefore, we target a constant 10% volatility for all three portfolios by adjusting the notional exposure of each portfolio based upon an exponentially-weighted estimate of prior 3-month realized volatility.

Source: Stevens Futures.  Calculations by Newfound Research.  Past performance is not an indicator of future results.  Performance is backtested and hypothetical.  Performance figures are gross of all fees, including, but not limited to, manager fees, transaction costs, and taxes.  Performance assumes the reinvestment of all distributions.

It appears, at least to the naked eye, that changes in the yield curve – and therefore the returns of these portfolios – may indeed exhibit positive autocorrelation.  For example, –Slope– appears to exhibit significant trends from 2000-2004, 2004-to 2007, and 2007-2012.

Whether those trends can be identified and exploited is another matter entirely.  Thus, with our stylized portfolios in hand, we can begin testing.

Trend Signals

We begin our analysis by exploring the application of time-series momentum signals across all three of the portfolios.  We evaluate lookback horizons ranging from 21-to-294 trading days (or, approximately 1-to-14 months).  Portfolios assume a 21-trading-day holding period and are implemented using 21 overlapping portfolios to control for timing luck.

Source: Stevens Futures.  Calculations by Newfound Research.  Past performance is not an indicator of future results.  Performance is backtested and hypothetical.  Performance figures are gross of all fees, including, but not limited to, manager fees, transaction costs, and taxes.  Performance assumes the reinvestment of all distributions.

Some observations:

  • Time-series momentum appears to generate positive returns for the Level portfolio. Over the period tested, longer-term measures (e.g. 8-to-14-month horizons) offer more favorable results.
  • Time-series momentum on the Level portfolio does, however, underperform naïve buy-and-hold. The returns of the strategy also do not offer a materially improved Sharpe ratio or drawdown profile.
  • Time-series momentum also appears to capture trends in the Slope portfolio. Interestingly, both short- and long-term lookbacks are less favorable over the testing period than intermediate-term (e.g. 4-to-8 month) ones.
  • Finally, time-series momentum appeared to offer no edge in timing curvature trades.

Here we should pause to acknowledge that we are blindly throwing strategies at data without much forethought.  If we consider, however, that we might reasonably expect duration to be a positively compensated risk premium, as well as the fact that we would expect the futures to capture a generally positive roll premium (due to a generally upward sloping yield curve), then explicitly shorting duration risk may not be a keen idea.

In other words, it may make more sense to implement our level trade as a long/flat rather than a long/short.  When implemented in this fashion, we see that the annualized return versus buy-and-hold is much more closely maintained while volatility and maximum drawdown are significantly reduced.

Source: Stevens Futures.  Calculations by Newfound Research.  Past performance is not an indicator of future results.  Performance is backtested and hypothetical.  Performance figures are gross of all fees, including, but not limited to, manager fees, transaction costs, and taxes.  Performance assumes the reinvestment of all distributions.

Taken together, it would appear that time-series momentum may be effective for trading the persistence in Level and Slope changes, though not in Curvature.

Momentum Signals

If we treat each stylized portfolio as a separate asset, we can also consider the returns of a cross-sectional momentum portfolio.  For example, each month we can rank the portfolios based upon their prior returns.  The top-ranking portfolio is held long; the 2nd ranked portfolio is held flat; and the 3rd ranked portfolio is held short.

As before, we will evaluate lookback horizons ranging from 21-to-294 trading days (approximately 1-to-14 months) and assuming a 21-trading-day holding period, implemented with 21 overlapping portfolios.

Results – as well as example allocations from the 7-month lookback portfolio – are plotted below.

Source: Stevens Futures.  Calculations by Newfound Research.  Past performance is not an indicator of future results.  Performance is backtested and hypothetical.  Performance figures are gross of all fees, including, but not limited to, manager fees, transaction costs, and taxes.  Performance assumes the reinvestment of all distributions.

Here we see very strong performance results except in the 1- and 2-month lookback periods.  The allocation graph appears to suggest that results are not merely the byproduct of consistently being long or short a particular portfolio and the total return level appears to suggest that the portfolio is able to simultaneously profit from both legs.

If we return back to the graph of the stylized portfolios, we can see a significant negative correlation between the Level and Slope portfolios from 1999 to 2011.  The negative correlation appears to disappear after this point, almost precisely coinciding with a 6+ year drawdown in the cross-sectional momentum strategy.

This is due to a mixture of construction and the economic environment.

From a construction perspective, consider that the Level portfolio is long the 2-, the 5-, and the 10-year UST futures while the Slope portfolio is short 2-year and long the 10-year UST futures.  Since the positions are held in a manner that targets equivalent duration exposure, when the 2-year rate moves more than the 10-year rate, we end up in a scenario where the two trades have negative correlation, since one strategy is short and the other is long the 2-year position.  Conversely, if the 10-year rate moves more than the 2-year rate, we end up in a scenario of positive correlation, since both strategies are long the 10-year.

Now consider the 1999-2011 environment.  We had an easing cycle during the dot-com bust, a tightening cycle during the subsequent economic expansion, and another easing cycle during the 2008 crisis.  This caused significantly more directional movement in the 2-year rate than the 10-year rate.  Hence, negative correlation.

After 2008, however, the front end of the curve became pinned to zero.  This meant that there was significantly more movement in the 10-year than the 2-year, leading to positive correlation in the two strategies.  With positive correlation there is less differentiation among the two strategies and so we see a considerable increase in strategy turnover – and effectiveness – as momentum signals become less differentiated.

With that in mind, had we designed our Slope portfolio to be long 2-year UST futures and short 10-year UST futures (i.e. simply inverted the sign of our allocations), we would have seen positive correlation between Level and Slope from 1999 to 2011, resulting in a very different set of allocations and returns.  In actually testing this step, we find that the 1999-2011 period is no longer dominated by Level versus Slope trades, but rather Slope versus Curvature.  Performance of the strategy is still largely positive, but the spread among specifications widens dramatically.

Taken all together, it is difficult to conclude that the success of this strategy was not, in essence, driven almost entirely by autocorrelation in easing and tightening cycles with a relatively stable back end of the curve.1   Given that there have only been a handful of full rate cycles in the last 20 years, we’d be reluctant to rely too heavily on the equity curve of this strategy as evidence of a robust strategy.

Conclusion

In this research note, we explored the idea of generating stylized portfolios designed to isolate and profit from changes to the form of the yield curve.  Specifically, using 2-, 5-, and 10-year UST futures we design portfolios that aim to profit from level, slope, and curvature changes to the US Treasury yield curve.

With these portfolios in hand, we test whether we can time exposure to these changes using time-series momentum.

We find that while time-series momentum generates positive performance for the Level portfolio, it fails to keep up with buy & hold.  Acknowledging that level exposure may offer a positive long-term risk premium, we adjust the strategy from long/short to long/flat and are able to generate a substantially improved risk-adjusted return profile.

Time-series momentum also appears effective for the Slope portfolio, generating meaningful excess returns above the buy-and-hold portfolio.

Applying time-series momentum to the Curvature portfolio does not appear to offer any value.

We also tested whether the portfolios can be traded employing cross-sectional momentum.  We find significant success in the approach but believe that the results are an artifact of (1) the construction of the portfolios and (2) a market regime heavily influenced by monetary policy.  Without further testing, it is difficult to determine if this approach has merit.

Finally, even though our study focused on portfolios constructed using U.S. Treasury futures, we believe the results have potential application for investors who are simply trying to figure out how to position their duration exposure.  For example, a signal to be short (or flat) the Level portfolio and long the Slope portfolio may imply a view of rising rates with a flattening curve.  Translating these quantitative signals into a forecast about yield-curve behavior may allow investors to better position their fixed income portfolios.

Since this study utilized U.S. Treasury futures, these results translate well to implementing a portable beta strategy. For example, if you were an investor with a desired risk profile on par with 100% equities, you could add bond exposure on top of the higher risk portfolio. This would add a (generally) diversifying return source with only a minor cash drag to the extent that margin requirements dictate.

 


 

Harvesting the Bond Risk Premium

This post is available as a PDF download here.

Summary­

  • The bond risk premium is the return that investors earn by investing in longer duration bonds.
  • While the most common way that investors can access this return stream is through investing in bond portfolios, bonds often significantly de-risk portfolios and scale back returns.
  • Investors who desire more equity-like risk can tap into the bond risk premium by overlaying bond exposure on top of equities.
  • Through the use of a leveraged ETP strategy, we construct a long-only bond risk premium factor and investigate its characteristics in terms of rebalance frequency and timing luck.
  • By balancing the costs of trading with the risk of equity overexposure, investors can incorporate the bond risk premium as a complementary factor exposure to equities without sacrificing return potential from scaling back the overall risk level unnecessarily.

The discussion surrounding factor investing generally pertains to either equity portfolios or bond portfolios in isolation. We can calculate value, momentum, carry, and quality factors for each asset class and invest in the securities that exhibit the best characteristics of each factor or a combination of factors.

There are also ways to use these factors to shift allocations between stocks and bonds (e.g. trend and standardizing based on historical levels). However, we do not typically discuss bonds as their own standalone factor.

The bond risk premium – or term premium – can be thought of as the premium investors earn from holding longer duration bonds as opposed to cash. In a sense, it is a measure of carry. Its theoretical basis is generally seen to be related to macroeconomic factors such as inflation and growth expectations.1

While timing the term premium using factors within bond duration buckets is definitely a possibility, this commentary will focus on the term premium in the context of an equity investor who wants long-term exposure to the factor.

The Term Premium as a Factor

For the term premium, we can take the usual approach and construct a self-financing long/short portfolio of 100% intermediate (7-10 year) U.S. Treasuries that borrows the entire portfolio value at the risk-free rate.

This factor, shown in bold in the chart below, has exhibited a much tamer return profile than common equity factors.

Source: CSI Analytics, AQR, and Bloomberg. Calculations by Newfound Research. Data from 1/31/1992 to 6/28/2019. Results are hypothetical.  Results assume the reinvestment of all distributions. Results are gross of all fees, including, but not limited to manager fees, transaction costs, and taxes. Past performance is not an indicator of future results.  

Source: CSI Analytics, AQR, and Bloomberg. Calculations by Newfound Research. Data from 1/31/1992 to 6/28/2019. Results are hypothetical.  Results assume the reinvestment of all distributions. Results are gross of all fees, including, but not limited to manager fees, transaction costs, and taxes. Past performance is not an indicator of future results.  

But over the entire time period, its returns have been higher than those of both the Size and Value factors. Its maximum drawdown has been less than 40% of that of the next best factor (Quality), and it is worth acknowledging that its volatility – which is generally correlated to drawdown for highly liquid assets with non-linear payoffs – has also been substantially lower.

The term premium also has exhibited very low correlation with the other equity factors.

Source: CSI Analytics, AQR, and Bloomberg. Calculations by Newfound Research. Data from 1/31/1992 to 6/28/2019. Results are hypothetical.  Results assume the reinvestment of all distributions. Results are gross of all fees, including, but not limited to manager fees, transaction costs, and taxes. Past performance is not an indicator of future results.  

A Little Free Lunch

Whether we are treating bonds as factor or not, they are generally the primary way investors seek to diversify equity portfolios.

The problem is that they are also a great way to reduce returns during most market environments through their inherently lower risk.

Anytime that an asset with lower volatility is added to a portfolio, the risk will be reduced. Unless the asset class also has a particularly high Sharpe ratio, maintaining the same level of return is virtually impossible even if risk-adjusted returns are improved.

In a 2016 paper2, Salient broke down this reduction in risk into two components: de-risking and the “free lunch” affect.

The reduction in risk form the free lunch effect is desirable, but the risk reduction from de-risking may or may not be desirable, depending on the investor’s target risk profile.

The following chart shows the volatility breakdown of a range of portfolios of the S&P 500 (IVV) and 7-10 Year U.S. Treasuries (IEF).

Source: CSI Analytics and Bloomberg. Calculations by Newfound Research. Data from 1/31/1992 to 6/28/2019. Results are hypothetical.  Results assume the reinvestment of all distributions. Results are gross of all fees, including, but not limited to manager fees, transaction costs, and taxes. Past performance is not an indicator of future results.  

Moving from an all equity portfolio to a 50/50 equity reduces the volatility from 14.2% to 7.4%. But only 150 bps of this reduction is from the free lunch effect that stems from the lower correlation between the two assets (-0.18). The remaining 530 bps of volatility reduction is simply due to lower risk.

In this case, annualized returns were dampened from 9.6% to 7.8%. While the Sharpe ratio climbed from 0.49 to 0.70, an investor seeking higher risk would not benefit without the use of leverage.

Despite the strong performance of the term premium factor, risk-seeking investors (e.g. those early in their careers) are generally reluctant to tap into this factor too much because of the de-risking effect.

How do investors who want to bear risk commensurate with equities tap into the bond risk premium without de-risking their portfolio?

One solution is using leveraged ETPs.

Long-Only Term Premium

By taking a 50/50 portfolio of the 2x Levered S&P 500 ETF (SSO) and the 2x Levered 7-10 Year U.S. Treasury ETF (UST), we can construct a portfolio that has 100% equity exposure and 100% of the term premium factor.3

But managing this portfolio takes some care.

Left alone to drift, the allocations can get very far away from their target 50/50, spanning the range from 85/15 to 25/75. Periodic rebalancing is a must.

Source: CSI Analytics and Bloomberg. Calculations by Newfound Research. Data from 1/31/1992 to 6/28/2019. Results are hypothetical.  Results assume the reinvestment of all distributions. Results are gross of all fees, including, but not limited to manager fees, transaction costs, and taxes. Past performance is not an indicator of future results.  

Of course, now the question is, “How frequently should we rebalance the portfolio?”

This boils down to a balancing act between performance and costs (e.g. ticket charges, tax impacts, operational burden, etc.).

On one hand, we would like to remain as close to the 50/50 allocation as possible to maintain the desired exposure to each asset class. However, this could require a prohibitive amount of trading.

From a performance standpoint, we see improved results with longer holding periods (take note of the y-axes in the following charts; they were scaled to highlight the differences).

Source: CSI Analytics and Bloomberg. Calculations by Newfound Research. Data from 1/31/1992 to 6/28/2019. Results are hypothetical.  Results assume the reinvestment of all distributions. Results are gross of all fees, including, but not limited to manager fees, transaction costs, and taxes. Past performance is not an indicator of future results.  

The returns do not show a definitive pattern based on rebalance frequency, but the volatility decreases with increasing time between rebalances. This seems like it would point to waiting longer between rebalances, which would be corroborated by a consideration of trading costs.

The issues with waiting longer between the rebalance are twofold:

  1. Waiting longer is essentially a momentum trade. The better performing asset class garners a larger allocation as time progresses. This can be a good thing – especially in hindsight with how well equities have done – but it allows the portfolio to become overexposed to factors that we are not necessarily intending to exploit.
  2. Longer rebalances are more exposed to timing luck. For example, a yearly rebalance may have done well from a performance perspective, but the short-term performance could vary by as much as 50,000 bps between the best performing rebalance month and the worst! The chart below shows the performance of each iteration relative to the median performance of the 12 different monthly rebalance strategies.

Source: CSI Analytics and Bloomberg. Calculations by Newfound Research. Data from 1/31/1992 to 6/28/2019. Results are hypothetical.  Results assume the reinvestment of all distributions. Results are gross of all fees, including, but not limited to, manager fees, transaction costs, and taxes. Past performance is not an indicator of future results.  

As the chart also shows, tranching can help mitigate timing luck. Tranching also gives the returns of the strategies over the range of rebalance frequencies a more discernible pattern, with longer rebalance period strategies exhibiting slightly higher returns due to their higher average equity allocations.

Under the assumption that we can tranche any strategy that we choose, we can now compare only the tranched strategies at different rebalance frequencies to address our concern with taking bets on momentum.

Pausing for a minute, we should be clear that we do not actually know what the true factor construction should be; it is a moving target. We are more concerned with robustness than simply trying to achieve outperformance. So we will compare the strategies to the median performance of the previously monthly offset annual rebalance strategies.

The following charts shows the aggregate risk of short-term performance deviations from this benchmark.

The first one shows the aggregate deviations, both positive and negative, and the second focuses on only the downside deviation (i.e. performance that is worse than the median).4

Both charts support a choice of rebalance frequency somewhere in the range of 3-6 months.

Source: CSI Analytics and Bloomberg. Calculations by Newfound Research. Data from 1/31/1992 to 6/28/2019. Results are hypothetical.  Results assume the reinvestment of all distributions. Results are gross of all fees, including, but not limited to manager fees, transaction costs, and taxes. Past performance is not an indicator of future results.  

With the rebalance frequency set based on the construction of the factor, the last part is a consideration of costs.

Unfortunately, this is more situation-specific (e.g. what commissions does your platform charge for trades?).

From an asset manager point-of-view, where we can trade with costs proportional to the size of the trade, execute efficiently, and automate much of the operational burden, tranching is our preferred approach.

We also prefer this approach over simply rebalancing back to the static 50/50 allocation more frequently.

In our previous commentary on constructing value portfolios to mitigate timing luck, we described how tranching monthly is a different decision than rebalancing monthly and that tranching frequency and rebalance frequency are distinct decisions.

We see the same effect here where we plot the monthly tranched annually rebalanced strategy (blue line) and the strategy rebalanced back to 50/50 every month (orange line).

Source: CSI Analytics and Bloomberg. Calculations by Newfound Research. Data from 1/31/1992 to 6/28/2019. Results are hypothetical.  Results assume the reinvestment of all distributions. Results are gross of all fees, including, but not limited to manager fees, transaction costs, and taxes. Past performance is not an indicator of future results.  

Tranching wins out.

However, since the target for the term premium factor is a 50/50 static allocation, running a simple allocation filter to keep the portfolio weights within a certain tolerance can be a way to implement a more dynamic rebalancing model while reducing costs.

For example, rebalancing when the allocations for SSO and UST we outside a 5% band (i.e. the portfolio was beyond a 55/45 or 45/55) achieved better performance metrics than the monthly rebalanced version with an average of only 3 rebalances per year.

Conclusion

The bond term premium does not have to be reserved for risk-averse investors. Investors desiring portfolios tilted heavily toward equities can also tap into this diversifying return stream as a factor within their portfolio.

Utilizing leveraged ETPs is one way to maintaining exposure to equities while capturing a significant portion of the bond risk premium. However, it requires more oversight than investing in other factors such as value, momentum, and quality, which are typically packaged in easy-to-access ETFs.

If a fixed frequency rebalance approach is used, tranching is an effective way to reduce timing risk, especially when markets are volatile. Aside from tranching, we find that, historically, holding periods between 3 and 6 months yield results close in line with the median rolling short-term performance of the individual strategies. Implementing a methodology like this can reduce the risk of poor luck in choosing the rebalance frequency or starting the strategy at an unfortunate time.

If frequent rebalances – like those seen with tranching – are infeasible, a dynamic schedule based on a drift in allocations is also a possibility.

Leveraged ETPs are often seen as risk trading instruments that are not fit for retail investors who are more focused on buy-and-hold systems. However, given the right risk management, these investment vehicles can be a way for investors to access the bond term premium, getting a larger free lunch, and avoiding undesired de-risking along the way.

Tactical Portable Beta

This post is available as a PDF download here.

Summary­

  • In this commentary, we revisit the idea of portable beta: utilizing leverage to overlay traditional risk premia on existing strategic allocations.
  • While a 1.5x levered 60/40 portfolio has historically out-performed an all equity blend with similar risk levels, it can suffer through prolonged periods of under-performance.
  • Positive correlations between stocks and bonds, inverted yield curves, and rising interest rate environments can make simply adding bond exposure on top of equity exposure a non-trivial pursuit.
  • We rely on prior research to introduce a tactical 90/60 model, which uses trend signals to govern equity exposure and value, momentum, and carry signals to govern bond exposure.
  • We find that such a model has historically exhibited returns in-line with equities with significantly lower maximum drawdown.

In November 2017, I was invited to participate in a Bloomberg roundtable discussion with Barry Ritholtz, Dave Nadig, and Ben Fulton about the future of ETFs.  I was quoted as saying,

Most of the industry agrees that we are entering a period of much lower returns for stocks and fixed income. That’s a problem for younger generations. The innovation needs to be around efficient use of capital. Instead of an ETF that holds intermediate-term Treasuries, I would like to see a U.S. Treasury ETF that uses Treasuries as collateral to buy S&P 500 futures, so you end up getting both stock and bond exposure.  By introducing a modest amount of leverage, you can take $1 and trade it as if the investor has $1.50. After 2008, people became skittish around derivatives, shorting, and leverage. But these aren’t bad things when used appropriately.

Shortly after the publication of the discussion, we penned a research commentary titled Portable Beta which extolled the potential virtues of employing prudent leverage to better exploit diversification opportunities.  For investors seeking to enhance returns, increasing beta exposure may be a more reliable approach than the pursuit of alpha.

In August 2018, WisdomTree introduced the 90/60 U.S. Balanced Fund (ticker: NTSX), which blends core equity exposure with a U.S. Treasury futures ladder to create the equivalent of a 1.5x levered 60/40 portfolio.  On March 27, 2019, NTSX was awarded ETF.com’s Most Innovative New ETF of 2018.

The idea of portable beta was not even remotely uniquely ours.  Two anonymous Twitter users – “Jake” (@EconomPic) and “Unrelated Nonsense” (@Nonrelatedsense) – had discussed the idea several times prior to my round-table in 2017.  They argued that such a product could be useful to free up space in a portfolio for alpha-generating ideas.  For example, an investor could hold 66.6% of their wealth in a 90/60 portfolio and use the other 33.3% of their portfolio for alpha ideas.  While the leverage is technically applied to the 60/40, the net effect would be a 60/40 portfolio with a set of alpha ideas overlaid on the portfolio. Portable beta becomes portable alpha.

Even then, the idea was not new.  After NTSX launched, Cliff Asness, co-founder and principal of AQR Capital Management, commented on Twitter that even though he had a “22-year head start,” WisdomTree had beat him to launching a fund.  In the tweet, he linked to an article he wrote in 1996, titled Why Not 100% Equities, wherein Cliff demonstrated that from 1926 to 1993 a 60/40 portfolio levered to the same volatility as equities achieved an excess return of 0.8% annualized above U.S. equities.  Interestingly, the appropriate amount of leverage utilized to match equities was 155%, almost perfectly matching the 90/60 concept.

Source: Asness, Cliff. Why Not 100% Equities.  Journal of Portfolio Management, Winter 1996, Volume 22 Number 2.

Following up on Cliff’s Tweet, Jeremy Schwartz from WisdomTree extended the research out-of-sample, covering the quarter century that followed Cliff’s initial publishing date.  Over the subsequent 25 years, Jeremy found that a levered 60/40 outperformed U.S. equities by 2.6% annualized.

NTSX is not the first product to try to exploit the idea of diversification and leverage.  These ideas have been the backbone of managed futures and risk parity strategies for decades. The entire PIMCO’s StocksPLUS suite – which traces its history back to 1986 – is built on these foundations.  The core strategy combines an actively managed portfolio of fixed income with 100% notional exposure in S&P 500 futures to create a 2x levered 50/50 portfolio.

The concept traces its roots back to the earliest eras of modern financial theory. Finding the maximum Sharpe ratio portfolio and gearing it to the appropriate risk level has always been considered to be the theoretically optimal solution for investors.

Nevertheless, after 2008, the words “leverage” and “derivatives” have largely been terms non gratisin the realm of investment products. But that may be to the detriment of investors.

90/60 Through the Decades

While we are proponents of the foundational concepts of the 90/60 portfolio, frequent readers of our commentary will not be surprised to learn that we believe there may be opportunities to enhance the idea through tactical asset allocation.  After all, while a 90/60 may have out-performed over the long run, the short-run opportunities available to investors can deviate significantly.  The prudent allocation at the top of the dot-com bubble may have looked quite different than that at the bottom of the 2008 crisis.

To broadly demonstrate this idea, we can examine the how the realized efficient frontier of stock/bond mixes has changed shape over time.  In the table below, we calculate the Sharpe ratio for different stock/bond mixes realized in each decade from the 1920s through present.

Source: Global Financial Data.  Calculations by Newfound Research.  Returns are hypothetical and backtested.  Returns are gross of all fees, transaction costs, and taxes.  Returns assume the reinvestment of all distributions.   Bonds are the GFD Indices USA 10-Year Government Bond Total Return Index and Stocks are the S&P 500 Total Return Index (with GFD Extension).  Sharpe ratios are calculated with returns excess of the GFD Indices USA Total Return T-Bill Index.  You cannot invest in an index.  2010s reflect a partial decade through 4/2019.

We should note here that the original research proposed by Asness (1996) assumed a bond allocation to an Ibbotson corporate bond series while we employ a constant maturity 10-year U.S. Treasury index.  While this leads to lower total returns in our bond series, we do not believe it meaningfully changes the conclusions of our analysis.

We can see that while the 60/40 portfolio has a higher realized Sharpe ratio than the 100% equity portfolio in eight of ten decades, it has a lower Sharpe ratio in two consecutive decades from 1950 – 1960.  And the 1970s were not a ringing endorsement.

In theory, a higher Sharpe ratio for a 60/40 portfolio would imply that an appropriately levered version would lead to higher realized returns than equities at the same risk level.  Knowing the appropriate leverage level, however, is non-trivial, requiring an estimate of equity volatility.  Furthermore, leverage requires margin collateral and the application of borrowing rates, which can create a drag on returns.

Even if we conveniently ignore these points and assume a constant 90/60, we can still see that such an approach can go through lengthy periods of relative under-performance compared to buy-and-hold equity.  Below we plot the annualized rolling 3-year returns of a 90/60 portfolio (assuming U.S. T-Bill rates for leverage costs) minus 100% equity returns.  We can clearly see that the 1950s through the 1980s were largely a period where applying such an approach would have been frustrating.

Source: Global Financial Data.  Calculations by Newfound Research.  Returns are hypothetical and backtested.  Returns are gross of all fees, transaction costs, and taxes.   Bonds are the GFD Indices USA 10-Year Government Bond Total Return Index and Stocks are the S&P 500 Total Return Index (with GFD Extension).  The 90/60 portfolio invests 150% each month in the 60/40 portfolio and -50% in the GFD Indices USA Total Return T-Bill Index.  You cannot invest in an index.

Poor performance of the 90/60 portfolio in this era is due to two effects.

First, 10-year U.S. Treasury rates rose from approximately 4% to north of 15%.  While a constant maturity index would constantly roll into higher interest bonds, it would have to do so by selling old holdings at a loss.  Constantly harvesting price losses created a headwind for the index.

This is compounded in the 90/60 by the fact that the yield curve over this period spent significant time in an inverted state, meaning that the cost of leverage exceeded the yield earned on 40% of the portfolio, leading to negative carry. This is illustrated in the chart below, with –T-Bills– realizing a higher total return over the period than –Bonds–.

Source: Global Financial Data.  Calculations by Newfound Research.  Returns are hypothetical and backtested.  Returns are gross of all fees, transaction costs, and taxes.  Returns assume the reinvestment of all distributions.   T-Bills are the GFD Indices USA Total Return T-Bill Index, Bonds are the GFD Indices USA 10-Year Government Bond Total Return Index, and Stocks are the S&P 500 Total Return Index (with GFD Extension). You cannot invest in an index.

This is all arguably further complicated by the fact that while a 1.5x levered 60/40 may closely approximate the risk level of a 100% equity portfolio over the long run, it may be a far cry from it over the short-run.  This may be particularly true during periods where stocks and bonds exhibit positive realized correlations as they did during the 1960s through 1980s.  This can occur when markets are more pre-occupied with inflation risk than economic risk.  As inflationary fears abated and economic risk become the foremost concern in the 1990s, correlations between stocks and bonds flipped.

Thus, during the 1960s-1980s, a 90/60 portfolio exhibited realized volatility levels in excess of an all-equity portfolio, while in the 2000s it has been below.

This all invites the question: should our levered allocation necessarily be static?

Getting Tactical with a 90/60

We might consider two approaches to creating a tactical 90/60.

The first is to abandon the 90/60 model outright for a more theoretically sound approach. Specifically, we could attempt to estimate the maximum Sharpe ratio portfolio, and then apply the appropriate leverage such that we either hit a (1) constant target volatility or (2) the volatility of equities.  This would require us to not only accurately estimate the expected excess returns of stocks and bonds, but also their volatilities and correlations. Furthermore, when the Sharpe optimal portfolio is highly conservative, notional exposure far exceeding 200% may be necessary to hit target volatility levels.

In the second approach, equity and bond exposure would each be adjusted tactically, without regard for the other exposure.  While less theoretically sound, one might interpret this approach as saying, “we generally want exposure to the equity and bond risk premia over the long run, and we like the 60/40 framework, but there might be certain scenarios whereby we believe the expected return does not justify the risk.”  The downside to this approach is that it may sacrifice potential diversification benefits between stocks and bonds.

Given the original concept of portable beta is to increase exposure to the risk premia we’re already exposed to, we prefer the second approach.  We believe it more accurately reflects the notion of trying to provide long-term exposure to return-generating risk premia while trying to avoid the significant and prolonged drawdowns that can be realized with buy-and-hold approaches.

Equity Signals

To manage exposure to the equity risk premium, our preferred method is the application of trend following signals in an approach we call trend equity.  We will approximate this class of strategies with our Newfound Research U.S. Trend Equity Index.

To determine whether our signals are able to achieve their goal of “protect and participate” with the underlying risk premia, we will plot their regime-conditional betas.  To do this, we construct a simple linear model:

We define a bear regime as the worst 16% of monthly returns, a bull regime as the best 16% of monthly returns, and a normal regime as the remaining 68% of months. Note that the bottom and top 16thpercentiles are selected to reflect one standard deviation.

Below we plot the strategy conditional betas relative to U.S. equity

We can see that trend equity has a normal regime beta to U.S. equities of approximately 0.75 and a bear market beta of 0.5, in-line with expectations that such a strategy might capture 70-80% of the upside of U.S. equities in a bull market and 40-50% of the downside in a prolonged bear market. Trend equity beta of U.S. equities in a bull regime is close to the bear market beta, which is consistent with the idea that trend equity as a style has historically sacrificed the best returns to avoid the worst.

Bond Signals

To govern exposure to the bond risk premium, we prefer an approach based upon a combination of quantitative, factor-based signals.  We’ve written about many of these signals over the last two years; specifically in Duration Timing with Style Premia (June 2017), Timing Bonds with Value, Momentum, and Carry (January 2018), and A Carry-Trend-Hedge Approach to Duration Timing (October 2018).  In these three articles we explore various mixes of value, momentum, carry, flight-to-safety, and bond risk premium measures as potential signals for timing duration exposure.

We will not belabor this commentary unnecessarily by repeating past research.  Suffice it to say that we believe there is sufficient evidence that value (deviation in real yield), momentum (prior returns), and carry (term spread) can be utilized as effective timing signals and in this commentary are used to construct bond indices where allocations are varied between 0-100%.  Curious readers can pursue further details of how we construct these signals in the commentaries above.

As before, we can determine conditional regime betas for strategies based upon our signals.

We can see that our value, momentum, and carry signals all exhibit an asymmetric beta profile with respect to 10-year U.S. Treasury returns.  Carry and momentum exhibit an increase in bull market betas while value exhibits a decrease in bear market beta.

Combining Equity and Bond Signals into a Tactical 90/60

Given these signals, we will construct a tactical 90/60 portfolio as being comprised of 90% trend equity, 20% bond value, 20% bond momentum, and 20% bond carry. When notional exposure exceeds 100%, leverage cost is assumed to be U.S. T-Bills.  Taken together, the portfolio has a large breadth of potential configurations, ranging from 100% T-Bills to a 1.5x levered 60/40 portfolio.

But what is the appropriate benchmark for such a model?

In the past, we have argued that the appropriate benchmark for trend equity is a 50% stock / 50% cash benchmark, as it not only reflects the strategic allocation to equities empirically seen in return decompositions, but it also allows both positive and negative trend calls to contribute to active returns.

Similarly, we would argue that the appropriate benchmark for our tactical 90/60 model is not a 90/60 itself – which reflects the upper limit of potential capital allocation – but rather a 45% stock / 30% bond / 25% cash mix.  Though, for good measure we might also consider a bit of hand-waving and just use a 60/40 as a generic benchmark as well.

Below we plot the annualized returns versus maximum drawdown for different passive and active portfolio combinations from 1974 to present (reflecting the full period of time when strategy data is available for all tactical signals).  We can see that not only does the tactical 90/60 model (with both trend equity and tactical bonds) offer a return in line with U.S. equities over the period, it does so with significantly less drawdown (approximately half).  Furthermore, the tactical 90/60 exceeded trend equity and 60/40 annualized returns by 102 and 161 basis points respectively.

These improvements to the return and risk were achieved with the same amount of capital commitment as in the other allocations. That’s the beauty of portable beta.

Source: Federal Reserve of St. Louis, Kenneth French Data Library, and Newfound Research.  Calculations by Newfound Research.  Returns are hypothetical and backtested.  Returns are gross of all fees, transaction costs, and taxes.  Returns assume the reinvestment of all distributions.   You cannot invest in an index.

Of course, full-period metrics can deceive what an investor’s experience may actually be like.  Below we plot rolling 3-year annualized returns of U.S. equities, the 60/40 mix, trend equity, and the tactical 90/60.

Source: Federal Reserve of St. Louis, Kenneth French Data Library, and Newfound Research.  Calculations by Newfound Research.  Returns are hypothetical and backtested.  Returns are gross of all fees, transaction costs, and taxes.  Returns assume the reinvestment of all distributions.   You cannot invest in an index.

The tactical 90/60 model out-performed a 60/40 in 68% of rolling 3-year periods and the trend equity model in 71% of rolling 3-year periods.  The tactical 90/60, however, only out-performs U.S. equities in 35% of rolling 3-year periods, with the vast majority of relative out-performance emerging during significant equity drawdown periods.

For investors already allocated to trend equity strategies, portable beta – or portable tactical beta – may represent an alternative source of potential return enhancement.  Rather than seeking opportunities for alpha, portable beta allows for an overlay of more traditional risk premia, which may be more reliable from an empirical and academic standpoint.

The potential for increased returns is illustrated below in the rolling 3-year annualized return difference between the tactical 90/60 model and the Newfound U.S. Trend Equity Index.

Source: Federal Reserve of St. Louis, Kenneth French Data Library, and Newfound Research.  Calculations by Newfound Research.  Returns are hypothetical and backtested.  Returns are gross of all fees, transaction costs, and taxes.  Returns assume the reinvestment of all distributions.   You cannot invest in an index.

From Theory to Implementation

In practice, it may be easier to acquire leverage through the use of futures contracts. For example, applying portable bond beta on-top of an existing trend equity strategy may be achieved through the use of 10-year U.S. Treasury futures.

Below we plot the growth of $1 in the Newfound U.S. Trend Equity Index and a tactical 90/60 model implemented with Treasury futures.  Annualized return increases from 7.7% to 8.9% and annualized volatility declines from 9.7% to 8.5%.  Finally, maximum drawdown decreases from 18.1% to 14.3%.

We believe the increased return reflects the potential return enhancement benefits from introducing further exposure to traditional risk premia, while the reduction in risk reflects the benefit achieved through greater portfolio diversification.

Source: Quandl and Newfound Research.  Calculations by Newfound Research.  Returns are hypothetical and backtested.  Returns are gross of all fees, transaction costs, and taxes.  Returns assume the reinvestment of all distributions.   You cannot invest in an index.

It should be noted, however, that a levered constant maturity 10-year U.S. Treasury index and 10-year U.S. Treasury futures are not the same.  The futures contracts are specified such that eligible securities for delivery include Treasury notes with a remaining term to maturity of between 6.5 and 10 years.  This means that the investor short the futures contract has the option of which Treasury note to deliver across a wide spectrum of securities with potentially varying characteristics.

In theory, this investor will always choose to deliver the bond that is cheapest. Thus, Treasury futures prices will reflect price changes of this so-calledcheapest-to-deliver bond, which often does not reflect an actual on-the-run 10-year Treasury note.

Treasury futures therefore utilize a “conversion factor” invoicing system referenced to the 6% futures contract standard.  Pricing also reflects a basis adjustment that reflects the coupon income a cash bond holder would receive minus financing costs (i.e. the cost of carry) as well as the value of optionality provided to the futures seller.

Below we plot monthly returns of 10-year U.S. Treasury futures versus the excess returns of a constant maturity 10-year U.S. Treasury index.  We can see that the futures had a beta of approximately 0.76 over the nearly 20-year period, which closely aligns with the conversion factor over the period.

Source: Quandl and the Federal Reserve of St. Louis.  Calculations by Newfound Research.

Despite these differences, futures can represent a highly liquid and cost-effective means of implementing a portable beta strategy.  It should be further noted that having a lower “beta” over the last two decades has not necessarily implied a lower return as the basis adjustment can have a considerable impact.  We demonstrate this in the graph below by plotting the returns of continuously-rolled 10-year U.S. Treasury futures (rolled on open interest) and the excess return of a constant maturity 10-year U.S. Treasury index.

Source: Quandl and Newfound Research.  Calculations by Newfound Research.  Returns are hypothetical and backtested.  Returns are gross of all fees, transaction costs, and taxes.  Returns assume the reinvestment of all distributions.   You cannot invest in an index.

Conclusion

In a low return environment, portable beta may be a necessary tool for investors to generate the returns they need to hit their financial goals and reduce their risk of failing slow.

Historically, a 90/60 portfolio has outperformed equities with a similar level of risk. However, the short-term dynamics between stocks and bonds can make the volatility of a 90/60 portfolio significantly higher than a simple buy-and-hold equity portfolio. Rising interest rates and inverted yield curves can further confound the potential benefits versus an all-equity portfolio.

Since constant leverage is not a guarantee and we do not know how the future will play out, moving beyond standard portable beta implementations to tactical solutions may augment the potential for risk management and lead to a smoother ride over the short-term.

Getting over the fear of using leverage and derivatives may be an uphill battle for investors, but when used appropriately, these tools can make portfolios work harder. Risks that are known and compensated with premiums can be prudent to take for those willing to venture out and bear them.

If you are interested in learning how Newfound applies the concepts of tactical portable beta to its mandates, please reach out (info@thinknewfound.com).

Did Declining Rates Actually Matter?

This post is available as a PDF here.

Summary­­

  • From 1981 to 2017, 10-year U.S. Treasury rates declined from north of 15% to below 2%.
  • Since bond prices appreciate when rates decline, many have pointed towards this secular decline as a tailwind that created an unprecedented bull market in bonds.
  • Exactly how much declining rates contributed, however, is rarely quantified. An informal poll, however, tells us that people generally believe the impact was significant (explaining >50% of bond returns).
  • We find that while, in theory, investors should be indifferent to rate changes, high turnover in bond portfolios means that a structural mis-estimation of rate changes could be harvested.
  • Despite the positive long-term impact of declining rates, coupon yield had a much more significant impact on long-term returns.
  • The bull market in bonds was caused more by the high average rates over the past 30 years than declining rates.

 

On 9/30/1981, the 10-year U.S. Treasury rate peaked at an all-time high of 15.84%.  Over the next 30 years, it deflated to an all-time low of 1.37% on 7/5/2016.

Source: Federal Reserve of St. Louis

 

It has been repeated in financial circles that this decline in rates caused a bull market in bond returns that makes past returns a particularly poor indicator of future results.

But exactly how much did those declining rates contribute?

We turned to our financial circle on Twitter[1] with a question: For a constant maturity, 10-year U.S. Treasury index, what percent of total return from 12/1981 through 12/2012 could be attributed to declining rates?

Little consensus was found.

 

Clearly there is a large disparity in views about exactly how much declining rates actually contributed to bond returns over the last 30 years.  What we can see is that people generally think it is a lot: over 50% of people said over 50% of returns can be attributed to declining rates.

Well let’s dig in and find out.

 

Rates Down, Bonds Up

To begin, let’s remind ourselves why the bond / rate relationship exists in the first place.

Imagine you buy a 10-year U.S. Treasury bond for $100 at the prevailing 5% rate.  Immediately after you buy, interest rates drop: all available 10-year U.S. Treasury bonds – still selling for $100 – are now offering only a 4% yield.

In every other way, except the yield being offered, the bond you now hold and the bonds being offered in the market are identical.  Except yours provides a higher yield.

Therefore, it should be more valuable.  After all, you are getting more return for your investment.  And hence we get the inverse relationship between bonds and interest rates.  As rates fall, existing bond values go up and as rates rise, existing bond values go down.

With rates falling by an average of 42 basis points a year over the last 35 years, we can imagine a pretty steady, and potentially sizable tailwind to returns.

 

Just How Much More Valuable?

In our example, exactly how much did our bond appreciate when rates fell?  Or, to ask the question another way: how much would someone now be willing to buy our bond for?

The answer arises from the fact that markets loathe an arbitrage opportunity.  Scratch that: markets love arbitrage.  So much so that they are quickly wiped away as market participants jump to exploit them.

We mentioned that in the example, the bond you held and the bonds now being offered by the market were identical in every fashion except the coupon yield they offer.

Consider what would happen if the 4% bonds and your 5% bonds were both still selling for $100.  Someone could come to the market, ­short-sell a 4% bond and use the $100 to buy your 5% bond from you.  Each coupon period, they would collect $5 from the bond they bought from you, pay $4 to cover the coupon payment they owe from the short-sale, and pocket $1.

Effectively, they’ve created a free stream of $1 bills.

Knowing this to be the case, someone else might step in first and try to offer you $101 for your bond to sweeten the deal.  Now they must finance by short-selling 1.01 shares of the 4% bonds, owing $4.04 each period and $101 at maturity.  While less profitable, they would still pocket a free $0.86 per coupon payment.[2]

The scramble to sweeten the offer continues until it reaches the magic figure of $108.11.  At this price, the arbitrage disappears: the cost of financing exactly offsets the extra yield earned by the bond.

Another way of saying this is that the yield-to-maturity of both bonds is identical.  If someone pays $108.11 for the 5% coupon bond, they may receive a $5 coupon each period, but there will be a “pull-to-par” effect as the bond matures, causing the bond to decline in value.  This effect occurs because the bond has a pre-defined payout stream: at maturity, you are only going to receive your $100 back.

 

Therefore, while your coupon yield may be 5%, your effective yield – which accounts for this loss in value over time – is 4%, perfectly matching what is available to other investors.

And so everyone becomes indifferent[3] to which bond they hold.  The bond you hold may be worth more on paper, but if we try to sell it to lock in our profit, we have to reinvest at a lower yield and offsets our gain.

In a strange way, then, other than mark-to-market gains and losses, we should be largely indifferent to rate changes. 

 

The Impact of Time

One very important aspect ignored by our previous example is time.  Interest rates rarely gap up or down instantaneously: rather they move over time.

We therefore need to consider the yield curve.  The yield curve tells us what rate is being offered for bonds of different maturities.

Source: Federal Reserve of St. Louis.

 

In the yield curve plotted above, we see an upward sloping trend.  Buying a 7-year U.S. Treasury earns us a 2.25% rate, while the 10-year U.S. Treasury offers 2.45%.

Which introduces an interesting dynamic: if rates do not change whatsoever, if we buy a 10-year bond today and wait three years, our bond will appreciate in value.

Why?

The answer is because it is now a 7-year bond, and compared to other 7-year bonds it is offering 0.20% more yield.

In fact, depending on the shape of the yield curve, it can continue to appreciate until the pull-to-par effect becomes too strong.  Below we plot the value of a 10-year U.S. Treasury as it matures, assuming that the yield curve stays entirely constant over time.

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

 

Unfortunately, like in our previous example, the amount of the bond gains in value is exactly equal to the level required to make us indifferent to holding the bond to maturity or selling it and reinvesting at the prevailing rate.  For all intents and purposes, we could simply pretend we bought a 7-year bond at 2.45% and rates fell instantly to 2.25%.  By the same logic as before, we’re no better off.

We simply cannot escape the fact that markets are not going to give us a free return.

 

The Impact of Choice

Again, reality is more textured than theory.  We are ignoring an important component: choice.

In our prior examples, our choice was between continuing to hold our bond, or selling it and reinvesting in the equivalent bond.  What if we chose to reinvest in something else?

For example:

  • We buy a 2.45% 10-year U.S. Treasury for $100
  • We wait three years
  • We sell the now 7-year U.S. Treasury for $101.28 (assuming the yield curve did not change)
  • We reinvest in 2.45% 10-year U.S. Treasuries, sold at $100

If the yield curve never changes, we can keep capturing this roll return by simply waiting, selling, and buying what we previously owned.

What’s the catch?  The catch, of course, is that we’re assuming rates won’t change.  If we stop for a moment, however, and consider what the yield curve is telling us, we realize this assumption may be quite poor.

The yield curve provides several rates at which we can invest.  What if we are only interested in investing over the next year?  Well, we can buy a 1-year U.S. Treasury at 0.85% and just hold it to maturity, or we could buy a 10-year U.S. Treasury for 2.45% and sell it after a year.

That is a pretty remarkable difference in 1-year return potential.

If the market is even reasonably efficient, then the expected 1-year return, no matter where we buy on the curve, should be the same.  Therefore, the only way the 10-year U.S. Treasury yield should be so much higher than the 1-year is if the market is predicting that rates are going to go up such that the extra yield is exactly offset by the price loss we take when we sell the bond.

Hence a rising yield curve tells us the market is expecting rising rates.  At least, that’s what the pure expectations hypothesis (“PEH”) says.  Competing theories argue that investors should earn at least some premium for bearing term risk.  Nevertheless, there should be some component of a rising yield curve that tells us rates should go up.

However, over the past 35 years, the average slope of the yield curve (measured as 10-year yields minus 2-year yields) has been over 100bp.  The market was, in theory, was consistently predicting rising rates over a period rates fell.

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

 

Not only could an investor potentially harvest roll-yield, but also the added bump from declining rates.

Unfortunately, doing so would require significant turnover.  We would have to constantly sell our bonds to harvest the gains.

While this may have created opportunity for active bond managers, a total bond market index typically holds bonds until maturity.

 

Turnover in a Bond Index

Have you ever looked at the turnover in a total bond market index fund?  You might be surprised.

While the S&P 500 has turnover of approximately 5% per year, the Bloomberg Barclay’s U.S. Aggregate often averages between 40-60% per year.

Where is all that turnover coming from?

  • Index additions (e.g. new issuances)
  • Index deletions (e.g. maturing bonds)
  • Paydowns
  • Coupon reinvestment

If the general structure of the fixed income market does not change considerably over time, this level of turnover implies that a total bond market index will behave very similarly to a constant duration bond fund.

Bonds are technically held to maturity, but roll return and profit/loss from shifts in interest rates are booked along the way as positions are rebalanced.

Which means that falling rates could matter.  Even better, we can test how much falling rates mattered by proxying a total bond index with a constant maturity bond index[4].

Specifically, we will look at a constant maturity 10-year U.S. Treasury index.  We will assume 10-year Treasuries are bought at the beginning of each year, held for a year, and sold as 9-year Treasuries[5].  The proceeds will then be reinvested back into the new 10-year Treasuries.  We will also assume that coupons are paid annually.

We ran the test from 12/1981 to 12/2012, since those dates represented both the highest and lowest end-of-year rates.

We will then decompose returns into three components:

  • Coupon yield (“Coupon”)
  • Roll return (“Roll”)
  • Rate changes (“Shift”)

Coupon yield is, simply, the return we get from the coupon itself.  Roll return is equal to the slope between 10-year and 9-year U.S. Treasuries at the point of purchase adjusted by the duration of the bond.  Rate changes are measured as price return we achieve due to shifts in the 9-year rate from the point at which we purchased the bond and the point at which we are selling it.

This allows us to create a return stream for each component as well as identify each component’s contribution to the total return of the index.

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

 

What we can see is that coupon return dominates roll and shift.  On an annualized basis, coupon was 6.24%, while roll only contributed 0.24% and shift contributed 2.22%.

Which leaves us with a final decomposition: coupon yield accounted for 71% of return, roll accounted for 3%, and shift accounted for 26%.

We can perform a similar test for constant maturity indices constructed at different points on the curve as well.

 

Total Return% Contribution
CouponRollShiftCouponRollShift
10-year6.24%0.24%2.22%71.60%2.84%25.55%
7-year6.08%0.62%1.72%72.16%7.37%20.47%
5-year5.81%0.65%1.29%75.01%8.38%16.61%

 

 

Conclusion: Were Declining Rates Important?

A resounding yes.  An extra 2.22% per year over 30+ years is nothing to sneeze at.  Especially when you consider that this was the result of a very unique period unlikely to be repeated over the next 30 years.

Just as important to consider, however, is that it was not the most important contributor to total returns.  While most people in our poll answered that decline in rates would account for 50%+ of total return, the shift factor only came in at 26%.

The honor of the highest contributor goes to coupon yield.  Even though rates deflated over 30 years, the average yield was high enough to be, by far and away, the biggest contributor to returns.

The bond bull was not due to declining rates, in our opinion, but rather the unusually high rates we saw over the period.

A fact which is changing today.  We can see this by plotting the annual sources of returns year-by-year.

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

 

Note that while coupon is always a positive contributor, its role has significantly diminished in recent years compared to the influence of rate changes.

The consistency of coupon and the varying influence of shift on returns (i.e. both positive and negative) means that coupon yield actually makes an excellent predictor of future returns.  Lozada (2015)[6] finds that the optimal horizon to use yield as a predictor of return in constant duration or constant-maturity bond funds is at twice the duration.

Which paints a potentially bleak picture for fixed income investors.

 

FundAssetDurationTTM YieldPredicted Return
AGGU.S. Aggregate Bonds5.742.37%2.37% per year through 2028
IEI3-7 Year U.S. Treasuries4.481.31%1.31% per year through 2025
IEF7-10 Year U.S. Treasuries7.591.77%1.77% per year through 2032
TLT20+ Year U.S. Treasuries17.392.56%2.56% per year through 2051
LQDInvestment Grade Bonds8.243.28%3.28% per year through 2033

Source: iShares.  Calculations by Newfound Research.

 

Note that we are using trailing 12-month distribution yield for the ETFs here.  We do this because ETF issuers often amortize coupon yield to account for pull-to-par effects, making it an approximation of yield-to-worst.  It is not perfect, but we don’t think the results materially differ in magnitude with any other measure: it’s still ugly.

The story remains largely the same as we’ve echoed over the past year: when it comes to fixed income, your current yield will be a much better predictor of returns than trying to guess about changing rates.

Coupon yield had 3x the influence on total return over the last 30 years than changes in rates did.

What we should be concerned about today is not rising rates: rather, we should be concerned about the returns that present low rates imply for the future.

And we should be asking ourselves: are there other ways we can look to manage risk or find return?

[1] Find us on Twitter!  Newfound is @thinknewfound and Corey is @choffstein.

[2] It is $0.86 instead of $0.96 because they need to set aside $0.10 to cover the extra dollar they owe at maturity.

[3] This is a bit of a simplification as the bonds will have different risk characteristics (e.g. different durations and convexity) which could cause investors, especially those with views on future rate changes, to prefer one bond over the other.

[4] We made the leap here from total bond index to constant duration index to constant maturity index.  Each step introduces some error, but we believe for our purposes the error is de minimis and a constant maturity index allows for greater ease of implementation.

[5] Since no 9-year U.S. Treasury is offered, we create a model for the yield curve using cubic splines and then estimate the 9-year rate.

[6] http://content.csbs.utah.edu/~lozada/Research/IniYld_6.pdf

 

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