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).
Trend following is “mechanically convex,” meaning that the convexity profile it generates is driven by the rules that govern the strategy.
While the convexity can be measured analytically, the unknown nature of future price dynamics makes it difficult to say anything specific about expected behavior.
Using simulation techniques, we aim to explore how different trend speed models behave for different drawdown sizes, durations, and volatility levels.
We find that shallow drawdowns are difficult for almost all models to exploit, that faster drawdowns generally require faster models, and that lower levels of price volatility tend to make all models more effective.
Finally, we perform historical scenario analysis on U.S. equities to determine if our derived expectations align with historical performance.
We like to use the phrase “mechanically convex” when it comes to trend following. It implies a transparent and deterministic “if-this-then-that” relationship between the price dynamics of an asset, the rules of a trend following, and the performance achieved by a strategy.
Of course, nobody knows how an asset’s future price dynamics will play out. Nevertheless, the deterministic nature of the rules with trend following should, at least, allow us to set semi-reasonable expectations about the outcomes we are trying to achieve.
A January 2018 paper from OneRiver Asset Management titled The Interplay Between Trend Following and Volatility in an Evolving “Crisis Alpha” Industry touches precisely upon this mechanical nature. Rather than trying to draw conclusions analytically, the paper employs numerical simulation to explore how certain trend speeds react to different drawdown profiles.
Specifically, the authors simulate 5-years of daily equity returns by assuming a geometric Brownian motion with 13% drift and 13% volatility. They then simulate drawdowns of different magnitudes occurring over different time horizons by assuming a Brownian bridge process with 35% volatility.
The authors then construct trend following strategies of varying speeds to be run on these simulations and calculate the median performance.
Below we re-create this test. Specifically,
We generate 10,000 5-year simulations assuming a geometric Brownian motion with 13% drift and 13% volatility.
To the end of each simulation, we attach a 20% drawdown simulation, occurring over T days, assuming a geometric Brownian bridge with 35% volatility.
We then calculate the performance of different NxM moving-average-cross-over strategies, assuming all trades are executed at the next day’s closing price. When the short moving average (N periods) is above the long moving average (M periods), the strategy is long, and when the short moving average is below the long moving average, the strategy is short.
For a given T-day drawdown period and NxM trend strategy, we report the median performance across the 10,000 simulations over the drawdown period.
By varying T and the NxM models, we can attempt to get a sense as to how different trend speeds should behave in different drawdown profiles.
Note that the generated tables report on the median performance of the trend following strategy over only the drawdown period. The initial five years of positive expected returns are essentially treated as a burn-in period for the trend signal. Thus, if we are looking at a drawdown of 20% and an entry in the table reads -20%, it implies that the trend model was exposed to the full drawdown without regard to what happened in the years prior to the drawdown. The return of the trend following strategies over the drawdown period can be larger than the drawdown because of whipsaw and the fact that the underlying equity can be down more than 20% at points during the period.
Furthermore, these results are for long/short implementations. Recall that a long/flat strategy can be thought of as 50% explore to equity plus 50% exposure to a long/short strategy. Thus, the results of long/flat implementations can be approximated by halving the reported result and adding half the drawdown profile. For example, in the table below, the 20×60 trend system on the 6-month drawdown horizon is reported to have a drawdown of -4.3%. This would imply that a long/flat implementation of this strategy would have a drawdown of approximately -12.2%.
Calculations by Newfound Research. Results are hypothetical. Returns are gross of all fees, including manager fees, transaction costs, and taxes.
There are several potential conclusions we can draw from this table:
None of the trend models are able to avoid an immediate 1-day loss.
Very-fast (10×30 to 10×50) and fast (20×60 and 20×100) trend models are able to limit losses for week-long drawdowns, and several are even able to profit during month-long drawdowns but begin to degrade for drawdowns that take over a year.
Intermediate (50×150 to 50×250) and slow (75×225 to 75×375) trend models appear to do best for drawdowns in the 3-month to 1-year range.
Very slow (100×300 to 200×400) trend models do very little at all for drawdowns over any timeframe.
Note that these results align with results found in earlier research commentaries about the relationship between measured convexity and trend speed. Namely, faster trends appear to exhibit convexity when measured over shorter horizons, whereas slower trend speeds require longer measurement horizons.
But what happens if we change the drawdown profile from 20%?
Varying Drawdown Size
Calculations by Newfound Research. Results are hypothetical. Returns are gross of all fees, including manager fees, transaction costs, and taxes.
We can see some interesting patterns emerge.
First, for more shallow drawdowns, slower trend models struggle over almost all drawdown horizons. On the one hand, a 10% drawdown occurring over a month will be too fast to capture. On the other hand, a 10% drawdown occurring over several years will be swamped by the 35% volatility profile we simulated; there is too much noise and too little signal.
We can see that as the drawdowns become larger and the duration of the drawdown is extended, slower models begin to perform much better and faster models begin to degrade in relative performance.
Thus, if our goal is to protect against large losses over sustained periods (e.g. 20%+ over 6+ months), intermediate-to-slow trend models may be better suited our needs.
However, if we want to try to avoid more rapid, but shallow drawdowns (e.g. Q4 2018), faster trend models will likely have to be employed.
Varying Volatility
In our test, we specified that the drawdown periods would be simulated with an intrinsic volatility of 35%. As we have explored briefly in the past, we expect that the optimal trend speed would be a function of both the dynamics of the trend process and the dynamics of the price process. In simplified models (i.e. constant trend), we might assume the model speed is proportional to the trend speed relative to the price volatility. For a more complex model, others have proposed that model speed should be proportional to the volatility of the trend process relative to the volatility of the price process.
Therefore, we also want to ask the question, “what happens if the volatility profile changes?” Below, we re-create tables for a 20% and 40% drawdown, but now assume a 20% volatility level, about half of what was previously used.
Calculations by Newfound Research. Results are hypothetical. Returns are gross of all fees, including manager fees, transaction costs, and taxes.
We can see that results are improved almost without exception.1
Not only do faster models now perform better over longer drawdown horizons, but intermediate and slow models are now much more effective at horizons where they had previously not been. For example, the classic 50×200 model saw an increase in its median return from -23.1% to -5.3% for 20% drawdowns occurring over 1.5 years.
It is worth acknowledging, however, that even with a reduced volatility profile, a shallower drawdown over a long horizon is still difficult for trend models to exploit. We can see this in the last three rows of the 20% drawdown / 20% volatility table where none of the trend models exhibit a positive median return, despite having the ability to profit from shorting during a negative trend.
Conclusion
The transparent, “if-this-then-that” nature of trend following makes it well suited for scenario analysis. However, the uncertainty of how price dynamics may evolve can make it difficult to say anything about the future with a high degree of precision.
In this commentary, we sought to evaluate the relationship between trend speed, drawdown size, drawdown speed, and asset volatility and a trend following systems ability to perform in drawdown scenarios. We generally find that:
The effectiveness of trend speed appears to be positively correlated with drawdown speed. Intuitively, faster drawdowns require faster trend models.
Trend models struggle to capture shallow drawdowns (e.g. 10%). Faster trend models appear to be effective in capturing relatively shallow drawdowns (~20%), so long as they happen with sufficient speed (<6 months). Slower models appear relatively ineffective against this class of drawdowns over all horizons, unless they occur with very little volatility.
Intermediate-to-slow trend models are most effective for larger, more prolonged drawdowns (e.g. 30%+ over 6+ months).
Lower intrinsic asset volatility appears to make trend models effective over longer drawdown horizons.
From peak-to-trough, the dot-com bubble imploded over about 2.5 years, with a drawdown of about -50% and a volatility of 24%. The market meltdown in 2008, on the other hand, unraveled in 1.4 years, but had a -55% drawdown with 37% volatility. Knowing this, we might expect a slower model to have performed better in early 2000, while an intermediate model might have performed best in 2008.
If only reality were that simple!
While our tests may have told us something about the expected performance, we only live through one realization. The precise and idiosyncratic nature of how each drawdown unfolds will ultimately determine which trend models are successful and which are not. Nevertheless, evaluating the historical periods of large U.S. equity drawdowns, we do see some common patterns emerge.
Calculations by Newfound Research. Results are hypothetical. Returns are gross of all fees, including manager fees, transaction costs, and taxes.
The sudden drawdown of 1987, for example, remains elusive for most of the models. The dot-com and Great Recession were periods where intermediate-to-slow models did best. But we can also see that trend is not a panacea: the 1946-1949 drawdown was very difficult for most trend models to navigate successfully.
Our conclusion is two-fold. First, we should ensure that the trend model we select is in-line with the sorts of drawdown profiles we are looking to create convexity against. Second, given the unknown nature of how drawdowns might evolve, it may be prudent to employ a variety of trend following models.
Successful investing requires that investors navigate around a large number of risks throughout their lifecycle. We believe that the two most daunting risks investors face are the risk of failing fast and the risk of failing slow.
Slow failure occurs when an investor does not grow their investment capital sufficiently over time to meet future real liabilities. This often occurs because they fail to save enough or because they invest too conservatively.
Fast failure occurs when an investor – often those who are living off of portfolio withdrawals and for whom time is no longer an ally – suffers a significant drawdown that permanently impairs their portfolio.
We believe that sensitivity to these risks should dictate an investor’s allocation profile. Investors sensitive to slow failure should invest more aggressively and bear more risk in certain bad states of the world for the potential to earn excess returns in good states. On the other hand, investors sensitive to fast failure should invest more conservatively, sacrificing returns in order to avoid catastrophe.
We believe this framework can also be used to inform how investors can fund an allocation from their strategic policy to trend equity strategies.
Homer’s Odyssey follows the epic ten-year journey of Odysseus and his men as they try to make their way home after the fall of Troy. Along the way, the soldiers faced a seemingly endless string of challenges, including a cyclops who ate them alive, a sorceress who turned them into pigs, and sirens that would have lured them to their deaths with a song had they not plugged their ears with beeswax.
In one trial, the men had to navigate the Strait of Messina between the sea monsters Scylla and Charybdis. With her six serpentine heads, each with a triple row of sharp teeth, Scylla haunted the cliffs that lined one edge of the strait. Ships that came too close would immediately lose six sailors to the ravenous monster. Living under a rock on the other side of the strait was Charybdis. A few times a day, this monster would swallow up large amounts of water and belch it out, creating whirlpools that could sink an entire ship.
The strait was so narrow that the monsters lived within an arrow’s range of one another. To safely avoid one creature meant almost necessarily venturing too close to the other. On the one hand was almost certain, but limited, loss; on the other, the low probability of complete catastrophe.
Slow failure results from taking too little risk, often from investors allocating too conservatively or holding excessive cash. In doing so, they fail to grow their capital at a sufficient rate to meet future real liabilities. Failure in this arena does not show up as a large portfolio drawdown: it creeps into the portfolio over time through opportunity cost or the slow erosion of purchasing power.
Fast failure results from the opposite scenario: taking too much risk. By allocating too aggressively (either to highly skewed or highly volatile investments), investors might incur material losses in their portfolios at a time when they cannot afford to do so.
We would argue that much of portfolio design is centered around figuring out which risk an investor is most sensitive to at a given point in their lifecycle and adjusting the portfolio accordingly.
Younger investors, for example, often have significant human capital (i.e. future earning potential) but very little investment capital. Sudden and large losses in their portfolios, therefore, are often immaterial in the long run, as both time and savings are on their side. Investing too conservatively at this stage in life can rely too heavily on savings and fail to exploit the compounding potential of time.
Therefore, younger, growth-oriented investors should be willing to bear the risk of failing fast to avoid the risk of failing slow. In fact, we would argue that it is the willingness to bear the risk of failing fast that allows these investors to potentially earn a premium in the first place. No pain, no premium.
Over time, investors turn their human capital into investment capital through savings and investment. At retirement, investors believe that their future liabilities are sufficiently funded, and so give-up gainful employment to live off of their savings and investments. In other words, the sensitivity to slow failure has significantly declined.
However, with less time for the potential benefits of compounding and no plan on replenishing investments through further savings, the sensitivity to the risk of fast failure is dramatically heightened, especially in the years just prior to and just after retirement. This is further complicated by the fact that withdrawals from the portfolio can heighten the impact of sustained and large drawdowns.
Thus, older investors tend shift from riskier stocks to safer bonds, offloading their fast failure risk to those willing to bear it. Yet we should be hesitant to de-risk entirely; we must also acknowledge longevity risk. Too conservative a profile may also lead to disaster if an investor outlives their nest-egg.
As we balance the scales of failing fast and slow, we can see why trying to invest a perpetual endowment is so difficult. Consistent withdrawals invite the risk of failing fast while the perpetual nature invites the risk of failing slow. A narrow strait to navigate between Scylla and Charybdis, indeed!
We would be remiss if we did not acknowledge that short-term, high quality bonds are not a panacea for fail fast risk. Inflation complicates the calculus and unexpected bouts of inflation (e.g. the U.S. in the 1970s) or hyper-inflation (e.g. Brazil in the 1980s, Peru from 1988-1991, or present-day Venezuela) can cause significant, if not catastrophic, declines in real purchasing power if enough investment risk is not borne.
Purchasing seemingly more volatile assets may actually be a hedge here. For example, real estate, when marked-to-market, may exhibit significant relative swings in value over time. However, as housing frequently represents one the largest real liabilities an investor faces, purchase of a primary residence can lock in the real cost of the asset and provide significant physical utility. Investors can further reduce inflation risk by financing the purchase with a modest amount of debt, a liability which will decline in real value with unexpected positive inflation shocks.
The aforementioned nuances notwithstanding, this broad line of thinking invites some interesting guidance regarding portfolio construction.
Investors sensitive to fast failure should seek to immunize their real future liabilities (e.g. via insurance, real asset purchases, cash-flow matching, structured products, et cetera). As they survey the infinite potential of future market states, they should be willing to give up returns in all states to avoid significant failure in any given one of them.
Investors sensitive to slow failure should seek to bear a diversified set of risk premia (e.g. equity risk premium, bond risk premium, credit premium, value, momentum, carry, et cetera) that allows their portfolios to grow sufficiently to meet future real liabilities. These investors, then, are willing to pursue higher returns in the vast majority of future market states, even if it means increased losses in a few states.
I personally imagine this as if the investor sensitive to failing slow has piled up all their risk – like a big mound of dough – in the bad outcome states of the world. For their willingness to bear this risk, they earn more return in the good outcome states. The investor sensitive to failing fast, on the other hand, smears that mound of risk across all the potential outcomes. In their unwillingness to bear risk in a particular state, they reduce return potential across all states, but also avoid the risk of catastrophe.
Source: BuzzFeed
Quantitatively, we saw exactly this trade-off play out in our piece The New Glide Path, where we attempted to identify the appropriate asset allocation for investors in retirement based upon their wealth level. We found that:
Investors who were dramatically under-funded – i.e. those at risk of failing slow – relative to real liabilities were allocated heavily to equities.
Investors who were near a safe funding level – i.e. those at risk of failing fast – were tilted dramatically towards assets like Treasury bonds in order to immunize their portfolio against fast failure.
The fortunate few investors who were dramatically over-funded could, pretty much, allocate however they pleased.
We believe this same failing slow and failing fast framework can also inform how trend equity strategies – like those we manage here at Newfound Research – can be implemented by allocators.
In our recent commentary Three Applications of Trend Equity we explored three implementation ideas for trend equity strategies: (1) as a defensive equity sleeve; (2) as a tactical pivot; or (3) as an alternative. While these are the most common approaches we see to implementing trend equity, we would argue that a more philosophically consistent route might be one that incorporates the notions of failing fast and failing slow.
In Risk Ignition with Trend Following we examined the realized efficient frontier of U.S. stocks and bonds from 1962-2017 and found that an investor who wanted to hold a portfolio targeting an annualized volatility of 10% would need to hold between 40-50% of their portfolio in bonds. If we were able to magically eliminate the three worst years of equity returns, at the cost of giving up the three best, that number dropped to 20-30%. And if we were able to eliminate the worst five at the cost of giving up the best five? Just 10%.
One interpretation of this data is that, with the benefit of hindsight, a moderate-risk investor would have had to carry a hefty allocation to bonds for the 55 years just to hedge against the low-probability risk of failing fast. If we believe the historical evidence supporting trend equity strategies, however, we may have an interesting solution at hand:
A strategy that has historically captured a significant proportion of the equity risk premium.
A strategy that has historically avoided a significant proportion of prolonged equity market declines.
Used appropriately, this strategy may help investors who are sensitive to failing slowly tactically increase their equity exposure when trends are favorable. Conversely, trend equity may help investors who are sensitive to failing fast de-risk their portfolio during negative trend environments.
To explore this opportunity, we will look at three strategic profiles: an 80% U.S. equity / 20% U.S. bond mix, a 50/50 mix, and a 20/80 mix. The first portfolio represents the profile of a growth investor who is sensitive to failing slow; the second portfolio represents a balanced investor, sensitive to both risks; the third represents a conservative investor who is sensitive to failing fast.
We will allocate a 10% slice of each portfolio to a naïve trend equity strategy in reverse proportion to the stock/bond mix. For example, for the 80/20 portfolio, 2% of the equity position and 8% of the bond position will be used to fund the trend equity position, creating a 78/12/10 portfolio. Similarly, the 20/80 will become an 12/78/10 and the 50/50 will become a 45/45/10.
We will use the S&P 500 index for U.S. equities, Dow Jones Corporate Bond index for U.S. bonds, and a 1-Year U.S. Government Note index for our cash proxy. The trend equity strategy will blend signals generated from trailing 6-through-12-month total returns, investing in the S&P 500 over the subsequent month in proportion to the number of positive signals. Remaining capital will be invested in the cash proxy. All portfolios are rebalanced monthly from 12/31/1940 through 12/31/2018.
Source: Global Financial Data. Calculations by Newfound Research. Returns are backtested and hypothetical. Past performance is not a guarantee of future results. Returns are gross of all fees. Returns assume the reinvestment of all distributions. None of the strategies shown reflect any portfolio managed by Newfound Research and were constructed solely for demonstration purposes within this commentary. You cannot invest in an index.
For conservative investors sensitive to the risk of failing fast, we can see that the introduction of trend equity not only slightly increased returns, but it reduced the maximum drawdown and Ulcer index profile of the portfolio. Below we plot the actual difference in portfolio drawdowns between a 12/78/10 mix and a 20/80 mix over the backtested period.
While we can see that there are periods where the 12/78/10 mix exhibited higher drawdowns (i.e. values below the 0% line), during major drawdown periods, the 12/78/10 mix historically provided relative relief. This is in line with our philosophy that risk cannot be destroyed, only transformed: the historical benefits that trend following has exhibited to avoiding significant and prolonged drawdowns have often come at the cost of increased realized drawdowns due to a slightly increased average allocation to equities as well as self-incurred drawdowns due to trading whipsaws.
On the opposite end of the spectrum, we can see that those investors sensitive to failing slowly were able to increase annualized returns without a significant increase to maximum drawdown. We should note, however, an increase in the Ulcer index, indicating more frequent and deeper drawdowns.
This makes sense, as we would expect the 78/12/10 mix to be on average over-allocated to equities, making it more sensitive to quick and sudden declines (e.g. 1987). Furthermore, the most defensive the mix can tilt is towards a 78/22 blend, leaving little wiggle-room in its ability to mitigate downside exposure. Nevertheless, we can see below that during periods of more prolonged drawdowns (e.g. 1975, 1980, and 2008), the 78/12/10 mix was able to reduce the drawdown profile slightly.
In these backtests we see that investors sensitive to failing fast can fund a larger proportion of trend equity exposure from their traditional equity allocation in an effort to reduce risk while maintaining their return profile. Conversely, investors sensitive to failing slow can fund a larger proportion of their trend equity exposure from bonds, hoping to increase their annualized return while maintaining the same risk exposure.
Of course, long-term annualized return statistics can belie short-term experience. Examining rolling return periods, we can gain a better sense as to our confidence as to the time horizon over which we might expect, with confidence, that a strategy should contribute to our portfolio.
Below we plot rolling 1-to-10-year annualized return differences between the 78/12/10 and the 80/20 mixes.
We can see that in the short-term (e.g. 1-year), there are periods of both significant out- and under-performance. Over longer periods (5- and 10-years), which tend to capture “full market cycles,” we see more consistent out-performance.
Of course, this is not always the case: the 78/12/10 mix underperformed the 80/20 portfolio for the 10 years following the October 1987 market crash. Being over-allocated to equities at that time had a rippling effect and serves to remind us that our default assumption should be that “risk cannot be destroyed, only transformed.” But when we have the option to adjust our exposure to these risks, the benefit of avoiding slow failure may outweigh the potential to underperform slightly.
This evidence suggests that funding an allocation to trend equity in a manner that is in line with an investor’s risk sensitivities may be beneficial. Nevertheless, we should also acknowledge that the potential benefits are rarely realized in a smooth, continuous manner and that the implementation should be considered a long-term allocation, not a trade.
Conclusion
Investors must navigate a significant number of risks throughout their lifecycle. At Newfound, we like to think of the two driving risks that investors face as the risk of failing fast and the risk of failing slow. Much like Odysseus navigating between Scylla and Charybdis, these risks are at direct odds with one another and trying to avoid one increases the risk of the other.
Fortunately, which of these risks an investor cares about evolves throughout their lifecycle. Young investors typically can afford to fail fast, as they have both future earning potential and time on their side. By not saving adequately, or investing too conservatively, however, a young investor can invite the risk of slow failure and find themselves woefully underfunded for future real liabilities. Hence investors at this stage or typically aggressively allocated towards growth assets.
As investors age, time and earning potential dwindle and the risk of fast failure increases. At this point, large and prolonged drawdowns can permanently impair an investor’s lifestyle. So long as real liabilities are sufficiently funded, the risk of slow failure dwindles. Thus, investors often de-risk their portfolios towards stable return sources such as high-quality fixed income.
We believe this dual-risk framework is a useful model for determining how any asset or strategy should fit within a particular investor’s plan. We demonstrate this concept with a simple trend equity strategy. For an investor sensitive to slow failure, we fund the allocation predominately from bond exposure; for an investor sensitive to fast failure, we fund the allocation predominately form equities.
Ultimately – and consistent with findings in our other commentaries – a risk-based mindset makes it obvious that allocation choices are really all about trade-offs in opportunity (“no pain, no premium”) and risk (“risk cannot be destroyed, only transformed.”)
It can be difficult to disentangle the difference between luck and skill by examining performance on its own.
We simulate the returns of investors with different prediction accuracy levels and find that an investor with the skill of a fair coin (i.e. 50%) would likely under-perform a simple buy-and-hold investor, even before costs are considered.
It is not until an investor exhibits accuracy in excess of 60% that a buy-and-hold investor is meaningfully “beaten” over rolling 5-year evaluation periods.
In the short-term, however, a strategy with a known accuracy rate can still masquerade as one far more accurate or far less accurate due to luck.
Further confounding the analysis is the role of skewness of the return distribution. Positively skewed strategies, like trend following, can actually exhibit accuracy rates lower than 50% and still be successful over the long run.
Relying on perceptions of accuracy alone may lead to highly misguided conclusions.
The only thing sure about luck is that it will change. — Bret Harte1
The distinction between luck and skill in investing can be extremely difficult to measure. Seemingly good or bad strategies can be attributable to either luck or skill, and the truth has important implications for the future prospects of the strategy.Source: Grinold and Kahn, Active Portfolio Management. (New York: McGraw-Hill, 1999).
Time is one of the surest ways to weed out lucky strategies, but the amount of time needed to make this decision with a high degree of confidence can be longer than we are willing to wait. Or, sometimes, even longer than the data we have.
For example, in order to be 95% confident that a strategy with a 7% historical return and a volatility of 15% has a true expected return that is greater than a 2% risk-free rate, we would need 27 years of data. While this is possible for equity and bond strategies, we would have a long time to wait in order to be confident in a Bitcoin strategy with these specifications.
Even after passing that test, however, that same strategy could easily return less than the risk-free rate over the next 5 years (the probability is 25%).
Regardless of the skill, would you continue to hold a strategy that underperformed for that long?
In this commentary, we will use a sample U.S. sector strategy that isolates luck and skill to explore the impacts of varying accuracy and how even increased accuracy may only be an idealized goal.
The (In)Accurate Investor
To investigate the historical impact of luck and skill in the arena of U.S. equity investing, we will consider a strategy that invests in the 30 industries from the Kenneth French Data Library.
Each month, the strategy independently evaluates each sector and either holds it or invests the capital at the risk-free rate. The term “evaluates” is used loosely here; the evaluation can be as simple as flipping a (potentially biased) coin.
The allocation allotted to each sector is 1/30th of the portfolio (3.33%). We are purposely not reallocating capital among the sectors chosen so that the sector calls based on the accuracy straightforwardly determine the performance.
To get an idea for the bounds of how well – or poorly – this strategy would have performed over time, we can consider three investors:
The Plain Investor – This investor simply holds all 30 sectors, equally weighted, all the time.
The Perfect Investor – This investor allocates with 100% accuracy. Using a crystal ball to look into the future, if a sector will go up in the subsequent month, this investor will allocate to it. If the sector will go down, this investor will invest the capital in cash.
The Anti-Perfect Investor – This investor not merely imperfect, they are the complete opposite of the Perfect Investor. They make the wrong calls to invest or not without fail. Their accuracy is 0%. They are so reliably bad that if you could short their strategy, you would be the Perfect Investor.
The Perfect and Anti-Perfect investors set the bounds for what performance is possible within this framework, and the Plain Investor denotes the performance of not making any decisions.
The growth of each boundary strategy over the entire time period is a little outrageous.
Annualized Return
Annualized Volatility
Maximum Drawdown
Plain Investor
10.5%
19.3%
83.9%
Perfect Investor
42.6%
11.0%
0.0%
Anti-Perfect Investor
-20.0%
12.1%
100.0%
Source: Kenneth French Data Library. Calculations by Newfound Research. Past performance is not a guarantee of future results. All returns are hypothetical and backtested. Returns are gross of all fees. This does not reflect any investment strategy offered or managed by Newfound Research and was constructed exclusively for the purposes of this commentary. It is not possible to invest in an index.
A more informative illustration is the rolling annualized 5-year return for each strategy.
Source: Kenneth French Data Library. Calculations by Newfound Research. Past performance is not a guarantee of future results. All returns are hypothetical and backtested. Returns are gross of all fees. This does not reflect any investment strategy offered or managed by Newfound Research and was constructed exclusively for the purposes of this commentary. It is not possible to invest in an index.
While the spread between the Perfect and Anti-Perfect investors ebbs and flows, its median value Is 59,000 basis points (“bps”). Between the Perfect and Plain investors, there is still 29,000 bps of annualized outperformance to be had. A natural wish is to make calls that harvest some of this spread.
Accounting for Accuracy
Now we will look at a set of investors who are able to evaluate each sector with some known degree of accuracy.
For each accuracy level between 0% and 100% (i.e. our Anti-Perfect and Perfect investors, respectively), we simulate 1,000 trials and look at how the historical results have played out.
A natural starting point is the investor who merely flips a fair coin for each sector. Their accuracy is 50%.
The chart below shows the rolling 5-year performance range of the simulated trials for the 50% Accurate Investor.
Source: Kenneth French Data Library. Calculations by Newfound Research. Past performance is not a guarantee of future results. All returns are hypothetical and backtested. Returns are gross of all fees. This does not reflect any investment strategy offered or managed by Newfound Research and was constructed exclusively for the purposes of this commentary. It is not possible to invest in an index.
In 59% of the rolling periods, the buy-and-hold Plain Investor beat even the best 50% Accurate Investor. The Plain Investor was only worse than the worst performing coin flip strategy in 6% of rolling periods.
Beating buy-and-hold is hard to do reliably if you rely only on luck.
In this case, having a neutral hit rate with the negative skew of the sector equity returns leads to negative information coefficients. Taking more bets over time and across sectors did not help offset this distributional disadvantage.
So, let’s improve the accuracy slightly to see if the rolling results improve. Even with negative skew (-0.42 median value for the 30 sectors), an improvement in the accuracy to 60% is enough to bring the theoretical information coefficient back into the positive realm.
Source: Kenneth French Data Library. Calculations by Newfound Research. Past performance is not a guarantee of future results. All returns are hypothetical and backtested. Returns are gross of all fees. This does not reflect any investment strategy offered or managed by Newfound Research and was constructed exclusively for the purposes of this commentary. It is not possible to invest in an index.
The worst of these more skilled investors is now beating the Plain Investor in 41% of the rolling periods, and the best is losing to the buy-and-hold investor in 13% of the periods.
Going the other way, to a 40% accurate investor, we find that the best one was beaten by the Plain investor 93% of the time, and the worst one never beats the buy-and-hold investor.
Source: Kenneth French Data Library. Calculations by Newfound Research. Past performance is not a guarantee of future results. All returns are hypothetical and backtested. Returns are gross of all fees. This does not reflect any investment strategy offered or managed by Newfound Research and was constructed exclusively for the purposes of this commentary. It is not possible to invest in an index.
If we only require a modest increase in our accuracy to beat buy-and-hold strategies over shorter time horizons, why isn’t diligently focusing on increasing our accuracy an easy approach to success?
In order to increase our accuracy, we must first find a reliable way to do so: a task easier said than done due to the inherent nature of probability. Something having a 60% probability of being right does not preclude it from being wrong for a long time. The Law of Large Numbers can require larger numbers than our portfolios can stand.
Thus, even if we have found a way that will reliably lead to a 60% accuracy, we may not be able to establish confidence in that accuracy rate. This uncertainty in the accuracy can be unnerving. And it can cut both ways.
A strategy with a hit rate of less than 50% can masquerade as a more accurate strategy simply for lack of sufficient data to sniff out the true probability.
Source: Kenneth French Data Library. Calculations by Newfound Research. Past performance is not a guarantee of future results. All returns are hypothetical and backtested. Returns are gross of all fees. This does not reflect any investment strategy offered or managed by Newfound Research and was constructed exclusively for the purposes of this commentary. It is not possible to invest in an index.
You may think you have an edge when you do not. And if you do not have an edge, repeatedly applying it will lead to worse and worse outcomes.2
Accuracy Schmaccuracy
Our preference is to rely on systematic bets, which generally fall under the umbrella of factor investing. Even slight improvements to the accuracy can lead to better results when applied over a sufficient breadth of investments. Some of these factors also alter the distribution of returns (i.e. the skew) so that accuracy improvements have a larger impact.
Consider two popular measures of trend, used as the signals to determine the allocations in our 30 sector US equity strategy from the previous sections:
12-1 Momentum: We calculate the return over an 11-month period, starting one month ago to account for mean reversionary effects. If this number is positive, we hold the sector; if it is negative, we invest that capital at the risk-free rate.
10-month Simple Moving Average (SMA): We average the prices over the prior 10 months and compare that value to the current price. If the current price is greater than or equal to the average, we hold the sector; if it is less than, we invest that capital at the risk-free rate.
These strategies have volatilities in line with the Perfect and Anti-Perfect Investors and returns similar to the Plain Investor.
Using our measure of accuracy as correctly calling the direction of the sector returns over the subsequent month, it might come as a surprise that the accuracies for the 12-1 Momentum and 10-month SMA signals are only 42% and 41%, respectively.
Even with this low accuracy, the following chart shows that over the entire time period, the returns of these strategies more closely resemble those of the 55% Accurate Investor and have even looked like those of the 70% Accurate Investor over some time periods. What gives?
Source: Kenneth French Data Library. Calculations by Newfound Research. Past performance is not a guarantee of future results. All returns are hypothetical and backtested. Returns are gross of all fees. This does not reflect any investment strategy offered or managed by Newfound Research and was constructed exclusively for the purposes of this commentary. It is not possible to invest in an index.
This is an example of how addressing the negative skew in the underlying asset returns can offset a sacrifice in accuracy. These trend following strategies may have overall accuracy of less than 50%, but they have been historically right when it counts.
Consistently removing large negative returns – at the expense of giving up some large positive returns – is enough to generate a return profile that looks much like a strategy that picks sectors with above average accuracy.
Whether investors can stick with a strategy that exhibits below 50% accuracy, however, is another question entirely.
Conclusion
While most investors expect the proof to be in the eating of the pudding, in this commentary we demonstrate how luck can have a meaningful impact in the determination of whether skill exists. While skill should eventually differentiate itself from luck, the horizon over which it will do so may be far, far longer than most investors suspect.
To explore this idea, we construct portfolios comprised of all thirty industry groups. We then simulate the results of investors with known accuracy rates, comparing their outcomes to 100% Accuracy, 100% Inaccurate, and Buy-and-Hold benchmarks.
Perhaps somewhat counter-intuitively, we find that an investor exhibiting 50% accuracy would have fairly reliably underperformed a Buy-and-Hold Investor. This seems somewhat counter-intuitive until we acknowledge that equity returns have historically exhibit negative skew, with the left tail of their return distribution (“losses”) being longer and fatter than the right (“gains”). Combining a neutral hit rate with negative skew creates negative information coefficients.
To offset this negative skew, we require increased accuracy. Unfortunately, even in the case where an investor exhibits 60% accuracy, there are a significant number of 5-year periods where it might masquerade as a strategy with a much higher or lower hit-rate, inviting false conclusions.
This is all made somewhat more confusing when we consider that a strategy can have an accuracy rate below 50% and still be successful. Trend following strategies are a perfect example of this phenomenon. The positive skew that has been historically exhibited by these strategies means that frequently inaccurate trades of small magnitude are offset by infrequent, by very large accurate trades.
Yet if we measure success by short-term accuracy rates, we will almost certainly dismiss this type of strategy as one with no skill.
When taken together, this evidence suggests that not only might it be difficult for investors to meaningfully determine the difference between skill and luck over seemingly meaningful time horizons (e.g. 5 years), but also that short-term perceptions of accuracy can be woefully misleading for long-term success. Highly accurate strategies can still lead to catastrophe if there is significant negative skew lurking in the shadows (e.g. an ETF like XIV), while inaccurate strategies can be successful with enough positive skew (e.g. trend following).
Trend equity strategies seek to meaningfully participate with equity market growth while side-stepping significant and prolonged drawdowns. These strategies aim to achieve this goal by dynamically adjusting market exposure based upon trend-following signals.
A naïve example of such a strategy would be a portfolio that invests in U.S. equities when the prior 1-year return for U.S. equities is positive and divests entirely into short-term U.S. Treasuries when it is negative.
The Theory
This category of strategies relies upon the empirical evidence that performance tends to persist in the short-run: positive performance tends to beget further positive performance and negative performance tends to beget further negative performance. The theory behind the evidence is that behavioral biases exhibited by investors lead to the emergence of trends.
In an efficient market, changes in the underlying value of an investment should be met by an immediate, commensurate change in the price of that investment. The empirical evidence of trends suggests that investors may not be entirely efficient at processing new information. Behavioral theory (Figure I) suggests that investors anchor their views on prior beliefs, causing price to underreact to new information. As price continues to drift towards fair value, herding behavior occurs, causing price to overreact and extend beyond fair value. Combined, these effects cause a trend.
Trend equity strategies seek to capture this potential inefficiency by systematically investing in equities when they are exhibiting positively trending characteristics and divesting when they exhibit negative trends. The potential benefit of this approach is that it can try to exploit two sources of return: (1) the expected long-term risk premium associated with equities, and (2) the convex payoff structure typically associated with trend-following strategies.
The Positive Convexity of Trend Following
As shown in Figure II, we can see that a hypothetical implementation of this strategy on large-cap U.S. equities has historically exhibited a convex return profile with respect to the underlying U.S. equity index, meaningfully participating in positive return years while reducing exposure to significant loss years.
“Risk Cannot Be Destroyed, Only Transformed.”
While the flexibility of trend equity strategies gives them the opportunity to both protect and participate, it also creates the potential for losses due to “whipsaw.” Whipsaws occur when the strategy changes positioning due to what appears to be a change in trend, only to have the market rapidly reverse course. Such a scenario can lead to ”buy high, sell low” and “sell low, buy high” scenarios. These scenarios can be exacerbated by the fact that trend equity strategies may go several years without experiencing whipsaw to only then suddenly experience multiple back-to-back whipsaw events at once.
As Defensive Equity
The most obvious implementation of trend equity strategies is within a defensive equity sleeve. In this approach, an allocation for the strategy is funded by selling strategic equity exposure (see Figure III). Typically combined with other defensive styles (e.g. minimum volatility, quality, et cetera), the goal of a defensive equity sleeve is to provide meaningful upside exposure to equity market growth while reducing downside risk.
This implementation approach has the greatest potential to reduce a policy portfolio’s exposure to downside equity risk and therefore may be most appropriate for investors for whom ”failing fast” is a critical threat. For example, pre-retirees, early retirees, and institutions making consistent withdrawals are highly subject to sequence risk and large drawdowns within their portfolios can create significant impacts on portfolio sustainability.
The drawback of a defensive equity implementation is that vanilla trend equity strategies can, at best, keep up with their underlying index during strong bull markets (see Figure IV). Given the historical evidence that markets tend to be up more frequently than they are down, this can make this approach a frustrating one to stick with for investors. Furthermore, up-capture during bull markets can be volatile on a year-to-year basis, with low up-capture during whipsaw periods and strong up-capture during years with strong trends. Therefore, investors should only allocate in this manner if they plan to do so over a full market cycle.
Implementation within a Defensive Equity sleeve may also be a prudent approach with investors for whom their risk appetite is far below their risk capacity (or even need); i.e. investors who are chronically under-allocated to equity exposure. Implementation of a strategy that has the ability to pro-actively de-risk may allow investors to feel more comfortable with a larger exposure.
Finally, this approach may also be useful for investors seeking to put a significant amount of capital to work at once. While evidence suggests that lump-sum investing (“LSI”) almost always out-performs dollar cost averaging (”DCA”), investors may feel uncomfortable with the significant timing luck from LSI. One potential solution is to utilize trend equity as a middle ground; for example, investors could DCA but hold trend equity rather than cash.
Pros
Maintains overall strategic allocation policy.
May help risk-averse investors more confidently maintain an appropriate risk profile.
May provide meaningful reduction in exposure to significant and prolonged equity losses.
Cons
High year-to-year tracking error relative to traditional equity benchmarks.
Typically under-performs equities during prolonged bull markets (see Figure IV).
As a Tactical Pivot
One creative way of implementing a trend equity strategy is as a tactical pivot within a portfolio. In this implementation, an allocation to trend equity is funded by selling both stocks and bonds, typically in equal amounts (see Figure V). By implementing in this manner, the investor’s portfolio will pivot around the policy benchmark, being more aggressively allocated when trend equity is fully invested, and more defensively allocated when trend equity de-risks.
This approach is often appealing because it offers a highly intuitive allocation sizing policy. The size of the tactical pivot sleeve as well as the mixture of stocks and bonds used to fund the sleeve defines the tactical range around the strategic policy portfolio (see Figure VI).
One benefit of this implementation is that trend equity no longer needs to out-perform an equity benchmark to add value. Rather, so long as the strategy outperforms the mixture of stocks and bonds used to fund the allocation (e.g. a 50/50 mix), the strategy can add value to the holistic portfolio design. For example, assume a trend equity strategy only achieves an 80% upside capture to an equity benchmark during a given year. Implemented as a defensive equity allocation, this up-capture would create a drag on portfolio returns relative to the policy benchmark. If, however, trend equity is implemented as a tactical pivot – funded, for example, from a 50/50 mixture of stocks and bonds – then so long as it outperformed the funding mixture, the portfolio return is improved due to its tilt towards equities.
Implementation as a tactical pivot can also add potential value during environments where stocks and bonds exhibit positive correlations and negative returns (e.g. the 1970s).
One potential drawback of this approach is that the portfolio can be more aggressively allocated than the policy benchmark during periods of sudden and large declines. How great a risk this represents will be dictated both by the size of the tactical pivot as well as the ratio of stocks and bonds in the funding mixture. For example, the potential overweight towards equities is significantly lower using a 70/30 stock/bond funding mix than a 30/70 mixture. A larger allocation to bonds in the funding mixture creates a higher downside hurdle rate for trend equity to add value during a negative equity market environment.
Pros
Lower hurdle rate for strategy to add value to portfolio during positive equity environments.
Intuitive allocation policy based on desired level of tactical tilts within the portfolio.
May provide cushion in environments where stocks and bonds are positively correlated.
Cons
Portfolio may be allocated above benchmark policy to risky assets during a sudden market decline.
Higher hurdle rate for strategy to add value to portfolio during negative equity environments.
As a Liquid Alternative
Due to its historically convex return profile and potentially high level of tracking error exhibited over short measurement horizons, trend equity may also be a natural fit within a portfolio’s alternative sleeve. Indeed, when analyzed more thoroughly, trend equity shares many common traits with other traditionally alternative strategies.
There are even strong correlations to traditional alternative categories. For example, a significant driver of returns in equity hedge and long/short equity categories is dynamic market beta exposure, particularly during significant market declines (see Figure VII). Trend equity strategies that are implemented with factor-based equity exposures or with the flexibility to make sector and geographic tilts may even more closely approximate these categories.
One potential benefit of this approach is that trend equity can be implemented in a highly liquid, highly transparent, and cost-effective manner when compared against many traditional alternatives. Furthermore, by implementing trend equity within an alternatives sleeve, investors may give it a wider berth in their mental accounting of tracking error, allowing for a more sustainable allocation versus implementation as a defensive equity solution.
A drawback of this implementation, however, is that trend equity will increase a portfolio’s exposure to equity beta. Therefore, more traditional alternatives may offer better correlation- and pay-off-based diversification, especially during sudden and large negative equity shocks. Furthermore, trend equity may lead to overlapping exposures with existing alternative exposures such as equity long/short or managed futures. Investors must therefore carefully consider how trend equity may fit into an already existing alternative sleeve.
Calculations by Newfound Research. Results are hypothetical. Returns are gross of all fees, including manager fees, transaction costs, and taxes.
Tactical Portable Beta
By Corey Hoffstein
On May 6, 2019
In Carry, Portfolio Construction, Risk & Style Premia, Term, Trend, Value, Weekly Commentary
This post is available as a PDF download here.
Summary
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,
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).