In Return StackingTM: Strategies for Overcoming a Low Return Environment, we advocated for the addition of managed futures to traditionally allocated portfolios. We argued that managed futures’ low empirical correlation to both equities and bonds and its historically positive average returns makes it an attractive diversifier. More specifically, we recommended implementing managed futures as an overlay to a portfolio to avoid sacrificing exposure to core stocks and bonds.
The luxury of writing research is that we work in a “clean slate” environment. In the real world, however, investors and allocators must contemplate changes in the context of their existing portfolios. Investors rarely just hold pure beta exposure, and we must consider, therefore, not only how a managed futures overlay might interact with stocks and bonds, but also how it might interact with existing active tilts.
The most common portfolio tilt we see is towards value stocks (and, often, quality-screened value). With this in mind, we want to briefly explore whether stacking managed futures remains attractive in the presence of an existing value tilt.
Diversifying Value
If we are already allocated to value, one of our first concerns might be whether an allocation to managed futures actually provides a diversifying return stream. One of our primary arguments for including managed futures into a traditional stock/bond portfolio is its potential to hedge against inflationary pressures. However, there are arguments that value stocks do much of the same, acting as “low duration” stocks compared to their growth peers. For example, in 2022, the Russell 1000 Value outperformed the broader Russell 1000 by 1,145 basis points, offering a significant buoy during the throes of the largest bout of inflation volatility in recent history.
However, broader empirical evidence does not actually support the narrative that value hedges inflation (see, e.g., Baltussen, et al. (2022), Investing in Deflation, Inflation, and Stagflation Regimes) and we can see in Figure 1 that the long-term empirical correlations between managed futures and value is near-zero.
(Note that when we measure value in this piece, we will look at the returns of long-only value strategies minus the returns of broad equities to isolate the impact of the value tilt. As we recently wrote, a long-only value tilt can be effectively thought as long exposure to the market plus a portfolio that is long the over-weight positions and short the under-weight positions1. By subtracting the market return from long-only value, we isolate the returns of the active bets the tilt is actually taking.)
Figure 1: Excess Return Correlation
Source: Kenneth French Data Library, BarclayHedge. Calculations by Newfound Research. Performance is backtested and hypothetical. Performance is gross of all costs (including, but not limited to, advisor fees, manager fees, taxes, and transaction costs) unless explicitly stated otherwise. Performance assumes the reinvestment of all dividends. Past performance is not indicative of future results. See Appendix A for index definitions.
Correlations, however, do not tell us about the tails. Therefore, we might also ask, “how have managed futures performed historically conditional upon value being in a drawdown?” As the past decade has shown, underperformance of value-oriented strategies relative to the broad market can make sticking to the strategy equally difficult.
Figure 2 shows the performance of the various value tilts as well as managed futures during periods when the value tilts realized a 10% or greater drawdown2.
Figure 2: Value Relative Drawdowns Greater than 10%
Source: Kenneth French Data Library, BarclayHedge. Calculations by Newfound Research. Performance is backtested and hypothetical. Performance is gross of all costs (including, but not limited to, advisor fees, manager fees, taxes, and transaction costs) unless explicitly stated otherwise. Performance assumes the reinvestment of all dividends. Past performance is not indicative of future results. See Appendix A for index definitions.
We can see that while managed futures may not have explicitly hedged the drawdown in value, its performance remained largely independent and accretive to the portfolio as a whole.
To drive the point of independence home, we can calculate the univariate regression coefficients between value implementations and managed futures. We find that the relationship between the strategies is statistically insignificant in almost all cases. Figure 3 shows the results of such a regression.
Figure 3: Univariate Regression Coefficients
Source: Kenneth French Data Library, BarclayHedge. Calculations by Newfound Research. *, **, and *** indicate statistical significance at the 0.05, 0.01, and 0.001 level. Performance is backtested and hypothetical. Performance is gross of all costs (including, but not limited to, advisor fees, manager fees, taxes, and transaction costs) unless explicitly stated otherwise. Performance assumes the reinvestment of all dividends. Past performance is not indicative of future results. See Appendix A for index definitions.
But How Much?
As our previous figures demonstrate, managed futures has historically provided a positively diversifying benefit in relation to value; but how can we thoughtfully integrate an overlay into an portfolio that wants to retain an existing value tilt?
To find a robust solution to this question, we can employ simulation techniques. Specifically, we block bootstrap 100,000 ten-year simulated returns from three-month blocks to find the robust information ratios and MAR ratios (CAGR divided by maximum drawdown) of the value-tilt strategies when paired with managed futures.
Figure 4 shows the information ratio frontier of these portfolios, and Figure 5 shows the MAR ratio frontiers.
Figure 4: Information Ratio Frontier
Source: Kenneth French Data Library, BarclayHedge. Calculations by Newfound Research. Performance is backtested and hypothetical. Performance is gross of all costs (including, but not limited to, advisor fees, manager fees, taxes, and transaction costs) unless explicitly stated otherwise. Performance assumes the reinvestment of all dividends. Past performance is not indicative of future results. See Appendix A for index definitions.
Figure 5: MAR Ratio Frontier
Source: Kenneth French Data Library, BarclayHedge. Calculations by Newfound Research. Performance is backtested and hypothetical. Performance is gross of all costs (including, but not limited to, advisor fees, manager fees, taxes, and transaction costs) unless explicitly stated otherwise. Performance assumes the reinvestment of all dividends. Past performance is not indicative of future results. See Appendix A for index definitions.
Under both metrics it becomes clear that a 100% tilt to either value or managed futures is not prudent. In fact, the optimal mix, as measured by either the Information Ratio or MAR Ratio, appears to be consistently around the 40/60 mark. Figure 6 shows the blends of value and managed futures that maximizes both metrics.
Figure 6: Max Information and MAR Ratios
Source: Kenneth French Data Library, BarclayHedge. Calculations by Newfound Research. Performance is backtested and hypothetical. Performance is gross of all costs (including, but not limited to, advisor fees, manager fees, taxes, and transaction costs) unless explicitly stated otherwise. Performance assumes the reinvestment of all dividends. Past performance is not indicative of future results. See Appendix A for index definitions.
In Figure 7 we plot the backtest of a 40% value / 60% managed futures portfolio for the different value implementations.
Figure 7: 40/60 Portfolios of Long/Short Value and Managed Futures
Source: Kenneth French Data Library, BarclayHedge. Calculations by Newfound Research. Performance is backtested and hypothetical. Performance is gross of all costs (including, but not limited to, advisor fees, manager fees, taxes, and transaction costs) unless explicitly stated otherwise. Performance assumes the reinvestment of all dividends. Past performance is not indicative of future results. See Appendix A for index definitions.
These numbers suggest that an investor who currently tilts their equity exposure towards value may be better off by only tilting a portion of their equity towards value and introducing a managed futures overlay onto their portfolio. For example, if an investor has a 60% stock and 40% bond portfolio and the 60% stock exposure is currently all value, they might consider moving 36% of it into passive equity exposure and introducing a 36% managed futures overlay.
Depending on how averse a client is to tracking error, we can plot how the tracking error changes depending on the degree of portfolio tilt. Figure 8 shows the estimated tracking error when introducing varying allocations to the 40/60 value/managed futures overlay.
Figure 8: Relationship between Value/Managed Futures Tilt and Tracking Error
Source: Kenneth French Data Library, BarclayHedge. Calculations by Newfound Research. Performance is backtested and hypothetical. Performance is gross of all costs (including, but not limited to, advisor fees, manager fees, taxes, and transaction costs) unless explicitly stated otherwise. Performance assumes the reinvestment of all dividends. Past performance is not indicative of future results. See Appendix A for index definitions.
For example, if we wanted to implement a tilt to a quality value strategy, but wanted a maximum tracking error of 3%, the portfolio might add an approximate allocation of 46% to the 40/60 value/managed futures overlay. In other words, 18% of their equity should be put into quality-value stocks and a 28% overlay to managed futures should be introduced.
Using the same example of a 60% equity / 40% bond portfolio as before, the 3% tracking error portfolio would hold 42% in passive equities, 18% in quality-value, 40% in bonds, and 28% in a managed futures overlay.
What About Other Factors?
At this point, it should be of no surprise that these results extend to the other popular equity factors. Figures 8 and 9 show the efficient information ratio and MAR ratio frontiers when we view portfolios tilted towards the Profitability, Momentum, Size, and Investment factors.
Figure 9: Information Ratio Frontier for Profitability, Momentum, Size, and Investment Tilts
Source: Kenneth French Data Library, BarclayHedge. Calculations by Newfound Research. Performance is backtested and hypothetical. Performance is gross of all costs (including, but not limited to, advisor fees, manager fees, taxes, and transaction costs) unless explicitly stated otherwise. Performance assumes the reinvestment of all dividends. Past performance is not indicative of future results. See Appendix A for index definitions.
Figure 10: MAR Ratio Frontier for Profitability, Momentum, Size, and Investment Tilts
Source: Kenneth French Data Library, BarclayHedge. Calculations by Newfound Research. Performance is backtested and hypothetical. Performance is gross of all costs (including, but not limited to, advisor fees, manager fees, taxes, and transaction costs) unless explicitly stated otherwise. Performance assumes the reinvestment of all dividends. Past performance is not indicative of future results. See Appendix A for index definitions.
Figure 11: Max Information and MAR Ratios for Profitability, Momentum, Size, and Investment Tilts
Source: Kenneth French Data Library, BarclayHedge. Calculations by Newfound Research. Performance is backtested and hypothetical. Performance is gross of all costs (including, but not limited to, advisor fees, manager fees, taxes, and transaction costs) unless explicitly stated otherwise. Performance assumes the reinvestment of all dividends. Past performance is not indicative of future results. See Appendix A for index definitions.
Once again, a 40/60 split emerges as a surprisingly robust solution, suggesting that managed futures has historically offered a unique, diversifying return to all equity factors.
Conclusion
Our analysis highlights the considerations surrounding the use of managed futures as a complement to a traditional portfolio with a value tilt. While value investing remains justifiably popular in real-world portfolios, our findings indicate that managed futures can offer a diversifying return stream that complements such strategies. The potential for managed futures to act as a hedge against inflationary pressures, while also offering a diversifying exposure during relative value drawdowns, strengthens our advocacy for their inclusion through a return stackingTM framework.
Our examination of the correlation between managed futures and value reveals a near-zero relationship, suggesting that managed futures can provide distinct benefits beyond those offered by a value-oriented approach alone. Moreover, our analysis demonstrates that a more conservative tilt to value, coupled with managed futures, may be a prudent choice for inverse to tracking error. This combination offers the potential to navigate unfavorable market environments and potentially holds more of a portfolio benefit than a singular focus on value.
Appendix A: Index Definitions
Book to Market – Equal-Weighted HiBM Returns for U.S. Equities (Kenneth French Data Library)
Profitability – Equal-Weighted HiOP Returns for U.S. Equities (Kenneth French Data Library)
Momentum – Equal-Weighted Hi PRIOR Returns for U.S. Equities (Kenneth French Data Library)
Size – Equal-Weighted SIZE Lo 30 Returns for U.S. Equities (Kenneth French Data Library)
Investment – Equal-Weighted INV Lo 30 Returns for U.S. Equities (Kenneth French Data Library)
Earnings Yield – Equal-Weighted E/P Hi 10 Returns for U.S. Equities (Kenneth French Data Library)
Cash Flow Yield – Equal-Weighted CF/P Hi 10 Returns for U.S. Equities (Kenneth French Data Library)
Dividend Yield – Equal-Weighted D/P Hi 10 Returns for U.S. Equities (Kenneth French Data Library)
Quality Value – Equal-Weighted blend of BIG HiBM HiOP, ME2 BM4 OP3, ME2 BM3 OP3, and ME2 BM3 OP4 Returns for U.S. Equities (Kenneth French Data Library)
Value Blend – An equal-weighted Returns of Book to Market, Earnings Yield, Cash Flow Yield, and Dividend Yield returns for U.S. Equities (Kenneth French Data Library)
Passive Equities (Market, Mkt) – U.S. total equity market return data from Kenneth French Library.
Managed Futures – BTOP50 Index (BarclayHedge). The BTOP50 Index seeks to replicate the overall composition of the managed futures industry with regard to trading style and overall market exposure. The BTOP50 employs a top-down approach in selecting its constituents. The largest investable trading advisor programs, as measured by assets under management, are selected for inclusion in the BTOP50. In each calendar year the selected trading advisors represent, in aggregate, no less than 50% of the investable assets of the Barclay CTA Universe.
Global Growth-Trend Timing
By Steven Braun
On November 4, 2019
In Portfolio Construction, Trend, Weekly Commentary
This post is available as a PDF download here.
Summary
We apologize in advance, as this commentary will be fairly graph- and table-heavy.
We have written fairly extensively on the topic of factor-timing in the past, and much of the success has been proven to be both hard to implement and recreate out of sample.
One of the inherent pains of trend following is the existence of whipsaws, or more precisely, the misidentification of perceived market trends, which turn out to be more noise than signal. An article from Philosophical Economics proposed using several economic indicators to tune down the noise that might affect price-driven signals such as trend following. Generally, this strategy imposed an overlay that turned trend following “on” when the change in the economic indicators were negative year-over-year signaling a higher likelihood of recession, and conversely, adopted a buy-and-hold stance when the economic indicators were not flashing warning lights.
This strategy presents a certain appeal as leading economic indicators may, as their name implies, lead the market for some time until capital preservation is warranted. Switching to a trend-following approach may allow a strategy to continue to participate in market appreciation while it lasts. On the other hand, using economic confirmation as a filter may help a strategy avoid the whipsaw costs generated from noisy market dips while positive economic conditions persist.
In an effort to test such a strategy out-of-sample, we took the approach global, hoping to capture a broader cross-section of economic and market environments.
First, we will consider trend following with no timing using the economic indicators.1
Below we plot the equity curves for Australia, Germany, Italy, Japan, Singapore, the United Kingdom, and the United States, alongside a strategy that is long the market when the market is above the trailing twelve-month average (“12 Month average”) and steps to cash when the price is below it. The ratio between the two is also included to show the relative cumulative performance between the trend strategy and the respective market. An increasing ratio means that the trend following strategy is adding value over buy-and-hold.
Source: MSCI, Global Financial Data. Calculations by Newfound Research. Past performance is not an indicator of future results. Performance is backtested and hypothetical. Performance figures are gross of all fees, including, but not limited to, manager fees, transaction costs, and taxes. Performance assumes the reinvestment of all distributions.
Through the graphs above, it becomes clear that much of the trend premium is realized by avoiding the large, prolonged bear markets that tend to occur during economic distress. In between these periods, however, the trend strategy lags the market. It makes sense, then, that a potential improvement to this strategy would be to implement an augmentation that could better distinguish between real price break-outs and those that lead to a whipsaw in the portfolio.
Growth-Trend Timing
For each country, we look at a number of economic indicators, including: corporate earnings growth, employment, housing starts, industrial production, and retail sales growth.2 The strategy then followed the same rules as described above: if the economic indicator in question displays a negative percentage change over the previous twelve-month period, a position is taken in a trend following strategy utilizing a twelve-month moving average signal. Otherwise, a buy-and-hold position is established.
To ensure that we are not benefitting from look-ahead bias, a lag of three months was imposed on each of the economic indicators, as it would be unrealistic to assume that the economic levels would be known at the end of each month.
Unfortunately, some of the economic data points could not be found for the entire period in which prices are available, though the analysis can still prove beneficial by indicating what economic regimes trend following is benefitted by growth-trend timing, or the potential identification where one indicator may work when another does not.3
In the charts below, we plot the growth-trend timing (referred to as GTT for the remainder of this commentary) for each country utilizing the available signals. The charts represent the relative cumulative performance over the respective country’s market return. For example, when the lines remain flat, the GTT approach has adopted buy-and-hold exposure and therefore matches the respective market’s returns. Any changes in the ratios are due to the GTT strategy investing in the trend following strategy.
Source: MSCI, Global Financial Data, St. Louis Fed, Bloomberg. Calculations by Newfound Research. Past performance is not an indicator of future results. Performance is backtested and hypothetical. Performance figures are gross of all fees, including, but not limited to, manager fees, transaction costs, and taxes. Performance assumes the reinvestment of all distributions.
What we see from the above figures is a mixed bag of results.
The overlay of economic indicators was by far successful in the mitigation of whipsaw losses, as each country reaped the benefits of being primarily long the market during bull markets. As the 12-month moving average strategy tended to slowly give up a portion of the gains realized from severe market environments, the majority of the GTT strategies remained relatively stagnant until the next major correction.
There are some instances, however, where the indicator was late to the economic party. It is worth remembering that the market is, in theory, a forward-looking measure, and therefore sudden economic shocks may not be captured in economic data as quickly as it is in market returns. This created cases where the strategy either missed the chance to be out of the market during a correction or was sitting on the sidelines during the subsequent recoveries. Notably, the employment signal in Australia, Italy, Singapore, and the United Kingdom tended to be a poor leading indicator as the strategy tended to be invested longer in the bear markets than the trend strategy.
A Candidate for Ensembling
The implicit assumption in the analysis above is that the included indicators behave in similar ways. For example, by using a twelve-month lookback period for the indicators, we are assuming that each indicator will begin to trend in roughly the same way.
That may not be a particularly fair assumption. Whereas housing starts and retail sales are generally considered leading indicators, employment (unemployment) rates are normally categorized as lagging indicators. For this reason, it may be more beneficial to use a shorter lookback period so as to pick up on potential problems in the economy as they begin to present themselves. Further, some signals tend to be more erratic than others, suggesting that a meaningful lookback period for one indicator may not be meaningful for another. With no perfect reason to prefer one lookback over another, we might consider different lookback periods so as to diversify any specification risk that may exist within the strategy.
With the benefit of hindsight, we know that not all recessions occur for the same reasons, so being reliant on one signal that has worked in the past may not be as beneficial in the future. With this in mind, we should consider that all indicators hold some information as to the state of the economy since one indicator may be signaling the all-clear while another may be flashing warning lights.
For the same reason medical professionals take multiple readings to gain insight into the state of the body, we should also consider any available signals to ascertain the health of the economy.
To ensemble this strategy, we will vary the lookbacks from six to eighteen months, while holding the lag at three months, as well as combine the available economic signals for each country. For the sake of brevity, we will hold the trend-following strategy the same with a twelve-month moving average.
Remember, if the economic signal is negative, it does not mean that we are immediately out of the market: a negative economic signal simply moves the strategy into a trend-following approach. With 5 economic indicators and 13 lookback periods, we have 65 possible strategies for each country. As an example, if 40 of these 65 models were positive and 25 were negative, we would hold 62% in the market and 38% in the trend following strategy.
The resulting performance statistics can be seen in the table below.
Source: MSCI, Global Financial Data, St. Louis Fed, Bloomberg. Calculations by Newfound Research. Past performance is not an indicator of future results. Performance is backtested and hypothetical. Performance figures are gross of all fees, including, but not limited to, manager fees, transaction costs, and taxes. Performance assumes the reinvestment of all distributions.
From the table above, we see that there are, again, mixed results. One country that particularly stands out is Italy in that the sign on its return flipped to negative and the drawdown was actually deeper with GTT than with a simple buy-and-hold strategy.
Source: MSCI, Global Financial Data, St. Louis Fed, Bloomberg. Calculations by Newfound Research. Past performance is not an indicator of future results. Performance is backtested and hypothetical. Performance figures are gross of all fees, including, but not limited to, manager fees, transaction costs, and taxes. Performance assumes the reinvestment of all distributions.
Digging deeper, it appears that the GTT strategy for Italy was actually whipsawed by more than just trend-following. Housing start data for Italy was not readily available until December 2008, so Italy may have been at a relative disadvantage when compared against the other countries. Since the reliable data we could find begins at the end of 2008 and the majority of the whipsaw losses occur post-Great Financial Crisis, we can run the analysis again, but with housing start data being added in upon its availability.
Source: MSCI, Global Financial Data, St. Louis Fed, Bloomberg. Calculations by Newfound Research. Past performance is not an indicator of future results. Performance is backtested and hypothetical. Performance figures are gross of all fees, including, but not limited to, manager fees, transaction costs, and taxes. Performance assumes the reinvestment of all distributions.
Adding housing starts in as an indicator did not meaningfully alter the results over the period. One hypothesis is that the indicators included could not fully encapsulate the complex state of Italy’s economy over the period. Italy has weathered three technical recessions over the past decade, so this could be a regime where the market is looking to sources outside the country for indications of distress or where the economic indicator is not reflective of the pressures driving the market.
Source: MSCI, St. Louis Fed. Calculations by Newfound Research. Past performance is not an indicator of future results. Performance is backtested and hypothetical. Performance figures are gross of all fees, including, but not limited to, manager fees, transaction costs, and taxes. Performance assumes the reinvestment of all distributions.
Above, we can see several divergences between the market movement and changes in real GDP. Specifically, in the past decade, we see that the market reacted to information that didn’t materialize in the country’s real GDP. More likely, the market was reacting to regional financial distress driven by debt concerns.
The MSCI Italy index is currently composed of 24 constituents with multinational business operations. Additionally, the index maintains large concentrations in financials, utilities, and energy: 33%, 25%, and 14%, respectively.4 Because of this sector concentration, utilizing the economic indicators may overly focus on the economic health of Italy while ignoring external factors such as energy prices or broader financial distress that could be swaying the market needle.
A parallel explanation could be that the Eurozone is entangled enough that signals could be interfering with each other between countries. Further research could seek to disaggregate signals between the Eurozone and the member-countries, attempting to differentiate between zone, regional, and country signals to ascertain further meaning.
Additionally, economic indicators are influenced by both the private and public sector so this could represent a disconnect between public company health and private company health.
Conclusion
In this commentary, we sought to answer the question, “can we improve trend-following by drawing information from a country’s economy”. It intuitively makes sense that an investor would generally opt for remaining in the market unless there are systemic issues that may lead to market distress. A strategy that successfully differentiates between market choppiness and periods of potential recession would drastically mitigate any losses incurred from whipsaw, thereby capturing a majority of the equity premium as well as the trend premium.
We find that growth-trend timing has been relatively successful in countries such as the United States, Germany, and Japan. However, the country that is being analyzed should be considered in light of their specific circumstances.
Peeking under the hood of Italy, it becomes clear that market movements may be influenced by more than a country’s implicit economic health. In such a case, we should pause and ask ourselves whether a macroeconomic indicator is truly reflective of that country’s economy or if there are other market forces pulling the strings.