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Is Managed Futures Value-able?

By

On June 19, 2023

In Craftsmanship, Inflation, Portfolio Construction, Trend, Value

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.

What Is Managed Futures?

By

On February 2, 2023

In Craftsmanship, Portfolio Construction, Risk Management, Trend

Summary

  • Much like in 2008, managed futures as an investment strategy had an impressive year in 2022. With most traditional asset classes struggling to navigate the inflationary macroeconomic environment, managed futures has been drawing interest as a potential diversifier.
  • Managed futures is a hedge fund category that uses futures contracts as their primary investment vehicle. Managed futures managers can engage in many different investment strategies, but trend following is the most common.
  • Trend following as an investment strategy has a substantial amount of empirical evidence promoting its efficacy as an investment strategy. There also exist several behavioral arguments for why this anomaly exists, and why we might expect it to continue.
  • As a diversifier, multi-asset trend following has provided diversification benefits when compared to both stocks and bonds. Additionally, trend following has posted positive returns in the four major drawdowns in equities since 2000.

Cut short your losses, and let your winners run. – David Ricardo, 1838

What is Managed Futures?

Managed futures is a hedge fund category originating in the 1980s, named for the ability to trade (both long and short) global equity, bond, commodity, and currency futures contracts. Today, these strategies have been made available to investors in both mutual fund and ETF wrappers. The predominate strategy of most managed futures managers is trend following, so much so, that the terms are often used synonymously.

While trend following is by far the largest and most pronounced strategy in the category, it is not the only strategy used in the space.1 Managed futures can engage in trend following, momentum trading, mean reversion, carry-focused strategies, relative value trading, macro driven strategies, or any combination thereof. Any individual managed futures manager may have a certain bias towards one of the strategies, though, trend following is by far the most utilized strategy of the group2.

Figure 1: The Taxonomy of Managed Futures

Adapted from Kaminski (2014). The most common characteristics are highlighted in orange.

What is Trend Following?

Simply put, trend following is a strategy that buys (‘goes long’) assets that have been rising in price and sells (‘goes short’) assets that have been decreasing in price, based on the premise that this trend will continue. The precise method of measuring trends varies widely, but each primarily relies on the difference between an asset’s price today and the price of the same asset previously. Some common methods of measuring trends include total return measurements, moving averages, and regression lines. These different approaches are all mathematically linked, and empirical evidence does not suggest that one method is necessarily better than another3.

Trend following has a rich history in financial markets, with centuries of evidence supporting the idea that markets tend to trend. The obvious question to then ask is: why? The past few decades of academic research has focused on explaining theories such as the Efficient Market Hypothesis and research into explanatory market factors (such as value and size), diminishing the amount of research being conducted on trend following.

Figure 2: The Life Cycle of a Trend

Adapted from AQR. For illustrative purposes only.

The classification of trend following as an anomaly, however, has not left it without theories for why it works. There are a number of generally accepted explanations for why trend following works, and more importantly, why the anomaly might continue to persist.

Anchoring Bias: When new data enters the marketplace, investors can overly rely on historical data, thereby underreacting to the new information. This can be seen in Figure 3 where, after the catalyst of new information enters the market, the price of a security will directionally follow the fair value of the asset, but not with a large enough magnitude to match the fair value precisely.

Disposition Effect: Investors have a tendency to take gains on their winning positions too early and hold onto their losing positions too long.

Herding: After a noticeable trend has been established, investors “bandwagon” into the trade, prolonging the directional trend, and potentially pushing the price past the asset’s fair value4.

Confirmation Bias: Investors tend to ignore information that is contrary to an their beliefs. A positive (or negative) signal will be ignored if the investor has a differing view, extending the time frame for the convergence of an asset’s price to its fair value.

Rational Inattention Bias: Investors cannot immediately digest all information due to a lack of information processing resources (or mental capacity). Consequently, prices move towards fair value more slowly as the information is processed by all investors.

As previously mentioned, methodologies may vary widely when analyzing an asset’s trend, but the general theme is to view an asset’s current price relative to some measure of its recent history. For example, one common example of this is to observe an asset’s current price versus its 200-day moving average: initiating a long position when the price is above its moving average or a short position when it is below. Extending Figure 2, we can graphically depict the trade cycle attempting to take advantage of such a trend.

Figure 3: The Life Cycle of a Trade

Source: Newfound Research, AQR. For illustrative purposes only

Of course, using such an idealized description of a trend is not typically what is found in the market, which leads to many false-starts, The risk-management decisions made to reduce the impact of these false-starts begins to highlight part of the attractiveness of the strategy as a diversifier.

Consider that the fair value of an asset is generally never known with a high degree of certainty. A trend following manager is thus reliant on the perceived direction of trend at any given time, and so, must make choices based on how the trend evolves or not.

Figure 4: Heads I Trend, Tails I Don’t

Adapted from Michael Covel. For illustrative purposes only.

When the model indicates that a trend has formed, the manager will initiate a position in the direction of the indicated trend (either short or long – blue line in Figure 4). As long as the trend continues, the strategy will hold that position, and only exit when the signal indicates that the trend no longer exists. At that time, the manager will remove the position, potentially taking the opposite position5.

The second case (red line in Figure 4) is one in which the trend reverses shortly after a position has been initiated. After establishing a position in the asset, the price of the asset reverts to its previous levels, possibly completely reversing in direction. In such a case, the signal will indicate that the trend no longer exists and recommend that the position be removed.

Historically, by quickly cutting losers and letting winning trades run, trend following has created a positively skewed return profile. Managed futures strategies tend to trade many different markets and underlying assets. This minimizes the impact of trends being rejected but may increase the probability of taking a position in an asset that has an outlier trend occurring that might be out of the scope of a traditional portfolio.

Kaminski (2014) refers to this characteristic as divergent risk taking6, where a divergent investor “profess[es] their own ignorance to the true structure of potential risks/benefits with some level of skepticism for what is knowable or is not dependable”.

This divergent risk behavior results in a positively skewed return distribution by not risking too much on a trade, removing the position if it goes against you, and allowing a trade to run if it is winning7.

The structural nature of trend following minimizes the size of any bets taken, and quickly eliminates a position if the bet is not paying off. By diversifying across many markets, asset classes, and economic goods, while maintaining sensible positions without directional bias, the strategy maintains staying power by not swinging for the fences and staying with a time-proven approach8, in a well-diversified manner.

Using Managed Futures as A Diversifier

The traditional investor portfolio has typically been dominated by two assets: stocks and bonds. In recent history, investors have even been able to use fixed income to buffer equity risk as high-quality bonds have exhibited flight-to-safety characteristics in times of extreme market turmoil. In the first two decades of the 2000s, this pairing has worked extremely well given that interest rates declined over the period, inflation remained low, and the bonds were resilient during the fallout of the tech bubble and the Great Financial Crisis.

In Figure 5, we chart the relationship between the year-over-year Consumer Price Index for All Urban Consumers (“CPIAUCSL”) versus the 12-month correlation between U.S. Stocks and 10-Year U.S. Treasuries9. We can see that negative correlation is most pronounced when inflation is low. Positive correlation regimes, on the other hand, have historically occurred in all realized ranges of CPI changes, the most striking occurring when inflation was extraordinarily high.

Figure 5: The Relationship Between Inflation and Equity-Bond Correlation

Source: FRED, Kenneth French Data Library, Tiingo. For illustrative purposes only.

Since trend following can hold both long and short positions, it has the potential to trade price trends in  assets in any direction that may emerge from increasing inflation risks.   This is highlighted by the performance of trend following in 2022, where the year-to-date real returns of U.S. equities10, 10-Year U.S. Treasuries, and the SG CTA Trend Index as of December 31, 2022 , were -19.5%, -16.5%, and +27.4%, respectively.  During 2022, trend following strategies were generally long the U.S. Dollar, short fixed income securities, and short equity indices. Additionally, the managers tended to hold mixed positions in the commodity space, taking long and short positions in the individual commodity contracts exhibiting both positive and negative trends.

Importantly, the dynamics exhibited throughout different economic regimes (such as monetary inflation vs supply/demand inflation) will unfold differently, so positions that were profitable in 2022 will likely not be the same in all environments. Trend following as a strategy, is dynamic in nature, and will adjust positioning as trends emerge and fade, regardless of the economic regime.

In addition to historically providing a ballast in inflationary regimes, one of managed futures’ claims to fame stems from the strategy’s ability to provide negative correlation in times of financial stress, specifically, in equity crises. The net result of including an allocation to trend following strategies during these periods has been a reduction in portfolio drawdowns and portfolio volatility.

Though managed futures have been in existence since the 1980’s, the strategy garnered its popularity coming out of the Great Financial Crisis, as it was one of the few investment strategies to provide a positive return. While this event shot the strategy to prominence, it was not an isolated incident. In fact, this relationship has been repeated frequently throughout history.

Table 1 shows the cumulative nominal returns of stocks, bonds, and managed futures when the equity market realized a greater-than 20% drawdown.

Table 1: Nominal Return of Equities, Bonds, and Managed Futures During Equity Crises

Source: FRED, Kenneth French Data Library, BarclayHedge. Calculations by Newfound Research. Time period is based on data availability. Performance is gross of all costs (including, but not limited to, advisor fees, manager fees, taxes, and transaction costs) unless explicitly stated otherwise Past performance is not a reliable indicator of future performance.

Since the inception of the SG CTA Trend Index11, bonds have provided diversification benefits in three of the four large drawdowns. 2022, however, was the first period in which inflation has been a concern in the market, and U.S. Treasuries were insufficient to reduce risk in a traditional portfolio.

We can see, though, that the SG CTA Trend Index provided similar diversification benefits during the drawdowns in the first two decades of the century, but also proved capable while inflation shocks rose to prominence in 2022.

Figure 6: Performance From 1999 to 2022

Source: BarclayHedge, Tiingo. 60/40 Portfolio is the Vanguard Balance Index Fund (“VBINX”) and returns presented are net of the management fee of the fund. Time period is based on data availability. Performance is gross of all costs (including, but not limited to, advisor fees, manager fees, taxes, and transaction costs) unless explicitly stated otherwise. Past performance is not a reliable indicator of future performance.

Conclusion

Traditional portfolios consisting of equity and fixed income exposure have enjoyed two decades of strong performance due to favorable economic tailwinds. With the changing economic regime and uncertainty facing markets ahead, however, investors have begun searching for potential additions to their portfolios to protect against inflation and to provide diversifying exposure to other macroeconomic headwinds.

Trend following as a strategy has extensive empirical evidence supporting both its standalone performance, as well as the diversifying benefits in relation to traditional asset classes such as stocks and bonds. In addition, trend following is mechanically convex in that it can provide positive returns in both bull and bear markets.

Managed futures is a strong contender as an addition to a stock-and-bond heavy portfolio. Finding its roots in the 1980s, the strategy has a tenured history in the investment landscape with a demonstrated history of providing diversifying exposure in times of equity crisis.

In this paper, we have shown that trend following is a robust trading strategy with behavioral underpinnings, suggesting that the strategy has staying power in the long-run, as well as desirable characteristics due to the mechanical nature of the strategy.

As a potential addition to a traditional investment portfolio, managed futures provides a source of diversification beyond that of mainstream asset classes, as well as strong absolute returns on a standalone basis.

APPENDIX A: TREND FOLLOWING AS AN OPTIONS STRADDLE

A trend following strategy can benefit from both positive and negative price trends. If prices are increasing, then a long position can be initiated; if prices are decreasing, then a short position can be initiated. Said differently: a trend following strategy can potentially profit from both increases or decreases in price.

This characteristic is immediately reminiscent of a long position in an option straddle, where a put and call option are purchased with the same strike price. This option position would, thereby, benefit if the price moves largely either positive or negative12.

Figure A1: Long Straddle Payoff Profile

Source: Newfound Research. For illustrative purposes only.

Empirically, these strategies have in fact performed remarkably similar. To illustrate this, we will create two simple strategies.

The first strategy is a simple trend following strategy that takes a long position in the S&P 500 when its prior 12-month return is positive, and a short position when its negative.

The second strategy will attempt to replicate the delta-position of a straddle expiring in one month, struck at the close price of the S&P 500 twelve months ago. We then compute the delta of this position using the Black-Scholes model13 and take a position in the S&P 500 equal to the computed delta. For example, if the price of the S&P 500 12-months ago was $3,000, we would calculate the delta of a straddle struck at $3,000. Since the delta of this position will range between -1 and 1, the strategy will use this as an allocation to the S&P 500.

Figure A2: Replicating Trend Following with Straddles

Source: Tiingo. Calculations by Newfound Research. Returns assume the reinvestment of all dividends. The S&P 500 is represented by the Vanguard 500 Index Fund Investor Shares (“VFINX”). For illustrative purposes only. Past performance is not a reliable indicator of future performance.

For both strategies, we will assume that any excess capital is held in cash, returning 0%. Figure A2 plots the growth of $1 invested in each strategy.

As we can see, the option strategy and the trend following strategy provide a roughly equivalent return profile. In fact, if we compare the quarterly returns of the two strategies to the S&P 500, an important pattern emerges. Both strategies exhibit convex relationships in relation to the S&P 500.

Figure A3: Trend Following Relationship to the Underlying

Source: Newfound Research. For illustrative purposes only.

Figure A4: Straddle Replication Relationship to the Underlying

Source: Newfound Research. For illustrative purposes only.

APPENDIX B: Index Definitions

U.S. Stocks: U.S. total equity market return data from Kenneth French Library. 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.

10-Year U.S. Treasuries: The 10-Year U.S. Treasury index is a constant maturity index calculated by assuming that a 10-year bond is purchased at the beginning of every month and sold at the end of that month to purchase a new bond at par at the beginning of the next month. You cannot invest directly in an index, and unmanaged index returns do not reflect any fees, expenses, or sales charges. The referenced index is shown for general market comparison and is not meant to represent any Newfound index or strategy. Data for 10-Year U.S. Treasury yields come from the Federal Reserve of St. Louis economic database (“FRED”).

SG Trend Index:  The SG Trend Index is designed to track the largest 10 (by AUM) CTAs and be representative of the managed futures trend-following space.

 

Payoff Diversification

By

On February 10, 2020

In Portfolio Construction, Risk Management, Weekly Commentary

This post is available as a PDF download here.

Summary

  • At Newfound, we adopt a holistic view of diversification that encompasses not only what we invest in, but also how and when we make those investment decisions.
  • In this three-dimensional perspective, what is correlation-based, how is payoff-based, and when is opportunity-based.
  • In this piece, we provide an example of what we mean by payoff-based diversification, using a simple strategically rebalanced portfolio and a naïve momentum strategy.
  • We find that the strategically rebalanced portfolio exhibits a payoff structure that is concave in nature whereas the momentum-based approach exhibits a convex profile.
  • By combining the two approaches – being careful in how we size positions – we can develop a portfolio that is less sensitive to the co-movement of underlying assets.

At Newfound, we embrace a holistic view of diversification that covers not just what we invest in, but also how and when we make those decisions.  What is the diversification most investors are well-versed in and covers traditional, correlation-based diversification between securities, assets, macroeconomic factors, and geographic regions.

We identify when as “opportunity diversification” because it captures the opportunities that are available when we make investment decisions.  This often goes overlooked in public markets (which is why we spend so much time writing about rebalance timing luck) but is well acknowledged in private markets where investors often allocate to multiple fund “vintages” to create diversification.

How is generally easy to understand, but sometimes difficult to visualize.  We call it “payoff diversification” to acknowledge that when viewed through he appropriate lens, every investment style creates a particular shape.  For example, when the return of a call option is plotted against the return of the underlying security, it generates a hockey-stick-like payoff profile.

In this short research note, we are going to demonstrate the payoff profiles of a strategically allocated portfolio and a naïve momentum strategy.  We will then show that by combining these two approaches we can create a portfolio that exhibits significantly less sensitivity to the co-movement of underlying assets.

The Payoff Profile of a Strategic Portfolio

Few investors consider a strategically allocated portfolio to be an active strategy.  And it isn’t; at least not until we introduce rebalancing.  Once we institute a process to systematically returning our drifted weights back to their original fixed mix, we create a strategy and a corresponding payoff profile.

But what does this payoff profile look like?  As an example, consider a U.S. 60/40 portfolio comprised of broad U.S. equities and a constant maturity 10-year U.S. Treasury index.  If equities out-perform bonds, our equity allocation will increase and our bond allocation will decrease.  If equities continue to out-perform bonds, we will benefit relative to our original policy weights.  Similarly, if equities under-perform bonds, then our relative equity allocation will decrease.  Again, should they continue to underperform, we are well positioned.

However, if we were to rebalance back to our original 60/40 allocation, we would eliminate the opportunity to benefit from the continuation of the relative performance.

On the other hand, consider the case where equities out-perform, our relative allocation to equities increases due to drift, and then equities subsequently under-perform.  Now allowing drift has hurt us and we would have been better off rebalancing.

We can visualize this relationship by plotting the return spread between stocks and bonds (x-axis) versus the return spread between a monthly-rebalanced portfolio and a buy-and-hold (drifted) approach (y-axis) over rolling 1-year periods.

Source: Kenneth French Data Library; Federal Reserve Bank of St. Louis.  Calculations by Newfound Research.  Returns are hypothetical and assume the reinvestment of all distributions.  Returns are gross of all fees, including, but not limited to, management fees, transaction fees, and taxes. The 60/40 portfolio is comprised of a 60% allocation to broad U.S. equities and a 40% allocation to a constant maturity 10-Year U.S. Treasury index.  The rebalanced variation is rebalanced at the end of each month whereas the buy-and-hold variation is allowed to drift over the 1-year period.  The 10-Year U.S. Treasuries index is a constant maturity index calculated by assuming a 10-year bond is purchased at the beginning of every month and sold at the end of that month to purchase a new bond at par at the beginning of the next month. You cannot invest directly in an index and unmanaged index returns do not reflect any fees, expenses or sales charges. Past performance is not indicative of future results. 

What we can see is a concave payoff function. When equities significantly out-perform bonds (far right side of the graph), the rebalanced portfolio under-performs the drifted portfolio.  Similarly, when bonds significantly out-perform equities (far left side of the graph), the rebalanced portfolio under-performs the drifted portfolio.  When the return spread between stocks and bonds is small– a case likely to be more indicative of mean-reversion than positive autocorrelation in the spread – we can see that rebalancing actually generates a positive return versus the drifted portfolio.

Those versed in options will note that this payoff looks incredibly similar to a 1-year strangle sold on the spread between stocks and bonds and struck at 0%.  The seller captures the premium when the realized spread remains small but loses money when the spread is more extreme.

The Payoff Profile of Naïve Momentum Following

We can now take the exact same approach to evaluating the payoff profile of a naïve momentum strategy.  Each month, the strategy will simply invest in either stocks or bonds based upon whichever had the highest trailing 12-month return

As this approach is explicitly trying to capture auto-correlation in the return spread between stocks and bonds, we would expect to see almost mirror behavior to the payoff profile we saw with strategic rebalancing.

Source: Kenneth French Data Library; Federal Reserve Bank of St. Louis.  Calculations by Newfound Research.  Returns are hypothetical and assume the reinvestment of all distributions.  Returns are gross of all fees, including, but not limited to, management fees, transaction fees, and taxes. The 60/40 portfolio is comprised of a 60% allocation to broad U.S. equities and a 40% allocation to a constant maturity 10-Year U.S. Treasury index.  The momentum portfolio is rebalanced monthly and selects the asset with the highest prior 12-month returns whereas the buy-and-hold variation is allowed to drift over the 1-year period.  The 10-Year U.S. Treasuries index is a constant maturity index calculated by assuming a 10-year bond is purchased at the beginning of every month and sold at the end of that month to purchase a new bond at par at the beginning of the next month. You cannot invest directly in an index and unmanaged index returns do not reflect any fees, expenses or sales charges. Past performance is not indicative of future results. 

While the profile may not be as tidy as before, we can see a convex payoff profile that tends to profit when the return spread is more extreme and lose money when the spread is narrow.  Again, those familiar with options will recognize this as similar to the payoff of a 1-year straddle based upon the return spread between stocks and bonds.  The buyer pays a premium but captures the spread when it is extreme.

Note, however, the scale of the y-axis.  Whereas the payoff profile for the rebalanced portfolio was between -3.0% and +2.0%, the payoff profile for this momentum approach is much larger, ranging between -30.0% and 40.0%.

Creating Payoff Diversification

We have seen that whether we strategically rebalance or adopt a momentum-based approach, both approaches create a payoff profile that is sensitive to the return spread in underlying assets.  But what if we do not want to take such a specific payoff bet?  One simple answer is diversification.

If we allocate to both the strategically rebalanced portfolio and the naïve momentum portfolio, we will realize both their payoff profiles simultaneously.  As their profiles are close mirrors of one another, we may be able to achieve a more neutral outcome.

We have to be careful, however, as to size the allocations appropriate.  Recall that the payoff profile of the strategically rebalanced portfolio was approximately 1/10th the size of the naïve momentum strategy.  For both profiles to contribute equally, we would want to allocate approximately 90% of our capital to the strategic rebalancing strategy and 10% of our capital to the momentum strategy.

Below we plot the payoff structure of such a mix.

Source: Kenneth French Data Library; Federal Reserve Bank of St. Louis.  Calculations by Newfound Research.  Returns are hypothetical and assume the reinvestment of all distributions.  Returns are gross of all fees, including, but not limited to, management fees, transaction fees, and taxes. The 60/40 portfolio is comprised of a 60% allocation to broad U.S. equities and a 40% allocation to a constant maturity 10-Year U.S. Treasury index.  The mixed portfolio is rebalanced monthly and is a 90% allocation to a rebalanced 60/40 and a 10% allocation to a naïve momentum strategy; whereas the buy-and-hold variation is allowed to drift over the 1-year period.  The 10-Year U.S. Treasuries index is a constant maturity index calculated by assuming a 10-year bond is purchased at the beginning of every month and sold at the end of that month to purchase a new bond at par at the beginning of the next month. You cannot invest directly in an index and unmanaged index returns do not reflect any fees, expenses or sales charges. Past performance is not indicative of future results. 

We can see that diversifying how we make decisions results in a payoff structure that is far more neutral to the co-movement of underlying securities in the portfolio.  The holy grail, of course, is not just to find strategies whose combination neutralizes sensitivity to the spread in returns, but actually creates a higher likelihood of positive outcomes in all environments.

Conclusion

In this research note, we aimed to provide greater insight into the idea of payoff diversification, the how in our what-how-when diversification framework.  To do so, we explored two simple examples: a strategically rebalanced 60/40 allocation and a naïve momentum strategy.

We found that the strategically rebalanced portfolio generates a payoff profile that is convex with respect to the spread in returns between stocks and bonds.  In general, the larger the spread, the more likely that rebalancing generates a negative return versus a buy-and-hold approach.  Conversely, the smaller the spread, the more likely that rebalancing generates a positive return.

The naïve momentum strategy – which simply bought the asset with the greatest prior 12-month returns – exhibited a convex profile.  When the return spread between stocks and bonds was large, the naïve momentum strategy was more likely to out-perform buy-and-hold.  Conversely, when the return spread was small, the naïve momentum strategy tended to under-perform.

Importantly, the magnitudes of the payoffs are significantly different, with the naïve momentum strategy generating returns nearly 10x larger than strategic rebalancing in the tails.  This difference has important implications for strategy sizing, and we find a portfolio mixture of 90% strategic rebalancing and 10% naïve momentum does a reasonably good job of neutralizing portfolio payoff sensitivity to the spread in stock and bond returns.

Diversification: More Than “What”

By

On November 25, 2019

In Risk Management, Weekly Commentary

Not seeing the video?  Click here.

 

Ensemble Multi-Asset Momentum

By

On July 22, 2019

In Craftsmanship, Momentum, Popular, Risk & Style Premia, Risk Management, Weekly Commentary

This post is available as a PDF download here.

Summary­

  • We explore a representative multi-asset momentum model that is similar to many bank-based indexes behind structured products and market-linked CDs.
  • With a monthly rebalance cycle, we find substantial timing luck risk.
  • Using the same basic framework, we build a simple ensemble approach, diversifying both process and rebalance timing risk.
  • We find that the virtual strategy-of-strategies is able to harvest diversification benefits, realizing a top-quartile Sharpe ratio with a bottom-quartile maximum drawdown.

Early in the 2010s, a suite of index-linked products came to market that raised billions of dollars.  These products – offered by just about every major bank – sought to simultaneously exploit the diversification benefits of modern portfolio theory and the potential for excess returns from the momentum anomaly.

While each index has its own bells and whistles, they generally follow the same approach:

  • A global, multi-asset universe covering equities, fixed income, and commodities.
  • Implemented using highly liquid ETFs.
  • Asset class and position-level allocation limits.
  • A monthly rebalance schedule.
  • A portfolio optimization that seeks to maximize weighted prior returns (e.g. prior 6 month returns) while limiting portfolio volatility to some maximum threshold (e.g. 5%).

And despite their differences, we can see in plotting their returns below that these indices generally share a common return pattern, indicating a common, driving style.

Source: Bloomberg.

Frequent readers will know that “monthly rebalance” is an immediate red flag for us here at Newfound: an indicator that timing luck is likely lurking nearby.

Replicating Multi-Asset Momentum

To test the impact of timing luck, we replicate a simple multi-asset momentum strategy based upon available index descriptions.

We rebalance the portfolio at the end of each month.  Our optimization process seeks to identify the portfolio with a realized volatility less than 5% that would have maximized returns over the prior six months, subject to a number of position and asset-level limits.  If the 5% volatility target is not achievable, the target is increased by 1% until a portfolio can be constructed that satisfies our constraints.

We use the following ETFs and asset class limits:

As a naïve test for timing luck, rather than assuming the index rebalances at the end of each month, we will simply assume the index rebalances every 21 trading days. In doing so, we can construct 21 different variations of the index, each representing the results from selecting a different rebalance date.

Source: CSI Analytics; Calculations by Newfound Research.  Results are backtested and hypothetical.  Results assume the reinvestment of all distributions.  Results are gross of all fees, including, but not limited to, manager fees, transaction costs, and taxes, with the exception of underlying ETF expense ratios.  Past performance is not an indicator of future results. 

As expected, the choice of rebalance date has a meaningful impact.  Annualized returns range from 4.7% to 5.5%, Sharpe ratios range from 0.6 to 0.9, and maximum drawdowns range from 9.9% to 20.8%.

On a year-by-year basis, the only thing that is consistent is the large spread between the worst and best-performing rebalance date.  On average, the yearly spread exceeds 400 basis points.

Min

Max

2008*

-9.91%

0.85%

2009

2.36%

4.59%

2010

6.46%

9.65%

2011

3.31%

10.15%

2012

6.76%

10.83%

2013

3.42%

6.13%

2014

5.98%

10.60%

2015

-5.93%

-2.51%

2016

4.18%

8.45%

2017

9.60%

11.62%

2018

-6.00%

-2.53%

2019 YTD

5.93%

10.01%

* Partial year starting 7/22/2018

We’ve said it in the past and we’ll say it again: timing luck can be the difference between hired and fired.  And while we’d rather be on the side of good luck, the lack of control means we’d rather just avoid this risk all together.

If it isn’t nailed down for a reason, diversify it

The choice of when to rebalance is certainly not the only free variable of our multi-asset momentum strategy.  Without an explicit view as to why a choice is made, our preference is always to diversify so as to avoid specification risk.

We will leave the constraints (e.g. volatility target and weight constraints) well enough alone in this example, but we should consider the process by which we’re measuring past returns as well as the horizon over which we’re measuring it.  There is plenty of historical efficacy to using prior 6-month total returns for momentum, but no lack of evidence supporting other lookback horizons or measurements.

Therefore, we will use three models of momentum: prior total return, the distance of price from its moving average, and the distance of a short-term moving average from a longer-term moving average.  We will vary the parameterization of these signals to cover horizons ranging from 3- to 15-months in length.

We will also vary which day of the month the portfolio rebalances on.

By varying the signal, the lookback horizon, and the rebalance date, we can generate hundreds of different portfolios, all supported by the same theoretical evidence but having slightly different realized results due to their particular specification.

Our robust portfolio emerges by calculating the weights for all these different variations and averaging them together, in many ways creating a virtual strategy-of-strategies.

Below we plot the result of this –ensemble approach– as compared to a –random sample of the underlying specifications–.  We can see that while there are specifications that do much better, there are also those that do much worse.  By employing an ensemble approach, we forgo the opportunity for good luck and avoid the risk of bad luck.   Along the way, though, we may pick up some diversification benefits: the Sharpe ratio of the ensemble approach fell in the top quartile of specifications and its maximum drawdown was in the bottom quartile (i.e. lower drawdown).

Source: CSI Analytics; Calculations by Newfound Research.  Results are backtested and hypothetical.  Results assume the reinvestment of all distributions.  Results are gross of all fees, including, but not limited to, manager fees, transaction costs, and taxes, with the exception of underlying ETF expense ratios.  Past performance is not an indicator of future results.

Conclusion

In this commentary, we again demonstrate the potential risk of needless specification and the potential power of diversification.

Using a popular multi-asset momentum model as our example, we again find a significant amount of timing luck lurking in a monthly rebalance specification.  By building a virtual strategy-of-strategies, we are able to manage this risk by partially rebalancing our portfolio on different days.

We go a step further, acknowledging that processrepresents another axis of risk. Specifically, we vary both how we measure momentum and the horizon over which it is measured.  Through the variation of rebalance days, model specifications, and lookback horizons, we generate over 500 different strategy specifications and combine them into a virtual strategy-of-strategies to generate our robust multi-asset momentum model.

As with prior commentaries, we find that the robust model is able to effectively reduce the risk of both specification and timing luck.  But perhaps most importantly, it was able to harvest the benefits of diversification, realizing a Sharpe ratio in the top quartile of specifications and a maximum drawdown in the lowest quartile.

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