Category: Risk & Style Premia Page 1 of 16

Is Managed Futures Value-able?

On June 19, 2023

In Craftsmanship, Inflation, Portfolio Construction, Trend, Value

In Return Stacking^TM: 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%

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

Figure 5: MAR Ratio Frontier

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

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

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

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

Figure 10: MAR Ratio Frontier for Profitability, Momentum, Size, and Investment Tilts

Figure 11: Max Information and MAR Ratios for Profitability, Momentum, Size, and Investment Tilts

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 stacking^TM 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.

Index Funds Reimagined?

By Corey Hoffstein

On May 19, 2023

In Craftsmanship, Portfolio Construction, Value

I recently had the privilege to serve as a discussant at the Democratize Quant 2023 conference to review Research Affliates’s new paper, Reimagining Index Funds. The post below is a summary of my presentation.

Introduction

In Reimagining Index Funds (Arnott, Brightman, Liu and Nguyen 2023), the authors propose a new methodology for forming an index fund, designed to avoid the “buy high, sell low” behavior that can emerge in traditional index funds while retaining the depth of liquidity and capacity. Specifically, they propose selecting securities based upon the underlying “economic footprint” of the business.

By using fundamental measures of size, the authors argue that the index will not be subject to sentiment-driven turnover. In other words, it will avoid those additions and deletions that have primarily been driven by changes in valuation rather than changes in fundamentals. Furthermore, the index will not arbitrarily avoid securities due to committee bias. The authors estimate that total turnover is reduced by 20%.

The added benefit to this approach, the authors further argue, is that index trading costs are actually quite large. While well-telegraphed additions and deletions allow index fund managers to execute market-on-close orders and keep their tracking error low, it also allows other market participants to front run these changes. The authors’ research suggests that these hidden costs could be upwards of 20 basis points per year, creating a meaningful source of negative alpha.

Methodology & Results

The proposed index construction methodology is fairly simple:

Footnote #3 in the paper further expands upon the four fundamental measures:

The results of this rather simple approach are impressive.

Tracking error to the S&P 500 comparable to that of the Russell 1000.
Lower turnover than the S&P 500 or the Russell 1000.
Statistically meaningful Fama-French-Carhart 4-Factor alpha.

But What Is It?

One of the most curious results of the paper is that despite having a stated value tilt, the realized value factor loading in the Fama-French-Carhart regression is almost non-existent. This might suggest that the alpha emerges from avoiding the telegraphed front-running of index additions and deletions.

However, many equity quants may notice familiar patterns in the cumulative alpha streams of the strategies. Specifically, the early years look similar to the results we would expect from a value tilt, whereas the latter years look similar to the results we might expect from a growth tilt.

With far less rigor, we can create a strategy that holds the Russell 1000 Value for the first half of the time period and switches to the Russell 1000 Growth for the second half. Plotting that strategy versus the Russell 1000 results in a very familiar return pattern. Futhermore, such a strategy would load positively on the value factor for the first half of its life and negatively for the second half of its life, leading a full-period factor regression to conclude zero exposure.

But how could such a dynamic emerge from such a simple strategy?

“Economic Footprint” is a Multi-Factor Tilt

The Economic Footprint variable is described as being an equal-weight metric of four fundamental measures: book value, sales, cash flow, and dividends, all measured as a percentage of all publicly-traded U.S. listed companies. With a little math (inspired by this presentation from Cliff Asness), we will show that Economic Footprint is actually a mutli-factor screen on both Value and Market-Capitalization.

Define the weight of a security in the market-capitalization weighted index as its market capitalization divided by the total market capitalization of the universe.

If we divide both sides of the Economic Footprint equation by the weight of the security, we find:Some subtle re-arrangements leave us with: The value tilt effectively looks at each security’s value metric (e.g. book-to-price) relative to the aggregate market’s value metric. When the metric is cheaper, the value tilt will be above 1; when the metric is more expensive, the value tilt will be less than 1. This value tilt then effectively scales the market capitalization weight.

Importantly, economic footprint does not break the link to market capitalization.

Breaking economic footprint into two constituent parts allows us to get a visual intuition as to how the strategy operates.

In the graphs below, I take the largest 1000 U.S. companies by market capitalization and plot them based upon their market capitalization weight (x-axis) and their value tilt (y-axis).

(To be clear, I have no doubt that my value tilt scores are precisely wrong if compared against Research Affiliates’s, but I have no doubt they are directionally correct. Furthermore, the precision does not change the logic of the forthcoming argument.)

If we were constructing a capitalization weighted index of the top 500 companies, the dots would be bisected vertically.

As a multi-factor tilt, however, economic footprint leads to a diagonal bisection.

The difference between these two graphs tells us what we are buying and what we are selling in the strategy relative to the naive capitalization-weighted benchmark.

We can clearly see that the strategy sells larg(er) glamour stocks and buys small(er) value stocks. In fact, by definition, all the stocks bought will be both (1) smaller and (2) “more value” and any of the stocks sold.

This is, definitionally, a size-value tilt. Why, then, are the factor loadings for size and value so small?

The Crucial Third Step

Recall the third step of the investment methodology: after selecting the companies by economic footprint, they are re-weighted by their market capitalization. Now consider an important fact we stated above: every company we screen out is, by definition, larger than any company we buy.

That means, in aggregate, the cohort we screen out will have a larger aggregate market cap than the cohort we buy.

Which further means that the cohort we don’t screen out will, definitionally, become proportionally larger.

For example, at the end of April 2023, I estimate that screening on economic footprint would lead to the sale of a cohort of securities with an aggregate market capitalization of $4 trillion and the purchase of a cohort of securities with an aggregate market capitalization of $1.3 trillion.

The cohort that remains – which was $39.5 trillion in aggregate market capitalization – would grow proportionally from being 91% of the underlying benchmark to 97% of our new index. Mega-cap growth names like Amazon, Google, Microsfot, and Apple would actually get larger based upon this methodology, increasing their collective weights by 120 basis points.

Just as importantly, this overweight to mega-cap tech would be a persistent artifact throughout the 2010s, suggesting why the relative returns may have looked like a growth tilt.

Why Value in 1999?

How, then, does the strategy create value-like results in the dot-com bubble? The answer appears to lie in two important variables:

What percentage of the capitalization-weighted index is being replaced?
How strongly do the remaining securities lean into a value tilt?

Consider the scatter graph below, which estimates how the strategy may have looked in 1999. We can see that 40% of the capitalization-weighted benchmark is being screened out, and 64% of the securities that remain have a positive value tilt. (Note that these figures are based upon numerical count; it would likely be more informative to measure these figures weighted by market capitalization.)

By comparison, in 2023 only 20% of the underlying benchmark names are replaced and of the securities that remain, just 30% have a tilt towards value. These graphics suggest that while a screen on economic footprint creates a definitive size/value tilt, the re-weighting based upon relative market capitalization can lead to dynamic style drift over time.

Conclusion

The authors propose a new approach to index construction that aims to maintain a low tracking error to traditional capitalization-weighted benchmarks, reduce turnover costs, and avoid “buy high, sell low” behavior. By selecting securities based upon the economic footprint of their respective businesses, the authors find that they are able to produce meaningful Fama-French-Carhart four-factor alpha while reducing portfolio turnover by 20%.

In this post I find that economic footprint is, as defined by the authors, actually a multi-factor tilt based value and market capitalization. By screening for companies with a high economic footprint, the proposed method introduces a value and size tilt relative to the underlying market capitalization weighted benchmark.

However, the third step of the proposed process, which then re-weights the selected securities based upon their relative market capitalization, will always increase the weight of the securities of the benchmark that were not screened out. This step creates the potential for meaningful style drift within the strategy over time.

I would argue the reason the factor regression exhibited little-to-no loading on value is that the strategy exhibited a positive value tilt over the first half of its lifetime and a negative value tilt over the second half, effectively cancelling out when evaluated over the full period. The alpha that emerges, then, may actually be style timing alpha.

While the authors argue that their construction methodology should lead to the avoidance of “buy high, sell low” behavior, I would argue that the third step of the investment process has the potential to lead to just that (or, at the very least, buy high). We can clearly see that in certain environments, portfolio construction choices can actually swamp intended factor bets.

Whether this methodology actually provides a useful form of style timing, or whether it is an unintended bet in the process that lead to a fortunate, positive ex-post result is an exercise left to other researchers.

What Is Managed Futures?

By Steven Braun

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.

Rebalance Timing Luck: The (Dumb) Luck of Smart Beta

By Corey Hoffstein

On August 14, 2020

In Defensive, Momentum, News, Risk & Style Premia, Value

We are proud to announce the release of our newest paper, Rebalance Timing Luck: The (Dumb) Luck of Smart Beta.

Abstract

Prior research and empirical investment results have shown that portfolio construction choices related to rebalance schedules may have non-trivial impacts on realized performance. We construct long-only indices that provide exposures to popular U.S. equity factors (value, size, momentum, quality, and low volatility) and vary their rebalance schedules to isolate the effects of “rebalance timing luck.” Our constructed indices exhibit high levels of rebalance timing luck, often exceeding 100 basis points annualized, with total impact dependent upon the frequency of rebalancing, portfolio concentration, and the nature of the underlying strategy. As a case study, we replicate popular factor-based index funds and similarly find meaningful performance impacts due to rebalance timing luck. For example, a strategy replicating the S&P Enhanced Value index saw calendar year return differentials above 40% strictly due to the rebalance schedule implemented. Our results suggest substantial problems for analyzing any investment when the strategy, its peer group, or its benchmark is susceptible to performance impacts driven by the choice of rebalance schedule.

Heads I Win, Tails I Hedge

By Corey Hoffstein

On July 6, 2020

In Risk & Style Premia, Risk Management, Weekly Commentary

This post is available as a PDF download here.

Summary

For hedging strategies, there is often a trade-off between degree, certainty, and cost.
Put options have high certainty and typically offer a high degree of protection, making them costly to hold and roll over the long run.
In this note, we briefly explore the application of different tactical signals to a 9-month, 25-delta rolling put strategy in an effort to reduce long-term costs.
We find that signals based upon volatility appear to perform significantly better than signals based upon price changes, likely due, at least in part, to the nature of the put we are purchasing, which has significant sensitivity to changes in implied volatilities.
These results must be taken with a significant grain of salt due to the low number of actual crisis events to measure against. Furthermore, these results are not applicable for investors for whom a certain degree of loss would be disruptive to their financial plan or needs (e.g. impacting withdrawal / spending plans or forcing the liquidation of assets). For other investors, however, the tactical application of put options may represent an interesting pay-off profile.

In managing risk, there are three primary trade-offs to consider: degree, cost, and certainty.

Degree measures how much protection we are looking too get. Rather than thinking of degree as how much of our portfolio we’re looking to protect (e.g. 10% vs 100% of our notional exposure), we want to think of this more in terms of the loss level we want the protection to begin at. For example, degree captures whether we want to protect against all losses or just losses greater than 30%.

Cost captures how much we must pay for our protection. This cost can be explicit (i.e. we pay a known, up-front premium) or implicit (e.g. whipsaw cost in trend following).

Finally, certainty captures how reliable the hedge is. A centrally cleared put option, for example, has a very high degree of certainty. Buying a call option on Treasury bonds (perhaps to benefit from the materialization of a flight-to-safety trade or as a bet on Fed policy during a crisis) carries with it some basis risk if our primary goal is to protect against equity losses.

Like many trade-offs in life, this is one of those “pick two” cases. You can have a high degree of protection with high reliability, but it will cost you a lot. If you want to reduce the cost, you’ll need to either reduce the degree of protection or the certainty.

Rather than trying to find the holy grail of high degree, high certainty, and low cost, our time is likely better spent first considering the axis by which we are constrained. For example, if a 50% loss represents a catastrophic outcome (e.g. impacting withdrawal / spending plans and potentially having knock-on effects in creating forced asset sales), then we can seek to maximize certainty and minimize cost for this specific scenario. On the other hand, if we cannot afford to spend more than 300 basis points a year on risk management, then we can try to maximize degree and certainty for that budget.

Put options, by definition, have a high degree of certainty, and therefore tend to carry a fairly substantial cost. For example, below we plot the return of a put option strategy that rolls 9-month, 25-delta puts each month, purchasing enough puts to cover 100% of the S&P 500.

Source: DiscountOptionsData.com. Calculations by Newfound Research. Returns are hypothetical and backtested. Returns are gross of all fees including, but not limited to, management fees, transaction fees, and taxes. Returns assume the reinvestment of all distributions.

Despite offering meaningful returns during the 2008 economic crisis and the recent March 2020 COVID-19 panic, this strategy has still lost -2.3% annualized.

To be fair, this is a very naïve tail hedging strategy. There are no considerations for generating offsetting carry (e.g. a put ratio approach), pro-active monetization, trade conversion (e.g. puts to put spreads), reasonable basis risk trades, or exchanging between linear and non-linear trades.

And it may not be wholly fair to evaluate the returns of a tail risk strategy in isolation. After all, it may help increase the geometric returns of an equity portfolio substantially if appropriately rebalanced.

Nevertheless, this example highlights that if we want to combine a high degree of protection with certainty, it should carry relatively high cost.

In this commentary we will explore a few ideas for dynamically employing put options, attempting to maintain relatively high certainty while minimizing cost.

Tactical Signals

Using tactical signals to identify when to buy put options is akin to waiting to smell smoke before calling your agent to buy fire insurance. It may save significant cost over the long run, but you risk failing to have protection in periods where you cannot get to the phone fast enough or by the time that you do, the cost of insurance is prohibitive.

Nevertheless, in cases where a tail hedge is not necessary (i.e. true knock-out conditions) but simply preferred, tactical tail hedging may provide an attractive payoff.

Below we explore a variety of signals which may indicate elevated risk going forward. At the core of our approach will be the 9-month 25-delta put strategy we introduced above. For each of our signals, when the signal indicates rising risk, we will buy into the put strategy. Otherwise, we will assume a 0% return cash position.

It should be stressed that this is a rather general approach to what can be a highly specific problem for allocators. By rolling far-dated puts each month, our strategy will have exhibit substantial convexity to changes in implied volatility, whereas a short-dated put would exhibit greater convexity to changes in the S&P 500 itself. This means that our approach may not be suitable for protecting against slow, tepid market declines.

Fortunately, market declines and changes in volatility have historically exhibited significant negative correlation. Therefore, for large and rapid declines, we can generally expect the value of our long-dated, deep out-of-the-money puts to appreciate significantly.

Given that our options will be highly sensitive to changes in implied volatility, we explore signals that are not only potentially related to losses in U.S. equities, but also appreciation of expected volatility.

Indicator	Measure	Thesis
S&P 500 Returns	63-Day Return	Negative returns in the S&P 500 may forecast negative returns going forward.
S&P 500 Returns	Z-Score of 63-Day Return (126-Day)	Below average returns in the S&P 500 may forecast negative returns going forward.
S&P 500 Trend	30×120 EWMA	Negative trend signals in the S&P 500 may forecast negative returns going forward.
1M IV	63-Day Change	Increasing implied volatility may be a sign that investors believe risk is increasing.
1M RV	63-Day Change	Increasing realized volatility may be a sign that volatility will increase in the future.
1M RV – IV	63-Day Change	If realized volatility is increasing beyond implied volatility, it may be a sign that protection is underpriced.
1M – 3-Month RV	63-Day Change	If short-term volatility is higher than medium-term volatility, it may be a sign that risk is increasing.
Skew (1M 25 Delta Put – Call)	63-Day Change	If the skew of the volatility curve is increasing, it may be a sign that investor demand for protection has gone up.
Short Volatility Strategy	63-Day Return	If the return of a short volatility strategy is negative, it may be a sign that risk is increasing.
High Yield Credit Spreads	63-Day Change	If markets are demanding an increasing premium for credit risk, it may be a sign that economic risk is increasing.

Why would we expect tactical signals to work? The core thesis is partially behavioral and partially structural. On the behavioral side, we expect investors to first under- and then over-react to regime changes in the market. Ideally tactical signals can cue us into these changes before the herd catches on, and then we can benefit as the herd reprices markets.

From a less irrational perspective, we expect investors to exhibit “knock-out” conditions whereby they become forced sellers. For example, as prices fall and volatility picks up, collateral requirements may go up. This can cause forced de-leveraging, further driving down prices and further driving up collateral requirements. This type of positive feedback loop can create liquidity and credit spirals in markets. Therefore, by buying protection at the early signs of a potential market dislocation, we can potentially protect ourselves from the non-economically driven behavior of other market participants.

Note that we focus on fairly short measurement periods. This is for two reasons. First, risk can reprice rapidly, so we want to make sure. Secondly, put options allow us to explicitly measure, per day, how much we’ll pay in premium for the non-linear payoff we are purchasing. This massively asymmetric payoff profile means that we may be able to afford more false positives, unlike trend following where our capital may be meaningfully eroded by whipsaw or jump risk.

Below we plot the returns of applying each signal. When a signal indicates heightened risk (e.g. increasing volatility or declining prices), we purchase the put strategy index. We tranche positions over a 20-trading-day period, meaning that if a signal stays constant, we’ll increase our position by 5% a day. If a signal turns on and then immediately off, we’ll carry at least a 5% position for 20 trading days.

Source: DiscountOptionsData.com; Tiingo.com; St. Louis Federal Reserve. Calculations by Newfound Research. Returns are hypothetical and backtested. Returns are gross of all fees including, but not limited to, management fees, transaction fees, and taxes. Returns assume the reinvestment of all distributions.

We can see that all of the approaches significantly cut down on the premium paid for protection. The “worst” performing strategy – the 63-day return z-score – had a loss of -1.0% annualized compared to the -2.3% for the constant put strategy.

Of course, just sitting in cash the entire time would have reduced the cost. The question we should ask is: how much did we forego in protection?

Below we plot the performance of these approaches over several of the larger market loss scenarios over the last 15 years.

We can see that the volatility-based models (e.g. changes in 1M IV, RV, RV – IV, and Skew) tend to do a fairly consistent job their up-capture, whereas performance-based measures on the S&P 500 (e.g. 63-day returns or 30×120 EWMA) are much less consistent. This is particularly apparent in the recent COVID-19 crisis, where return-based signals were too delayed. Interestingly, this lower upside capture was not met with decreased cost: the return-based signals were some of the worst performing models. Only the high yield credit spread model seemed to offer a balanced trade-off.

Interestingly, signals derived from a short-volatility strategy were negative in 2008. In this strategy we are short an at-the-money call and put. Calling this strategy short-volatility may be a bit of a misnomer, as it will profit when realized returns stay range-bound, which is different than explicitly generating a return from declining volatility. Nevertheless, we can see that the return profile of this approach, plotted below, looks very much like “picking up pennies in front of a steam roller.” Unfortunately, the steam roller seems to manifest rather quickly, so the 63-day return signal may be too slow in this case.

Conclusion

Given their high certainty and degree of protection offered, put options can be prohibitively expensive (particularly after a significant market decline, when demand for protection often goes up). For investors for whom a certain level of loss is truly disruptive to operations or creates a knock-out condition, protection is not an option. For others, though, the selective use of put options may provide an interesting, diversifying payoff profile.

In this commentary, we briefly explored the application of different tactical signals to a far-dated, deep out-of-the-money put strategy. Not surprisingly, we found that all of the approaches helped reduce the annualized cost of the put strategy. However, not all of the signals provided meaningful upside capture. Given that there are few actual periods where the put strategy offers positive returns, missing these gains defeats the whole purpose of the exercise.

We found that volatility-based signals worked best. This may be due to a combination of two facts: (1) the put strategy has meaningful sensitivity to changes in implied volatility, and (2) the put strategy has an asymmetric payoff profile, reducing the cost of false positives.

These results should taken with a large grain of salt, however, as the number of meaningful payoff periods is very low. Future research should explore how these signals work when applied to different equity indices, ETFs, or even individual stocks.