Category: Risk Management Page 1 of 11

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.

The Hidden Cost in Costless Put-Spread Collars: Rebalance Timing Luck

By Corey Hoffstein

On January 24, 2023

In Craftsmanship, Portfolio Construction, Risk Management

We have published a new paper on the topic of rebalance timing luck in option strategies: The Hidden Cost in Costless Put-Spread Collars: Rebalance Timing Luck.

Prior research and empirical investment results demonstrate that strategy performance can be highly sensitive to rebalance schedules, an effect called rebalance timing luck (“RTL”). In this paper we extend the empirical analysis to option-based strategies. As a case study, we replicate a popular strategy – the self-financing, three-month put-spread collar – with three implementations that vary only in their rebalance schedule. We find that the annualized tracking error between any two implementations is in excess of 400 basis points. We also decompose the empirically-derived rebalance timing luck for this strategy into its linear and non-linear components. Finally, we provide intuition for the driving causes of rebalance timing luck in option-based strategies.

Liquidity Cascades: The Coordinated Risk of Uncoordinated Market Participants

By Corey Hoffstein

On September 11, 2020

In Portfolio Construction, Risk Management, Weekly Commentary

This paper is unlike any research we’ve shared in the past. Within we dive into the circumstantial evidence surrounding the “weird” behavior many investors believe markets are exhibiting. We tackle narratives such as the impact of central bank intervention, the growing scale of passive / indexed investing, and asymmetric liquidity provisioning.

Spoiler: Individually, the evidence for these narratives may be nothing more than circumstantial. In conjunction, however, they share pro-cyclical patterns that put pressure upon the same latent risk: liquidity.

In the last part of the paper we discuss some ideas for how investors might try to build portfolios that can both seek to exploit these dynamics as well as remain resilient to them.

Read it now.

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.

Option-Based Trend Following

By Nathan Faber

On June 23, 2020

In Risk Management, Trend, Weekly Commentary

This post is available as a PDF download here.

Summary

The convex payoff profile of trend following strategies naturally lends itself to comparative analysis with option strategies.
To isolate the two extremes of paying for whipsaw – either up front or in arrears – we replicate an option strategy that buys 1-month at-the-money calls and puts based on the trend signal.
We find that while option premiums steadily eat away at the balance of the options portfolio, the avoidance of large whipsaw events gives the strategy a boost at key times over the past 15 years, especially recently.
We examine how this whipsaw cost fits into the historical context of the options strategy and explore some simple ways to shift between the option-based trend following and the standard model.
The extent that whipsaw can be mitigated while still maintaining the potential to earn diversified returns is likely limited, but the optimal blend of trend following and options can be a beneficial guideline for investors to weather both sudden and prolonged drawdowns.

The non-linear payoff of trend following strategies has many similarities to options strategies, and by way of analogy, we can often gain insight into which market environments will favor trend following and why.

In our previous research piece, Straddles and Trend Following, we looked at purchasing straddles – that is, a call option and a put option – with a strike price tied to the anchor price of the trend following model. For example, if the trend following model invested in equities when the return over the past 12 months was positive, for a security that was at $100 12-months ago and is at $120 today, we would purchase a call and a put option with a strike price of $100. In this case, the call would be 20% in-the-money (ITM) and the put would be out-of-the-money (OTM).

In essence, this strategy acted like an insurance policy where the payout was tied to a reversion in the trend signal, and the premium paid when the trend signal was strong was small.

This concept of insurance is an important discussion topic in trend following strategies. The risk we must manage in these types of strategies, either directly through insurance or some other indirect means like diversification, is whipsaw.

In this commentary, we will construct an options strategy that is similar to a trend following strategy. The option strategy will pay a premium up-front to avoid whipsaw. By comparing this strategy to trend following that bears the full risk of whipsaw, we can set a better practical bound for how much investors should expect to pay or earn for bearing this risk.

Methodology and Data

For this analysis, we will use the S&P 500 index for equity returns, the 1-year LIBOR rate as the risk-free rate, and options data on the S&P 500 (SPX options).

To bridge the gap between practice and abstraction, we will utilize a volatility surface calibrated to real option data to price options. We will constrain our SPX options to $5 increments and interpolate total implied variance to get prices for options that were either illiquid or not included in the data set.

For the most part, we will stick to options that expire on the third Friday of each month and will mention when we deviate from that assumption.

The long/short trend equity strategy looks at total returns of equities over 12 months. If this return is positive, the strategy invests in equities for the following month. If the return is negative, the strategy shorts equities for the following month and earns the risk-free rate on the cash. The strategy is rebalanced monthly on the options expiration dates.

For the option-based trend strategy, on each rebalance date, we will purchase a 1-month call if the trend signal is positive or a put if the trend signal is negative. We will purchase all options at-the-money (ATM) and hold them to expiration. The strategy is fully cash-collateralized. Any premium is paid on the options roll date, interest is earned on the remaining account balance, and the option payout is realized on the next roll date.

Why are we now using ATM options when previous research used ITM and OTM options, potentially deeply ITM or OTM?

Here we are looking to isolate the cost of whipsaw in the premium paid for the option while earning a payout that is close to that of the underlying in the event that our trend signal is correct. If we utilized OTM options, then our premium would be lower but we would realize smaller gains if the underlying followed the trend. ITM options would have downside exposure before the protection kicked in.

We are also not using straddles since we do not want to pay extra premium for the chance to profit off a whipsaw. The underlying assumption here is that there is value in the trend following signal. Either strategy is able to capitalize on that (i.e. it’s the control variable); the strategies primarily differ in their treatment of whipsaw costs.

The High Cost of ATM Options

The built-in whipsaw protection in the options does not come cheap. The chart below shows the –L/S trend following strategy–, the –option-based trend strategy–, and the ratio of the two (dotted).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.

During normal market environments and even in prolonged equity-market drawdown periods like 2008, trend following outperformed the option-based strategy. Earning the full return on the underlying equity is generally beneficial.

However, something that is “generally beneficial” can be erased very quickly. In March 2020, the trend following strategy reverted back to the level of the option-based strategy. If you had only looked at cumulative returns over those 15 years, you would not be able to tell much difference between the two.

The following chart highlights these tail effects.

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.

In most months, the option-based strategy forfeits its ~1.5% premium for the ATM option. The 75^th percentile cutoff is 2.2% and the 90^th percentile cutoff is 2.9%. These premiums have occasionally spiked to 6-7%.

While these premiums are not always forfeited without some offsetting gain, they are always paid relative to the trend following strategy.

A 3% whipsaw event in trend should definitely not be a surprise based on the typical up-front cost of the option strategy.

Source: DiscountOptionsData.com. Calculations by Newfound Research.

But What About a 30% Whipsaw?

Now that’s a good question.

Up until March 2020, for the 15 years prior, the largest whipsaws relative to the options strategy were 12-13%. This is the epitome of tail risk, and it can be disheartening to think that now that we have seen 30% underperformance, we should probably expect more at some point in the (hopefully very distant) future.

However, a richer sample set can shed some light on this very poor performance.

Let’s relax our assumption that we roll the options and rebalance the trend strategies on the third Friday of the month and instead allow rebalances and rolls on any day in the month. Since we are dealing with one-month options, this is not beyond implementation since there are typically options listed that expire on Monday, Wednesday, and Friday.

The chart below shows all of these option strategies and how large of an effect that roll / rebalance timing luck can have.

With timing luck in both the options strategies and trend following, there can be large effects when the luck cuts opposite ways.

The worst returns between rebalances of trend following relative to each options strategy highlight how bad the realized path in March 2020 truly was.

In many of the trend following and option strategies pairs, the worst underperformance of trend following over any monthlong period was around 10%.

Returning to the premise that the options strategies are analogous to trend following, we see the same effects of timing luck that we have explored in previous research: effects that make comparing variants of the same strategy or similar strategies more nuanced. Whether an option strategy is used for research, benchmarking, or active investing, the implications of this timing luck should be taken into account.

But even without taking a multi-model approach at this point to the options strategy, can we move toward a deeper understanding of when it may be an effective way to offset some of the risk of whipsaw?

I’d Gladly Pay You Tuesday for a Whipsaw Risk Today

With the two extremes of paying for whipsaw up front with options and being fully exposed to whipsaw through trend following, perhaps there is a way to tailor this whipsaw risk profile. If the risk of whipsaw is elevated but the cost of paying for the insurance is cheap, then the options strategy may be favorable. On the other hand, if option premiums are high, trend following may more efficiently capture the market returns.

The price of the options (or their implied volatilities) is a natural place to start investigating this topic since it encapsulates the premium for whipsaw insurance. The problem is that it may not be a reliable signal if there is no barrier to efficiency in the options market, either behavioral or structural.

Comparing the ATM option implied volatilities with the trend signal (12-month trailing returns), we see a negative correlation, which indicates that the options-based strategy will have a higher hurdle rate of return in strongly downtrending market environments.

Source: DiscountOptionsData.com. Calculations by Newfound Research.

But this is only one piece of the puzzle.

Do these implied volatilities relate to the forward 1-month returns for the S&P 500?

Based on the above scatterplot: not really. However, since we are merely sticking implied volatility in the middle of the trend following signal and the forward return, and we believe that trend following works over the long run, then we must believe there is some relationship between implied volatility and forward returns.

While this monthly trend following signal is directionally correct over the next month 60% of the time, historically, that says nothing about the magnitude of the returns based on the signal.

Without looking too much into the data to avoid overfitting a model, we will set a simple cutoff of 20% implied volatility. If options cost more than that, we will utilize trend following. If they cost less, we will invest in the options strategy.

We will also compare it to a 50/50 blend of the two.

The switching strategy (gray line) worked well until around 2013 when the option prices were cheap, but the risk of whipsaw was not realized. It did make it through 2015, 2016 and 4Q 2018 better than trend following.

When viewed in a broader context of a portfolio, since these are alternative strategies, it does not take a huge allocation to make a difference. These strategies manage equity risk, so we can pair them with an allocation to the S&P 500 (SPY) and see how the aggregate statistics are affected over the period from 2005 to April 2020.

The chart below plots the efficient frontiers of allocations to 100% SPY at the point of convergence on the right of the graph) to 40% SPY on the left of the graph with the remainder allocated to the risk- management strategy.

The Sharpe ratio is maximized at a 35% allocation to the switching strategy, a 25% allocation to the option-based strategy, and 10% for the trend following strategy.

Conclusion

In this research note, we explored the link between trend following and options strategies using 1-month ATM put and call options, depending on the sign of the trend.

The cost of ATM options Is generally 1.5% of the portfolio value, but the fact that this cost can spike upwards of 9% should justify larger whipsaws in trend following strategies. Very large whipsaws, like in March 2020, not only show that the cost can be seemingly unbounded but also that there is significant exposure to timing luck based upon the option roll dates.

Then, we moved on to investigating a simple way to allocate between the two strategies based upon the cost of the options, When the options were cheap, we used that strategy, and when they were expensive, we invested in the trend following strategy. A modest allocation is enough to make a different in the realized efficient frontier.

Deciding to pay the up-front payment of the whipsaw insurance premium, bear the full risk a whipsaw, or land somewhere in between is largely up to investor preferences. It is risky to have a large downside potential, but the added benefit of no premiums can be enough to offset the risk.

An implied volatility threshold was a rather crude signal for assessing the risk of whipsaw and the price of insuring against it. Further research into one or multiple signals and a robust process for aggregating them into an investment decision is needed to make more definitive statements on when trend following is better than options or vice versa. The extent that whipsaw can be mitigated while still maintaining the potential to earn diversified returns is likely limited, but the optimal blend of trend following and options can be a beneficial guideline for investors to weather both sudden and prolonged drawdowns.