This post is available as a PDF download here.
Summary
- We typically discuss trend following in the context of risk management for investors looking to diversify their diversifiers.
- While we believe that trend following is most appropriate for investors concerned about sequence risk, levered trend following may have use for investors pursuing growth.
- In a simple back-test, a naïve levered trend following considerably increases annualized returns and reduces negative skew and kurtosis (“fat tails”).
- The introduced leverage, however, significantly increases annualized volatility, meaning that the strategy still exhibits significant and large drawdown profiles.
- Nevertheless, trend following may be a way to allow for the incorporation of leverage with reduced risk of permanent portfolio impairment that would otherwise occur from large drawdowns.
In an industry obsessed with alpha, our view here at Newfound has long been to take a risk-first approach to investing. In light of this, when we discuss trend following techniques, it is often with an eye towards explicitly managing drawdowns. Our aim is to help investors diversify their diversifiers and better manage the potentially devastation that sequence risk can wreak upon their portfolios.
Thus, we often discuss the application of trend following for soon-to-be and recent retirees who are in peak sequence risk years.
- Empirical evidence suggests that trend following can be a highly effective means of limiting exposure to significant and prolonged drawdowns.
- Trend following is complementary to other diversifiers like fixed income, which can theoretically increase the Sharpe ratio of the diversification bucket as a whole.
- Instead of acting as a static hedge, the dynamic approach of trend following can also help investors take advantage of market tailwinds. This may be particularly important if real interest rates remain low.
- The potential tax inefficiency of trend following is significantly lower when the alternative risk management technique is fixed income.
Despite our focus on using trend following to manage sequence risk, we often receive questions from investors still within their accumulation phase asking whether trend following can be appropriate for them as well. Most frequently, the question is, “If trend following can manage downside risk, can I use a levered approach to trend following in hopes of boosting returns?”
This commentary explores that idea, specifically in the context of available levered ETFs.
Does Naïve Levered Trend Following Work?
In an effort to avoid overfitting our results to any one particular model or parameterization of trend following, we have constructed our signals employing a model-of-models approach [1] Specifically, we use four different definitions of trend for a given N-period lookback:
- Price-Minus-Moving-Average: When price is above its N-period simple moving average, invest.Otherwise, divest.
- EWMA Cross-Over: When the (N/4)-length exponentially-weighted moving average is above the (N/2)-length exponentially-weighted moving average, invest.Otherwise, divest.
- EWMA Slope: When the (N/2)-length exponentially-weighted moving average is positively sloped, invest. Otherwise, divest.
- Percentile Channel: When price crossed above the trailing 75thpercentile over the prior N-periods, invest. Stay invested until it crosses below its trailing 25thpercentile over the prior N-periods. Stay divested until it crosses back above the 75th
For each of these four models, we also run a number of parameterizations covering 6-to-18-month lookbacks. In grand total, there are 4 models with 5 parameterizations each, giving us 30 variations of trend signals.
Using these signals, we construct three models. In the first model, we simply invest in U.S. equities in proportion to the number of signals that are positive. For example, if 75% of the trend following signals are positive, the portfolio is 75% invested in U.S. equities and 25% in the risk-free asset.
For our leveraged model, we simply multiply the percentage of signals by 2x and invest that proportion of our portfolio in U.S. equities and the remainder in the risk-free asset. In those cases where the amount invested in U.S. equities exceeds 100% of the portfolio, we assume a negative allocation to the risk-free asset (e.g. if we invest 150% of our assets in U.S. equities, we assume a -50% allocation to the risk-free asset).
With the benefit of hindsight, we should not be surprised at the results. If we know that trend following is effective at limiting severe and prolonged drawdowns (the kryptonite to levered investors), then it should come as no surprise that a levered trend following strategy does quite well.
It is well worth pointing out, however, that a highly levered strategy can be quickly wiped out by a sudden and immediate drawdown that trend following is unable to sidestep. Assuming a 2x levered position, our portfolio would be quickly wiped out by a sharp 50% correction. While such an event did not happen during the 1900s for U.S. equities, that does not mean it cannot happen in the future. Caveat emptor.
Logarithmically-plotted equity curves can be deceiving, so it is important that we also compare the annual return characteristics.
Source: Kenneth French Data Library. Calculations by Newfound Research. Returns are gross of all fees, including transaction fees, taxes, and any management fees. Returns assume the reinvestment of all distributions. Past performance is not a guarantee of future results.
While we can see that a simple trend following approach effectively “clips” the tails of the underlying distribution – giving up both the best and the worst annual returns – the levered strategy still has significant mass in both directions. Evaluating the first several moments of the distributions, however, we see that both simple and levered trend following significantly reduce the skew and kurtosis of the return distribution.
| Mean | Standard Deviation | Skew | Kurtosis | |
| U.S. Equities | 9.4% | 19% | -1.01 | 1.36 |
| Trend Following | 9.5% | 13% | 0.09 | -0.92 |
| Levered Trend Following | 14.4% | 26% | 0.11 | -0.78 |
Nevertheless, the standard deviation of the levered trend following strategy exceeds even that of the underlying asset, a potential indication that expectations for the approach may be less about, “Can I avoid large drawdowns?” and more about, “Can I use leverage for growth and still avoid catastrophe?” We can see this by plotting the joint annual log-return distributions.
We can see that for U.S. equity returns between 0% and -20%, the Levered Trend Following strategy can exhibit returns between -20% and -40%. About 11% of the observations fall into this category, making it an occurrence that a levered trend follower should expect to experience multiple times in their investment lifecycle. We can even see one year where U.S. equities are slightly positive and the levered model exhibits a near -30% return. It is in the most extreme U.S. equity years – those exceeding -20% – that the trend following aspect appears to come into play.
We must also ask the question, “can this idea survive associated fees?” If investors are looking to apply this approach using levered ETFs, they must consider the expense ratios of the ETFs themselves, transaction costs, and bid/ask spreads. Here we will use the ProShares Ultra S&P 500 ETF (“SSO”) as a data proxy. The expense ratio is 0.90% and the average bid/ask spread is 0.03%. Since transactions costs vary, we will assume an added annual 0.20% fee for asset-based pricing.
In comparison, for the naïve model, we will use the SPDR S&P 500 ETF (“SPY”) as the data proxy and assume an expense ratio of 0.09% and an average bid/ask spread 0.004%. Since most platforms have a vanilla S&P 500 ETF on their no-transaction fee list, we will not add any explicit transaction costs.
We plot the strategy equity curves below net of these assumed fees.
The annualized return for the Levered Trend Following strategy declines from 15.9% to 14.5%, while the unlevered version only falls from 10.1% to 10.0%. While the overall return of the levered version declines by 140 basis points per year, it still far exceeds the total return performance of the unlevered version.
Conclusion
Based upon this initial analysis, it would appear that a simple, levered trend following approach may be worth further consideration for investors in the accumulation phase of their investment lifecycle.
Do-it-yourself investors may have no problem implementing this idea on their own using levered ETFs, but other investors may prefer a simple, packaged approach. Unfortunately, as far as we are aware, no such packaged product exists in the marketplace today.
However, one workaround may be to utilize levered ETFs to “make room” for an unlevered trend following strategy. For example, if a growth-oriented investor currently holdings an 80/20 stock/bond mix and wanted to introduce a 20% allocation to trend following, they could re-orient their portfolio to be 60% stocks, 10% 2x levered stocks, 10% 2x levered bonds, and 20% trend following. This would have the effect of being an 80/20 stock/bond portfolio with 20% leverage applied to introduce the trend following strategy. While there are the nuances of daily reset to consider in the levered ETF solutions, this approach may allow for the modest introduction of levered trend following into the portfolio.
It is worth noting that while we employed up to 2x leverage in this commentary, there is no reason investors could not apply a lower amount, either by mixing levered and unlevered ETFs, or by using a solution like the new Portfolio+ line-up from Direxion, which applies 1.25x leverage to underlying indices.
As we like to say here at Newfound, “risk cannot be destroyed, only transformed.” While this commentary explored levered trend following in comparison to unlevered exposure, a more apt comparison might simply be to levered market exposure. We suspect that the trend following overlay creates the same transformation: a reduction of the best and worst years at the cost of whipsaw. However, the introduction of leverage further heightens the risk of sudden and immediate drawdowns: the exact loss profile trend following is ill-suited to avoid.
[1] Nothing in this commentary reflects an actual investment strategy or model managed by Newfound and any investment strategies or investment approaches reflected herein are constructed solely for purposes of analyzing and evaluating the topics herein.
How to Benchmark Trend-Following
By Nathan Faber
On May 14, 2018
In Risk & Style Premia, Risk Management, Trend, Weekly Commentary
This post is available as a PDF download here.
Summary
Any strategy that deviates from the most basic is compared to a benchmark. But how do you choose an appropriate benchmark?
The complicated nature of benchmarking can be easily seen by considering something as simple as a value stock strategy.
You may pit your concentrated value manager you currently use up against the more diversified value manager you used previously. At that time, you may have compared that value manager to a systematic smart-beta ETF like the iShares S&P 500 Value ETF (ticker: IVE). And if you were invested in that ETF, you might compare its performance to the S&P 500.
What prevents you from benchmarking them all to the S&P 500? Or from benchmarking the concentrated value strategy to all of the other three?
Benchmark choices are not unique and are highly dependent on what aspect of performance you wish to measure.
In an ideal world, the only benchmark for an investor would be whether or not they are on track for hitting their financial goals. However, in an industry obsessed with relative performance, choosing a benchmark is a necessary exercise.
This commentary will explore some of the important considerations when choosing a benchmark for trend-following strategies.
The Purpose of a Trend-Following Benchmark
As an investment manager, our goal with benchmarking is to check that a strategy’s performance is in line with our expectations. Performance versus a benchmark can answer questions such as:
With long/flat trend-following strategies, the appropriate benchmark should gauge when the manager is making correct or incorrect calls in either direction.
Unfortunately, we frequently see long/flat equity trend-following strategies benchmarked to an all-equity index like the S&P 500. This is similar to the coinflip game we outlined in our previous commentary about protecting and participating with trend-following.[1]
The behavioral implications of this kind of benchmarking are summarized in the table below.
The two cases with wrong calls – to move to cash when the market goes up or remain invested when the market goes down – are appropriately labeled, as is the correct call to move to cash when the market is going down. However, when the market is going up and the strategy is invested, it is merely keeping up with its benchmark even though it is behaving just as one would want it to.
To reward the strategy in either correct call case, the benchmark should consist of allocations to both equity and cash.
A benchmark like this can provide objective answers to the questions outlined above.
Deriving a Trend-Following Benchmark
Sticking with the trend-following strategy example we outlined in our previous commentary[2], we can look at some of the consequences of choosing different benchmarks in terms of how much the trend-following strategy deviates from them over time.
The chart below shows the annualized tracking error of the strategy to the range of strategic proportions of equity and cash.
Source: Kenneth French Data Library. Data from July 1926 – February 2018. Calculations by Newfound Research. Returns are gross of all fees, including transaction fees, taxes, and any management fees. Returns assume the reinvestment of all distributions. This document does not reflect the actual performance results of any Newfound investment strategy or index. All returns are backtested and hypothetical. Past performance is not a guarantee of future results.
The benchmark that minimizes the tracking error is a 47% allocation to equities and 53% to cash. This 0.47 is also the beta of the trend-following strategy, so we can think of this benchmark as accounting for the risk profile of the strategy over the entire 92-year period.
But what if we took a narrower view by constraining this analysis to recent performance?
The chart below shows the equity allocation of the benchmark that minimizes the tracking error to the trend-following strategy over rolling 1-year periods.
Source: Kenneth French Data Library. Data from July 1926 – February 2018. Calculations by Newfound Research. Returns are gross of all fees, including transaction fees, taxes, and any management fees. Returns assume the reinvestment of all distributions. This document does not reflect the actual performance results of any Newfound investment strategy or index. All returns are backtested and hypothetical. Past performance is not a guarantee of future results.
A couple of features stand out here.
First, if we constrain our lookback period to one year, a time-period over which many investors exhibit anchoring bias, then the “benchmark” that we may think we will closely track – the one we are mentally tied to – might be the one that we deviate the most from over the next year.
And secondly, the approximately 50/50 benchmark calculated using the entire history of the strategy is rarely the one that minimizes tracking error over the short term.
The median equity allocation in these benchmarks is 80%, the average is 67%, and the data is highly clustered at the extremes of 100% equity and 100% cash.
Source: Kenneth French Data Library. Data from July 1926 – February 2018. Calculations by Newfound Research. Returns are gross of all fees, including transaction fees, taxes, and any management fees. Returns assume the reinvestment of all distributions. This document does not reflect the actual performance results of any Newfound investment strategy or index. All returns are backtested and hypothetical. Past performance is not a guarantee of future results.
The Intuitive Trend-Following Benchmark
Is there a problem in determining a benchmark using the tracking error over the entire period?
One issue is that it is being calculated with the benefit of hindsight. If you had started a trend-following strategy back in the 1930s, you would have arrived at a different equity allocation for the benchmark based on this analysis given the available data (e.g. using data up until the end of 1935 yields an equity allocation of 37%).
To remove this reliance on having a sufficiently long backtest, our preference is to rely more on the strategy’s rules and how we would use it in a portfolio to determine our trend-following benchmarks.
For a trend following strategy that pivots between stocks and cash, a 50/50 benchmark is a natural choice.
It is broad enough to include the assets in the trend-following strategy’s investment universe while being neutral to the calls to be long or flat.
Seeing the 50/50 portfolio be the answer to the tracking error minimization problem over the entire data simply provides empirical evidence for its use.
One argument against using a 50/50 blend could focus on the fact that the market is generally up more frequently than it is down, at least historically. While this is true, the magnitude of down moves has often been larger than the magnitude of up moves. Since this strategy is explicitly meant as a risk management tool, accounting for both the magnitude and the frequency is prudent.
Another argument against its use could be the belief that we are entering a different market environment where history will not be an accurate guide going forward. However, given the random nature of market moves coupled with the behavioral tendencies of investors to overreact, herd, and anchor, a benchmark close to a 50/50 is likely still a fitting choice.
Setting Expectations with a Trend-Following Benchmark
Now that we have a benchmark to use, how do we use it to set our expectations?
Neglecting the historical data for the moment, from the ex-ante perspective, it is helpful to decompose a typical market cycle into four different segments and assess how we expect trend-following to behave:
Of course, this is a somewhat ideal scenario that rarely plays out perfectly. Whipsaw events occur as prices recover (decline) before declining (recovering) again.
But it is important to note how the level of risk relative to this 50/50 benchmark varies over time.
Contrast this with something like an all equity strategy benchmarked to the S&P 500 where the risk is likely to be similar during most market environments.
Now, if we look at the historical data, we can see this borne out in the graph of the drawdowns for trend-following and the 50/50 benchmark.
Source: Kenneth French Data Library. Data from July 1926 – February 2018. Calculations by Newfound Research. Returns are gross of all fees, including transaction fees, taxes, and any management fees. Returns assume the reinvestment of all distributions. This document does not reflect the actual performance results of any Newfound investment strategy or index. All returns are backtested and hypothetical. Past performance is not a guarantee of future results.
In most prolonged and major (>20%) drawdowns, trend-following first underperforms the benchmark, then outperforms, then lags as equities improve, and then outperform again.
Using the most recent example of the Financial Crisis, we can see the capture ratios verses the benchmark in each regime.
Source: Kenneth French Data Library. Data from October 2007 – February 2018. Calculations by Newfound Research. Returns are gross of all fees, including transaction fees, taxes, and any management fees. Returns assume the reinvestment of all distributions. This document does not reflect the actual performance results of any Newfound investment strategy or index. All returns are backtested and hypothetical. Past performance is not a guarantee of future results.
The underperformance of the trend-following strategy verses the benchmark is in line with expectations based on how the strategy is desired to work.
Another way to use the benchmark to set expectations is to look at rolling returns historically. This gives context for the current out- or underperformance relative to the benchmark.
From this we can see which percentile the current return falls into or check to see how many standard deviations it is away from the average level of relative performance.
Source: Kenneth French Data Library. Data from July 1926 – February 2018. Calculations by Newfound Research. Returns are gross of all fees, including transaction fees, taxes, and any management fees. Returns assume the reinvestment of all distributions. This document does not reflect the actual performance results of any Newfound investment strategy or index. All returns are backtested and hypothetical. Past performance is not a guarantee of future results.
In all this, there are a few important points to keep in mind:
Conclusion
Benchmarking a trend-following strategy can be a difficult exercise in managing behavioral biases. With the tendency to benchmark all equity-based strategies to an all-equity index, investors often set themselves up for a let-down in a bull market with trend-following.
With benchmarking, the focus is often on lagging the benchmark by “too much.” This is what an all-equity benchmark can do to trend-following. However, the issue is symmetric: beating the benchmark by “too much” can also signal either an issue with the strategy or with the benchmark choice. This is why we would not benchmark a long/flat trend-following strategy to cash.
A 50/50 portfolio of equities and cash is generally an appropriate benchmark for long/flat trend-following strategies. This benchmark allows us to measure the strategy’s ability to correctly allocate when equities are both increasing or decreasing.
Too often, investors use benchmarking solely to see which strategy is beating the benchmark by the most. While this can be a use for very similar strategies (e.g. a set of different value managers), we must always be careful not to compare apples to oranges.
A benchmark should not conjure up an image of a dog race where the set of investment strategies are the dogs and the benchmark is the bunny out ahead, always leading the way.
We must always acknowledge that for a strategy to outperform over the long-run, it must undergo shorter periods of underperformance. Diversifying approaches can manage events that are unfavorable to one strategy, and benchmarking is a tool to set expectations around the level of risk management necessary in different market environments.
[1] https://blog.thinknewfound.com/2018/05/leverage-and-trend-following/
[2] https://blog.thinknewfound.com/2018/03/protect-participate-managing-drawdowns-with-trend-following/