Tag: trend following Page 4 of 5

The New Glide Path

On July 2, 2018

In Portfolio Construction, Risk Management, Sequence Risk, Weekly Commentary

This post is available as a PDF download here.

Summary

In practice, investors and institutions alike have spending patterns that makes the sequence of market returns a relevant risk factor.
All else held equal, investors would prefer to make contributions before large returns and withdrawals before large declines.
For retirees making constant withdrawals, sustained declines in portfolio value represent a significant risk. Trend-following has demonstrated historical success in helping reduce the risk these types of losses.
Traditionally, stock/bond glide paths have been used to control sequence risk. However, trend-following may be able to serve as a valuable hybrid between equities and bonds and provide a means to diversify our diversifiers.
Using backward induction and a number of simplifying assumptions, we generate a glide path based upon investor age and level of wealth.
We find that trend-following receives a significant allocation – largely in lieu of equity exposure – for investors early in retirement and whose initial consumption rate closely reflects the 4% level.

In past commentaries, we have written at length about investor sequence risk. Summarized simply, sequence risk is the sensitivity of investor goals to the sequence of market returns. In finance, we traditionally assume the sequence of returns does not matter. However, for investors and institutions that are constantly making contributions and withdrawals, the sequence can be incredibly important.

Consider for example, an investor who retires with $1,000,000 and uses the traditional 4% spending rule to allocate a $40,000 annual withdrawal to themselves. Suddenly, in the first year, their portfolio craters to $500,000. That $40,000 no longer represents just 4%, but now it represents 8%.

Significant drawdowns and fixed withdrawals mix like oil and water.

Sequence risk is the exact reason why traditional glide paths have investors de-risk their portfolios over time from growth-focused, higher volatility assets like equities to traditionally less volatile assets, like short-duration investment grade fixed income.

Bonds, however, are not the only way investors can manage risk. There are a variety of other methods, and frequent readers will know that we are strong advocates for the incorporation of trend-following techniques.

But how much trend-following should investors use? And when?

That is exactly what this commentary aims to explore.

Building a New Glidepath

In many ways, this is a very open-ended question. As a starting point, we will create some constraints that simplify our approach:

The assets we will be limited to are broad U.S. equities, a trend-following strategy applied to U.S. equities, a 10-year U.S. Treasury index, and a U.S. Treasury Bill index.
In any simulations we perform, we will use resampled historical returns.
We assume an annual spend rate of $40,000 growing at 3.5% per year (the historical rate of annualized inflation over the period).
We assume our investor retires at 60.
We assume a male investor and use the Social Security Administration’s 2014 Actuarial Life Table to estimate the probability of death.

Source: St. Louis Federal Reserve and Kenneth French Database. Past performance is hypothetical and backtested. Trend Strategy is a simple 200-day moving average cross-over strategy that invests in U.S. equities when the price of U.S. equities is above its 200-day moving average and in U.S. T-Bills otherwise. Returns are gross of all fees and assume the reinvestment of all dividends. None of the equity curves presented here represent a strategy managed by Newfound Research.

To generate our glide path, we will use a process of backwards induction similar to that proposed by Gordon Irlam in his article Portfolio Size Matters (Journal of Personal Finance, Vol 13 Issue 2). The process works thusly:

Starting at age 100, assume a success rate of 100% for all wealth levels except for $0, which has a 0% success rate.
Move back in time 1 year and generate 10,000 1-year return simulations.
For each possible wealth level and each possible portfolio configuration of the four assets, use the 10,000 simulations to generate 10,000 possible future wealth levels, subtracting the inflation-adjusted annual spend.
For a given simulation, use standard mortality tables to determine if the investor died during the year. If he did, set the success rate to 100% for that simulation. Otherwise, set the success rate to the success rate of the wealth bucket the simulation falls into at T+1.
For the given portfolio configuration, set the success rate as the average success rate across all simulations.
For the given wealth level, select the portfolio configuration that maximizes success rate.
Return to step 2.

As a technical side-note, we should mention that exploring all possible portfolio configurations is a computationally taxing exercise, as would be an optimization-based approach. To circumvent this, we employ a quasi-random low-discrepancy sequence generator known as a Sobol sequence. This process allows us to generate 100 samples that efficiently span the space of a 4-dimensional unit hypercube. We can then normalize these samples and use them as our sample allocations.

If that all sounded like gibberish, the main thrust is this: we’re not really checking every single portfolio configuration, but trying to use a large enough sample to capture most of them.

By working backwards, we can tackle what would be an otherwise computationally intractable problem. In effect, we are saying, “if we know the optimal decision at time T+1, we can use that knowledge to guide our decision at time T.”

This methodology also allows us to recognize that the relative wealth level to spending level is important. For example, having $2,000,000 at age 70 with a $40,000 real spending rate is very different than having $500,000, and we would expect that the optimal allocation would different.

Consider the two extremes. The first extreme is we have an excess of wealth. In this case, since we are optimizing to maximize the probability of success, the result will be to take no risk and hold a significant amount of T-Bills. If, however, we had optimized to acknowledge a desire to bequeath wealth to the next generation, you would likely see the opposite extreme: with little risk of failure, you can load up on stocks and to try to maximize growth.

The second extreme is having a significant dearth of wealth. In this case, we would expect to see the optimizer recommend a significant amount of stocks, since the safer assets will likely guarantee failure while the risky assets provide a lottery’s chance of success.

The Results

To plot the results both over time as well as over the different wealth levels, we have to plot each asset individually, which we do below. As an example of how to read these graphs, below we can see that in the table for U.S. equities, at age 74 and a $1,600,000 wealth level, the glide path would recommend an 11% allocation to U.S. equities.

A few features we can identify:

When there is little chance of success, the glide path tilts towards equities as a potential lottery ticket.
When there is a near guarantee of success, the glide path completely de-risks.
While we would expect a smooth transition in these glide paths, there are a few artifacts in the table (e.g. U.S. equities with $200,000 wealth at age 78). This may be due to a particular set of return samples that cascade through the tables. Or, because the trend following strategy can exhibit nearly identical returns to U.S. equities over a number of periods, we can see periods where the trend strategy received weight instead of equities (e.g. $400,000 wealth level at age 96 or $200,000 at 70).

Ignoring the data artifacts, we can broadly see that trend following seems to receive a fairly healthy weight in the earlier years of retirement and at wealth levels where capital preservation is critical, but growth cannot be entirely sacrificed. For example, we can see that an investor with $1,000,000 at age 60 would allocate approximately 30% of their portfolio to a trend following strategy.

Note that the initially assumed $40,000 consumption level aligns with the generally recommended 4% withdrawal assumption. In other words, the levels here are less important than their size relative to desired spending.

It is also worth pointing out again that this analysis uses historical returns. Hence, we see a large allocation to T-Bills which, once upon a time, offered a reasonable rate of return. This may not be the case going forward.

Conclusion

Financial theory generally assumes that the order of returns is not important to investors. Any investor contributing or withdrawing from their investment portfolio, however, is dramatically affected by the order of returns. It is much better to save before a large gain or spend before a large loss.

For investors in retirement who are making frequent and consistent withdrawals from their portfolios, sequence manifests itself in the presence of large and prolonged drawdowns. Strategies that can help avoid these losses are, therefore, potentially very valuable.

This is the basis of the traditional glidepath. By de-risking the portfolio over time, investors become less sensitive to sequence risk. However, as bond yields remain low and investor life expectancy increases, investors may need to rely more heavily on higher volatility growth assets to avoid running out of money.

To explore these concepts, we have built our own glide path using four assets: broad U.S. equities, 10-year U.S. Treasuries, U.S. T-Bills, and a trend following strategy. Not surprisingly, we find that trend following commands a significant allocation, particularly in the years and wealth levels where sequence risk is highest, and often is allocated to in lieu of equities themselves.

Beyond recognizing the potential value-add of trend following, however, an important second takeaway may be that there is room for significant value-add in going beyond traditional target-date-based glide paths for investors.

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

Benchmarking a trend-following strategy can be a difficult exercise in managing behavioral biases.
While the natural tendency is often to benchmark equity trend-following to all-equities (e.g. the S&P 500), this does not accurately give the strategy credit for choosing to be invested when the market is going up.
A 50/50 portfolio of equities and cash is generally an appropriate benchmark for long/flat trend-following strategies, both for setting expectations and for gauging current relative performance.
If we acknowledge that for a strategy to outperform over the long-run, it must undergo shorter periods of underperformance, using this symmetric benchmark can isolate market environments that underperformance should be expected.
Diversifying risk-management approaches (e.g. pairing strategic allocation with tactical trend-following) 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.

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.

Benchmarking is one of the most frequently abused facets of investing. It can be extremely useful when applied in the correct manner, but most of the time, it is simply a hurdle to sticking with an investment plan.

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:

Is the out- or underperformance appropriate for the given market environment?
Is the magnitude of out- or underperformance typical?
How is the strategy behaving in the context of other ways of managing risk?

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.

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.

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:

Initial decline – Equity markets begin to sell off, and the fully invested trend-following strategy underperforms the 50/50 benchmark.
Prolonged drawdown – The trend-following strategy adapts to the decline and moves to cash. The trend-following strategy outperforms.
Initial recovery – The trend-following strategy is still in cash and lags the benchmark as prices rebound off the bottom.
Sustained recovery – The trend-following strategy reinvests and captures more of the upside than the benchmark.

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.

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.

In all this, there are a few important points to keep in mind:

Price moves that occur faster than the scope of the trend-following measurement can be one source of the largest underperformance events.
Along a similar vein, whipsaw is a key risk of trend-following. Highly oscillatory markets will not be favorable to trend-following. In these scenarios, trend following can underperform even fully invested equities.
With percentile analysis, there is always a first time for anything. Having a rich data history covering a variety of market scenarios mitigates this, but setting new percentiles, either on the low end or high end, is always possible.
Sometimes a strategy is expected to lag its benchmark in a given market environment. A primary goal with benchmarking is it accurately set these expectations for the potential magnitude of relative performance and design the portfolio accordingly.

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/

Leverage and Trend Following

By Corey Hoffstein

On May 7, 2018

In Risk & Style Premia, Risk Management, Trend, Weekly Commentary

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 75^thpercentile over the prior N-periods, invest. Stay invested until it crosses below its trailing 25^thpercentile over the prior N-periods. Stay divested until it crosses back above the 75^th

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.

The Importance of Diversification in Trend Following

By Corey Hoffstein

On April 30, 2018

In Craftsmanship, Risk & Style Premia, Risk Management, Trend, Weekly Commentary

This post is available as a PDF download here.

Summary

Diversification is a key ingredient to a successful trend following program.
While most popular trend following programs take a multi-asset approach (e.g. managed futures programs), we believe that single-asset strategies can play a meaningful role in investor portfolios.
We believe that long-term success requires introducing sources of diversification within single-asset portfolios. For example, in our trend equity strategies we employ a sector-based framework.
We believe the increased internal diversification allows not only for a higher probability of success, but also increases the degrees of freedom with which we can manage the strategy.
Introducing diversification, however, can also introduce tracking error, which can be a source of frustration for benchmark-sensitive investors.

Our friends over at ReSolve Asset Management recently penned a blog post titled Diversification – What Most Novice Investors Miss About Trend Following. What the team at ReSolve succinctly shows – which we tried to demonstrate in our own piece, Diversifying the What, How, and When of Trend Following– is that diversification is a hugely important component of developing a robust trend following program.

A cornerstone argument of both pieces is that the overwhelming success of a simple trend following approach applied to U.S. equities may be misleading. The same approach, when applied to a large cross-section of majority international equity indices, shows a large degree of dispersion.

That is not to say that the approach does not work: in fact, it is the robustness across such a large cross-section that gives us confidence that it does. Rather, we see that the relative success seen in applying the approach on U.S. equity markets may be a positive outlier.

ReSolve proposes a diversified, multi-asset trend following approach that is levered to the appropriate target volatility. In our view, this solution is both theoretically and empirically sound.

That said, here at Newfound we do offer a number of solutions that apply trend following on a single asset class. Indeed, the approach we are most well-known for (going back to when were founded in August 2008), has been long/flat trend following on U.S. equities.

How do we reconcile the belief that multi-asset trend following likely offers a higher risk-adjusted return, but still offer single-asset trend following strategies? The answer emerges from our ethos of investing at the intersection of quantitative and behavioral finance. Specifically, we acknowledge that investors tend to exhibit an aversion to non-transparent strategies that have significant tracking error to their reference benchmarks.

Trend following approaches on single asset classes like U.S. equities (an asset class that tends to dominate the risk profile of most U.S. investors) can therefore potentially offer a more sustainable risk management solution, even if it does so with a lower long-term risk-adjusted return than a multi-asset approach.

Nevertheless, we believe that how a trend following strategy is implemented is critical for long-term success. This is especially true for approaches that target single asset classes.

Finding Diversification Within Single-Asset Strategies

Underlying Newfound’s trend equity strategies (both our Sector and Factor series) is a sector-based methodology. The reason for employing this methodology is an effort to maximize internal strategy diversification. Recalling our three-dimensional framework of diversification – “what” (investments), “how” (process), and “when” (timing) – our goal in using sectors is to increase diversification along the what axis.

As an example, below we plot the correlation between sector-based trend following strategies. Specifically, we use a simple long/flat 200-day moving average cross-over system.

Source: Kenneth French Data Library. Calculations by Newfound Research. Trend following strategy is a 200-day simple moving average cross-over approach where the strategy holds the underlying sector long when price is above its 200-day simple moving average and invests in the risk-free asset when price falls below. 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 none of the sector strategies offer negative correlation to one another (nor would we expect them to), we can see that many of the cross-correlations are substantially less than one. In fact, the average pairwise correlation is 0.50.

Source: Kenneth French Data Library. Calculations by Newfound Research. Trend following strategy is a 200-day simple moving average cross-over approach where the strategy holds the underlying sector long when price is above its 200-day simple moving average and invests in the risk-free asset when price falls below. Not an actual strategy managed by Newfound. Hypothetical strategy created solely for this commentary and all returns are backtested and hypothetical. 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.

We would expect that we can benefit from this diversification by creating a strategy that trades the underlying sectors, which in aggregate provide us exposure to the entire U.S. equity market, rather than trading a single trend signal on the entire U.S. equity market itself. Using a simple equal-weight approach among the seconds, we find exactly this.

Source: Kenneth French Data Library. Calculations by Newfound Research. Trend following strategy is a 200-day simple moving average cross-over approach where the strategy holds the underlying sector long when price is above its 200-day simple moving average and invests in the risk-free asset when price falls below. Not an actual strategy managed by Newfound. Hypothetical strategy created solely for this commentary and all returns are backtested and hypothetical. 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.

There are two important things to note. First is that the simple trend following approach, when applied to broad U.S. equities, offers a Sharpe ratio higher than trend following applied to any of the underlying sectors themselves. We can choose to believe that this is because there is something special about applying trend following at the aggregate index level, or we can assume that this is simply the result of a single realization of history and that our forward expectations for success should be lower.

We would be more likely to believe the former if we demonstrated the same effect across the globe. For now, we believe it is prudent to assume the latter.

The most important detail of the chart, however, is that a simple equally-weighted portfolio of the underlying sector strategies not only offered a dramatic increase in the Sharpe ratio compared to the median sector strategy, but also a near 15% boost in Sharpe ratio against that offered by trend following on broad U.S. equities.

Using a sector-based approach also affords us greater flexibility in our portfolio construction. For example, while a single-signal approach to trend following across broad U.S. equities creates an “all in” or “all out” dynamic, using sectors allows us to either incorporate other signals (e.g. cross-sectional momentum, as popularized in Gary Antonacci’s dual momentum approach) or re-distribute available capital.

For example, below we plot the annualized return versus maximum drawdown for an equal-weight sector strategy that allows for the re-use of capital. For example, when a trend signal for a sector turns negative, instead of moving the capital to cash, the capital is equally re-allocated across the remaining sectors. A position limit is then applied, allowing the portfolio to introduce the risk-free asset when a certain number of sectors has turned off.

Source: Kenneth French Data Library. Calculations by Newfound Research. Not an actual strategy managed by Newfound. Hypothetical strategy created solely for this commentary and all returns are backtested and hypothetical. 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.

The annotations on each point in the plot reflect the maximum position size, which can also be interpreted as inversely proportional the number of sectors that have to still be exhibiting a positive trend to remain fully invested. For example, the point labeled 9.1% does not allow for any re-use of capital, as it requires all 11 sectors to be positive. On the other hand, the point labeled 50% requires just two sectors to exhibit positive trends to remain fully invested.

We can see that the degree to which capital is re-used becomes an axis along which we can trade-off our pursuit of return versus our desire to protect on the downside. Limited re-use decreases both drawdown and annualized return. We can also see, however, that after a certain amount of capital re-use, the marginal increase in annualized return decreases dramatically while maximum drawdown continues to increase.

Of course, the added internal diversification and the ability to re-use available capital do not come free. The equal-weight sector framework employed introduces potentially significant tracking error to broad U.S. equities, even without introducing the dynamics of trend following.

We can see that the average long-term tracking error is not insignificant, and at times can be quite extreme. The dot-com bubble, in particular, stands out as the equal-weight framework would have a significant underweight towards technology. During the dot-com boom, this would likely represent a significant source of frustration for investors. Even in less extreme times, annual deviations of plus-or-minus 4% from broad U.S. equities would not be uncommon.

Conclusion

For investors pursuing trend following strategies, diversification is a key ingredient. Many of the most popular trend following programs – for example, managed futures – take a multi-asset approach. However, we believe that a single-asset approach can still play a meaningful role for investors who seek to manage specific asset risk or who are looking for a potentially more transparent solution.

Nevertheless, diversification remains a critical consideration for single-asset solutions as well. In our trend equity strategies here at Newfound, we employ a sector-based framework so as to increase the number of signals that dictate our overall equity exposure.

An ancillary benefit of this process is that the sectors provide us another axis with which to manage our portfolio. We not only have the means by which to introduce other signals into our allocation process (e.g. overweighting sectors exhibiting favorable value or momentum tilts), but we can also decide how much capital we wish to re-invest when trend signals turn negative.

Unfortunately, these benefits do not come free. A sector-based framework can also potentially introduce a significant degree of tracking error to standard equity benchmarks. While we believe that the pros outweigh the cons over the long run, investors should be aware that such an approach can lead to significant relative deviations in performance over the short run.

Risk Ignition with Trend Following

By Corey Hoffstein

On April 23, 2018

In Risk & Style Premia, Risk Management, Trend, Weekly Commentary

This post is available as a PDF download here.

Summary

While investors are often concerned about catastrophic risks, failing to allocate enough to risky assets can lead investors to “fail slowly” by not maintaining pace with inflation or supporting withdrawal rates.
Historically, bonds have acted as the primary means of managing risk.However, historical evidence suggests that investors may carry around a significant allocation to fixed income only to offset the tail risks of a few bad years in equities.
Going forward, maintaining a large, static allocation to fixed income may represent a significant opportunity cost for investors.
Trend following strategies have historically demonstrated the ability to significantly reduce downside risk, though often give up exposure to the best performing years as well.
Despite reducing upside capture, trend following strategies may represent a beneficial diversifier for conservative portfolios going forward, potentially allowing investors to more fully participate with equity market growth without necessarily fully exposing themselves to equity market risk.

In our recent commentary Failing Slow, Failing Fast, and Failing Very Fast, we re-introduced the idea of “risk ignition,” a phrase we first read in Aaron Brown’s book Red Blooded Risk. To quote the book on the core concept of the idea,

“Taking less risk than is optimal is not safer; it just locks in a worse outcome. Taking more risk than is optimal also results in a worse outcome, and often leads to complete disaster.”

Risk ignition is about taking sufficient risk to promote growth, but not so much risk as to create a high probability of catastrophe.

Traditionally, financial planners have tried to find the balance of risk in the intersection of an investor’s tolerance for risk and their capacity to bear it. The former addresses the investor’s personal preferences while the latter addresses their financial requirements.

What capacity fails to capture, in our opinion, is an investor’s need to take risk. It would be difficult to make the argument that a recent retiree with $1,000,000 saved and a planned 4% inflation-adjusted withdrawal rate should ever be allocated to 100% fixed income in the current interest rate environment, no matter what his risk tolerance is. Bearing too little risk is precisely how investors end up failing slowly.

The simple fact is that earning a return above the risk-free rate requires bearing risk. It is why, after all, the excess annualized return that equities earn is known as the “equity risk premium.” Emphasis on the “risk premium” part.

As more and more Baby Boomers retire, prevailing low interest rates mean that traditionally allocated conservative portfolios may no longer offer enough upside to address longevity risk. However, blindly moving these investors into riskier profiles (which may very well be above their risk tolerance anyway) may be equally imprudent, as higher portfolio volatility increases sensitivity to sequence risk when an investor begins taking distributions.

This is where we believe that tactical strategies can play an important role.

Holding Bonds for Insurance

In the simplest asset allocation framework, investors balance their desire to pursue growth with their tolerance (and even capacity) for risk by blending stocks and bonds. More conservative investors tend to hold a larger proportion of fixed income instruments, preferring their defined cash flows and maturity dates, while growth investors tilt more heavily towards equities. Stocks fight the risk of lost purchasing power (i.e. inflation) while bonds fight the risk of capital loss.

The blend between equities and bonds will ultimately be determined by balancing exposure to these two risks.

But why not simply hold just stocks? A trivial question, but one worth acknowledging. The answer is found in the graph below, where we plot the distribution fitting the annual returns of a broad U.S. equity index from 1962 to 2017. What we see is a large negative skew, which implies that the left tail of the distribution is much larger than the right. In plain English: every once in a while, stocks crash. Hard.

The large left tail implies a drawdown risk that investors with short time horizons, or who are currently taking distributions from their portfolios, may not be able to bear. This is evident by plotting the realized excess return of different stock / bond[1] mixes versus their respective realized volatility profiles. We can see that volatility is largely driven by the equity allocation in the portfolio.

This left tail, and long-term equity realized equity volatility in general, is driven by just a few outlier events. To demonstrate, we will remove the worst performing years for U.S. equities from the dataset. For the sake of fairness, we’ll also drop an equal number of best years (acknowledging that the best years often follow the worse, and vice versa). Despite losing the best years, the worst years are so bad that we still see a tremendous shift up-and-to-the-left in the realized frontier, indicating higher realized returns with less risk.

Consider that the Sharpe optimal portfolio moves from the 50% stocks / 50% bonds mixture when the full data set is used to an 80% stock / 20% bond split when the best and worst three years are dropped.

Note that in the full-sample frontier, achieving a long-term annualized volatility of 10% requires holding somewhere between 40-50% of our portfolio in 10-year U.S. Treasuries. When we drop the best and worst 3 years of equity returns, the same risk level can be achieved with just a 20-30% allocation to bonds.

If we go so far as to drop the best and worst five years? We would only need 10% of our portfolio in bonds to hit that long-term volatility target.

One interpretation of this data is that investors carry a very significant allocation to bonds in their portfolio simply in effort to hedge the left-tail risks of equities. For a “balanced” investor (i.e. one around the 10% volatility level of a 60/40 portfolio), the worst three years of equity returns increases the recommended allocation to bonds by 20-30%!

Why is this important? Consider that forward bond forecasts heavily rely on current interest rates. Despite the recent increase in the short-end of the U.S. Treasury yield curve, intermediate term rates remain well-below long-term averages. This has two major implications:

If a bear market were to emerge, bonds may not provide the same protection they did in prior bear environments. (See our commentary Bond Returns: Don’t Be Jealous, Be Worried)
The opportunity cost for holding bonds versus equities may be quite elevated (if the term premium has eroded while the equity risk premium has remained constant).

Enter trend following.

Cutting the Tails with Trend Following

At its simplest, trend following says to remain invested while an investment is still appreciating in value and divest (or, potentially, even short) when an investment begins to depreciate.

(Since we’ve written at length about trend following in the past, we’ll spare the details in this commentary. For those keen on learning more about the history and theory of trend following, we would recommend our commentaries Two Centuries of Momentum and Protect and Participate: Managing Drawdowns with Trend Following.)

How, exactly, trend is measured is part of the art. The science, however, largely remains the same: trend following has a long, documented trail of empirical evidence suggesting that it may be an effective means of reducing drawdown risk in a variety of asset classes around the globe.

We can see in the example below that trend following applied to U.S. equities over the last 50+ years is no exception.

(In this example, we have applied a simple price-minus-moving-average trend following strategy. When price is above the 200-day moving average, we invest in broad U.S. equities. When price falls below the 200-day moving average, we divest into the risk-free asset. The model is evaluated daily after market close and trades are assumed to be executed at the close of the following day.)

Source: Kenneth French Data Library and Federal Reserve of St. Louis. 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 the long-term equity curve tells part of the story – nearly matching long-term returns while avoiding many of the deepest – we believe that a more nuanced conversation can be had by looking at the joint distribution of annual returns between U.S. equities and the trend following strategy.

Source: Kenneth French Data Library. Calculations by Newfound Research. Scatter plot shows the joint distribution of annual returns from 1962 to 2017 for a broad U.S. equity index and a trend following strategy. 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.

We can see that when U.S. equity returns are positive, the trend following strategy tends to have positive returns as well (albeit slightly lower ones). When returns are near zero, the trend following strategy has slightly negative returns. And when U.S. equity returns are highly negative, the trend following strategy significantly limits these returns.

In many ways, one might argue that the return profile of a trend following strategy mirrors that of a long call option (or, alternatively, index plus a long put option). The strategy has historically offered protection against large drawdowns, but there is a “premium” that is paid in the form of whipsaw.

We can also see this by plotting the annual return distribution of U.S. equities with the distribution of the trend strategy superimposed on top.

The trend strategy exhibits significantly less skew than U.S. equities, but loses exposure in both tails. This means that while trend following has historically been able to reduce exposure to significant losses, it has also meant giving up the significant gains. This makes sense, as many of the market’s best years come off the heels of the worst, when trend following may be slower to reinvest.

In fact, we can see that as we cut off the best and worst years, the distribution of equity returns converges upon the distribution of the trend following strategy.

Our earlier analysis of changes to the realized efficient frontier when the best and worst years are dropped indicates that the return profile of trend following may be of significant benefit to investors. Specifically, conservative investors may be able to hold a larger allocation to trend following than naked equities. This allows them to tilt their exposure towards equities in positive trending periods without necessarily invoking a greater level of portfolio volatility and drawdown due to the negative skew equities exhibit.

In the table below, we find the optimal mix of stocks, bonds, and the trend strategy that would have maximized excess annualized return for the same level of volatility of a given stock/bond blend.

Target	U.S. Equities	10-Year Treasury Index	Trend Strategy
0/100	7.4%	34.7%	58.0%
10/90	9.7%	48.4%	41.9%
20/80	11.5%	59.5%	29.0%
30/70	10.9%	56.4%	32.7%
40/60	8.9%	43.8%	47.3%
50/50	6.6%	29.9%	63.6%
60/40	37.2%	25.0%	37.8%
70/30	45.4%	14.0%	40.7%
80/20	53.9%	3.1%	43.1%
90/10	75.9%	0.0%	24.1%
100/0	100.0%	0.0%	0.0%

We can see that across the board, the optimal portfolio would have had a significant allocation to the trend following strategy. Below, we plot excess annualized return versus volatility for each of these portfolios (in orange) as well as the target mixes (in blue).

In all but the most aggressive cases (where trend following simply was not volatile enough to match the required volatility of the benchmark allocation), trend following creates a lift in excess annualized return. This is because trend following has historically allowed investors to simultaneously decrease overall portfolio risk in negative trending environments and increaseexposure to equities in positive trending ones.

Consider, for example, the optimal mixture that targets the same risk profile of the 30/70 stock/bond blend. The portfolio holds 9.7% in stocks, 48.4% in bonds and 41.9% in the trend strategy. This means that in years where stocks are exhibiting a positive trend, the portfolio is a near 50/50 stock/bond split. In years where stocks are exhibiting a negative trend, the portfolio tilts towards a 10/90 split. Trend following allows the portfolio to both be far more aggressive as well as far more defensive than the static benchmark.

Used in this manner, even if the trend following strategy underperforms stocks in positive trending years, so long as it outperforms bonds, it can add value in the context of the overall portfolio! While bonds have, historically, acted as a static insurance policy, trend following acts in a far more dynamic capacity, allowing investors to try to maximize their exposure to the equity risk premium.

Conclusion

Historically, stocks and bonds have acted as the building blocks of asset allocation. Investors pursuing a growth mandate have tilted towards stocks, while those focused on capital preservation have tilted more heavily towards bonds.

For conservative investors, the need to employ a large bond position is mainly driven by the negative skew exhibited by equity returns. However, this means that investors are significantly under-allocated to equities, and therefore sacrifice significant growth potential, during non-volatile years.

With low forecasted returns in fixed income, the significant allocation to bonds carried around by most conservative investors may represent a significant opportunity cost, heightening the risk offailing slow.

Trend following strategies, however, offer a simple alternative. The return profile of these strategies has historically mimicked that of a call option: meaningful upside participation with limited downside exposure. While not contractually guaranteed, this dynamic exposure may offer investors a way to reduce their allocation to fixed income without necessarily increasing their exposure to left-tail equity risk.

[1] We use a constant maturity 10-year U.S. Treasury index for bonds.

Tag: trend following Page 4 of 5

The New Glide Path

Summary­

Building a New Glidepath

The Results

Conclusion

How to Benchmark Trend-Following

Summary­

The Purpose of a Trend-Following Benchmark

Deriving a Trend-Following Benchmark

The Intuitive Trend-Following Benchmark

Setting Expectations with a Trend-Following Benchmark

Conclusion

Leverage and Trend Following

Summary­

Does Naïve Levered Trend Following Work?

Conclusion

The Importance of Diversification in Trend Following

Summary­

Finding Diversification Within Single-Asset Strategies

Conclusion

Risk Ignition with Trend Following

Summary­

Holding Bonds for Insurance

Cutting the Tails with Trend Following

Conclusion

Summary

Summary

Summary

Summary

Summary