Category: Weekly Commentary Page 5 of 21

Global Growth-Trend Timing

By Steven Braun

On November 4, 2019

In Portfolio Construction, Trend, Weekly Commentary

This post is available as a PDF download here.

Summary

While trend following may help investors avoid prolonged drawdowns, it is susceptible to whipsaw where false signals cause investors to either buy high and sell low (realizing losses) or sell low and buy high (a missed opportunity).
Empirical evidence suggests that using economic data in the United States as a filter of when to employ trend-following – a “growth-trend timing” model – has historically been fruitful.
When evaluated in other countries, growth-trend timing has been historically successful in mitigating whipsaw losses without sacrificing the ability to avoid large drawdowns. However, we see mixed results on whether this actually improves upon naïve trend-following.
We find that countries that can be influenced by factors originating outside of their borders might not benefit from an introspective economic signal.

We apologize in advance, as this commentary will be fairly graph- and table-heavy.

We have written fairly extensively on the topic of factor-timing in the past, and much of the success has been proven to be both hard to implement and recreate out of sample.

One of the inherent pains of trend following is the existence of whipsaws, or more precisely, the misidentification of perceived market trends, which turn out to be more noise than signal. An article from Philosophical Economics proposed using several economic indicators to tune down the noise that might affect price-driven signals such as trend following. Generally, this strategy imposed an overlay that turned trend following “on” when the change in the economic indicators were negative year-over-year signaling a higher likelihood of recession, and conversely, adopted a buy-and-hold stance when the economic indicators were not flashing warning lights.

This strategy presents a certain appeal as leading economic indicators may, as their name implies, lead the market for some time until capital preservation is warranted. Switching to a trend-following approach may allow a strategy to continue to participate in market appreciation while it lasts. On the other hand, using economic confirmation as a filter may help a strategy avoid the whipsaw costs generated from noisy market dips while positive economic conditions persist.

In an effort to test such a strategy out-of-sample, we took the approach global, hoping to capture a broader cross-section of economic and market environments.

First, we will consider trend following with no timing using the economic indicators.¹

Below we plot the equity curves for Australia, Germany, Italy, Japan, Singapore, the United Kingdom, and the United States, alongside a strategy that is long the market when the market is above the trailing twelve-month average (“12 Month average”) and steps to cash when the price is below it. The ratio between the two is also included to show the relative cumulative performance between the trend strategy and the respective market. An increasing ratio means that the trend following strategy is adding value over buy-and-hold.

Source: MSCI, Global Financial Data. Calculations by Newfound Research. Past performance is not an indicator of future results. Performance is backtested and hypothetical. Performance figures are gross of all fees, including, but not limited to, manager fees, transaction costs, and taxes. Performance assumes the reinvestment of all distributions.

Through the graphs above, it becomes clear that much of the trend premium is realized by avoiding the large, prolonged bear markets that tend to occur during economic distress. In between these periods, however, the trend strategy lags the market. It makes sense, then, that a potential improvement to this strategy would be to implement an augmentation that could better distinguish between real price break-outs and those that lead to a whipsaw in the portfolio.

Growth-Trend Timing

For each country, we look at a number of economic indicators, including: corporate earnings growth, employment, housing starts, industrial production, and retail sales growth.² The strategy then followed the same rules as described above: if the economic indicator in question displays a negative percentage change over the previous twelve-month period, a position is taken in a trend following strategy utilizing a twelve-month moving average signal. Otherwise, a buy-and-hold position is established.

To ensure that we are not benefitting from look-ahead bias, a lag of three months was imposed on each of the economic indicators, as it would be unrealistic to assume that the economic levels would be known at the end of each month.

Unfortunately, some of the economic data points could not be found for the entire period in which prices are available, though the analysis can still prove beneficial by indicating what economic regimes trend following is benefitted by growth-trend timing, or the potential identification where one indicator may work when another does not.³

In the charts below, we plot the growth-trend timing (referred to as GTT for the remainder of this commentary) for each country utilizing the available signals. The charts represent the relative cumulative performance over the respective country’s market return. For example, when the lines remain flat, the GTT approach has adopted buy-and-hold exposure and therefore matches the respective market’s returns. Any changes in the ratios are due to the GTT strategy investing in the trend following strategy.

Source: MSCI, Global Financial Data, St. Louis Fed, Bloomberg. Calculations by Newfound Research. Past performance is not an indicator of future results. Performance is backtested and hypothetical. Performance figures are gross of all fees, including, but not limited to, manager fees, transaction costs, and taxes. Performance assumes the reinvestment of all distributions.

What we see from the above figures is a mixed bag of results.

The overlay of economic indicators was by far successful in the mitigation of whipsaw losses, as each country reaped the benefits of being primarily long the market during bull markets. As the 12-month moving average strategy tended to slowly give up a portion of the gains realized from severe market environments, the majority of the GTT strategies remained relatively stagnant until the next major correction.

There are some instances, however, where the indicator was late to the economic party. It is worth remembering that the market is, in theory, a forward-looking measure, and therefore sudden economic shocks may not be captured in economic data as quickly as it is in market returns. This created cases where the strategy either missed the chance to be out of the market during a correction or was sitting on the sidelines during the subsequent recoveries. Notably, the employment signal in Australia, Italy, Singapore, and the United Kingdom tended to be a poor leading indicator as the strategy tended to be invested longer in the bear markets than the trend strategy.

A Candidate for Ensembling

The implicit assumption in the analysis above is that the included indicators behave in similar ways. For example, by using a twelve-month lookback period for the indicators, we are assuming that each indicator will begin to trend in roughly the same way.

That may not be a particularly fair assumption. Whereas housing starts and retail sales are generally considered leading indicators, employment (unemployment) rates are normally categorized as lagging indicators. For this reason, it may be more beneficial to use a shorter lookback period so as to pick up on potential problems in the economy as they begin to present themselves. Further, some signals tend to be more erratic than others, suggesting that a meaningful lookback period for one indicator may not be meaningful for another. With no perfect reason to prefer one lookback over another, we might consider different lookback periods so as to diversify any specification risk that may exist within the strategy.

With the benefit of hindsight, we know that not all recessions occur for the same reasons, so being reliant on one signal that has worked in the past may not be as beneficial in the future. With this in mind, we should consider that all indicators hold some information as to the state of the economy since one indicator may be signaling the all-clear while another may be flashing warning lights.

For the same reason medical professionals take multiple readings to gain insight into the state of the body, we should also consider any available signals to ascertain the health of the economy.

To ensemble this strategy, we will vary the lookbacks from six to eighteen months, while holding the lag at three months, as well as combine the available economic signals for each country. For the sake of brevity, we will hold the trend-following strategy the same with a twelve-month moving average.

Remember, if the economic signal is negative, it does not mean that we are immediately out of the market: a negative economic signal simply moves the strategy into a trend-following approach. With 5 economic indicators and 13 lookback periods, we have 65 possible strategies for each country. As an example, if 40 of these 65 models were positive and 25 were negative, we would hold 62% in the market and 38% in the trend following strategy.

The resulting performance statistics can be seen in the table below.

Source: MSCI, Global Financial Data, St. Louis Fed, Bloomberg. Calculations by Newfound Research. Past performance is not an indicator of future results. Performance is backtested and hypothetical. Performance figures are gross of all fees, including, but not limited to, manager fees, transaction costs, and taxes. Performance assumes the reinvestment of all distributions.

From the table above, we see that there are, again, mixed results. One country that particularly stands out is Italy in that the sign on its return flipped to negative and the drawdown was actually deeper with GTT than with a simple buy-and-hold strategy.

Digging deeper, it appears that the GTT strategy for Italy was actually whipsawed by more than just trend-following. Housing start data for Italy was not readily available until December 2008, so Italy may have been at a relative disadvantage when compared against the other countries. Since the reliable data we could find begins at the end of 2008 and the majority of the whipsaw losses occur post-Great Financial Crisis, we can run the analysis again, but with housing start data being added in upon its availability.

Adding housing starts in as an indicator did not meaningfully alter the results over the period. One hypothesis is that the indicators included could not fully encapsulate the complex state of Italy’s economy over the period. Italy has weathered three technical recessions over the past decade, so this could be a regime where the market is looking to sources outside the country for indications of distress or where the economic indicator is not reflective of the pressures driving the market.

Source: MSCI, St. Louis Fed. Calculations by Newfound Research. Past performance is not an indicator of future results. Performance is backtested and hypothetical. Performance figures are gross of all fees, including, but not limited to, manager fees, transaction costs, and taxes. Performance assumes the reinvestment of all distributions.

Above, we can see several divergences between the market movement and changes in real GDP. Specifically, in the past decade, we see that the market reacted to information that didn’t materialize in the country’s real GDP. More likely, the market was reacting to regional financial distress driven by debt concerns.

The MSCI Italy index is currently composed of 24 constituents with multinational business operations. Additionally, the index maintains large concentrations in financials, utilities, and energy: 33%, 25%, and 14%, respectively.⁴ Because of this sector concentration, utilizing the economic indicators may overly focus on the economic health of Italy while ignoring external factors such as energy prices or broader financial distress that could be swaying the market needle.

A parallel explanation could be that the Eurozone is entangled enough that signals could be interfering with each other between countries. Further research could seek to disaggregate signals between the Eurozone and the member-countries, attempting to differentiate between zone, regional, and country signals to ascertain further meaning.

Additionally, economic indicators are influenced by both the private and public sector so this could represent a disconnect between public company health and private company health.

Conclusion

In this commentary, we sought to answer the question, “can we improve trend-following by drawing information from a country’s economy”. It intuitively makes sense that an investor would generally opt for remaining in the market unless there are systemic issues that may lead to market distress. A strategy that successfully differentiates between market choppiness and periods of potential recession would drastically mitigate any losses incurred from whipsaw, thereby capturing a majority of the equity premium as well as the trend premium.

We find that growth-trend timing has been relatively successful in countries such as the United States, Germany, and Japan. However, the country that is being analyzed should be considered in light of their specific circumstances.

Peeking under the hood of Italy, it becomes clear that market movements may be influenced by more than a country’s implicit economic health. In such a case, we should pause and ask ourselves whether a macroeconomic indicator is truly reflective of that country’s economy or if there are other market forces pulling the strings.

Factor Orphans

By Corey Hoffstein

On October 28, 2019

In Risk & Style Premia, Weekly Commentary

This post is available as a PDF download here.

Summary

To generate returns that are different than the market, we must adopt a positioning that is different than the market.
With the increasing adoption of systematic factor portfolios, we explore whether an anti-factor stance can generate contrarian-based profits.
Specifically, we explore the idea of factor orphans: stocks that are not included in any factor portfolio at a given time.
To identify these stocks, we replicate four popular factor indices: the S&P 500 Enhanced Value index, the S&P 500 Momentum index, the S&P 500 Low Volatility index, and the S&P 500 Quality index.
On average, there are over 200 stocks in the S&P 500 that are orphaned at any given time.
Generating an equal-weight portfolio of these stocks does not exhibit meaningfully different performance than a naïve equal-weight S&P 500 portfolio.

Contrarian investing is nothing new. Holding a variant perception to the market is often cited as a critical component to generating differentiated performance. The question in the details is, however, “contrarian to what?”

In the last decade, we’ve witnessed a dramatic rise in the popularity of systematically managed active strategies. These so-called “smart beta” portfolios seek to harvest documented risk premia and market anomalies and implement them with ruthless discipline.

But when massively adopted, do these strategies become the commonly-held view and therefore more efficiently priced into the market? Would this mean that the variant perception would actually be buying those securities totally ignored by these strategies?

This is by no means a new idea. Morningstar has long maintained its Unloved strategy that purchases the three equity categories that have witnessed the largest outflows at the end of the year. A few years ago, Vincent Deluard constructed a “DUMB” beta portfolio that included all the stocks shunned by popular factor ETFs. In the short out-of-sample period the performance of the strategy was tested, it largely kept pace with an equal-factor portfolio. More recently, a Bank of America research note claimed that a basket of most-hated securities – as defined by companies neglected by mutual funds and shorted by hedge funds hedge funds – had tripled the S&P 500’s return over the past year.

The approach certainly has an appealing narrative: as the crowd zigs to adopt smart beta, we zag. But has it worked?

To test this concept, we wanted to identify what we call “factor orphans”: those securities not held by any factor portfolio. Once identified, we can build a portfolio holding these stocks and track its performance over time.

As a quant, this idea strikes us as a little crazy. A stock not held in a value, momentum, low volatility, or quality index is likely one that is expensive, highly volatile, with poor fundamentals and declining performance. Precisely the type of stock factor investing would tell us not to own.

But perhaps the fact that these securities are orphaned means that there are no more sellers: the major cross-section of market strategies have already abandoned the stock. Thus, stepping in to buy them may allow us to offload them later when they are picked back up by these systematic approaches.

Perhaps this idea is crazy enough it just might work…

To test this idea, we first sought to replicate four common factor benchmarks: the S&P 500 Enhanced Value index, the S&P 500 Momentum index, the S&P 500 Low Volatility index and the S&P 500 Quality index. Once replicated, we can use the underlying baskets as being representative of the holdings for factor portfolios is general.

Results of our replication efforts are plotted below. We can see that our models fit the shape of most of the indices closely, with very close fits for the Momentum and Low Volatility portfolios.

The Quality replication represents the largest deviation from the underlying index, but still approximates the shape of the total return profile rather closely. This gives us confidence that the portfolio we constructed is a quality portfolio (which should come as no surprise, as securities were selected based upon common quality metrics), but the failure to more closely replicate this index may represent a thorn in our ability to identify truly orphaned stocks.

At the end of each month, we identify the set of all securities held by any of the four portfolios. The securities in the S&P 500 (at that point in time) but not in the factor basket are the orphaned stocks. Somewhat surprisingly, we find that approximately 200 names are orphaned at any given time, with the number reaching as high as 300 during periods when underlying factors converge.

Also interesting is that the actual overlap in holdings in the factor portfolios is quite low, rarely exceeding 30%. This is likely due to the rather concentrated nature of the indices selected, which hold only 100 stocks at a given time.

Source: Sharadar. Calculations by Newfound Research.

Once our orphaned stocks are identified, we construct a portfolio that holds them in equal weight. We rebalance our portfolio monthly to sell those stocks that have been acquired by a factor portfolio and roll into those securities that have been abandoned.

We plot the results of our exercise below as well as an equally weighted S&P 500 benchmark.

While the total return is modestly less (but certainly not statistically significantly so), what is most striking is how little deviation there is in the orphaned stock portfolio versus the equal-weight benchmark.

However, as we have demonstrated in the past, the construction choices in a portfolio can have a significant impact upon the realized results. As we look at the factor portfolios themselves, we must acknowledge that they represent relative tilts to the benchmark, and that the absence of one security might actually represent a significantly smaller relative underweight to the benchmark than the absence of another. Or the absence of one security may actually represent a smaller relative underweight than another that is actually included.

Therefore, as an alternative test we construct an equal-weight factor portfolio and subtract the S&P 500 market-capitalization weights. The result is the implied over- and under-weights of the combined factor portfolios. We then rank securities to select the 100 most under-weight securities each month and hold them in equal weight.

Of course, we didn’t actually have to perform this exercise had we stepped back to think for a moment. We generally know that these (backtested) factors have out-performed the benchmark. Therefore, selecting stocks that they are underweight means we’re taking the opposite side of the factor trade, which we know has not worked.

Which does draw an important distinction between most underweight and orphaned. It would appear that factor orphans do not necessarily create the strong anti-factor tilt the way that the most underweight portfolio does.

For the sake of completion, we can also evaluate the portfolios containing securities held in just one of the factor portfolios, two of the factor portfolios, three of the factor portfolios, or all of the factor portfolios at a given time.

Below we plot the count of securities in such portfolios over time. We can see that it is very uncommon to identify securities that are simultaneously held by all the factors, or even three of the factors, at once.

Source: Sharadar. Calculations by Newfound Research.

We can see that the portfolio built from stocks held in just one factor (“In One”) closely mimics the portfolio built from stocks held in no factor (“In Zero”), which in turn mimics the S&P 500 Equal Weight portfolio. This is likely because the portfolios include so many securities that they effectively bring you back to the index.

On the other end of the spectrum, we see the considerable risks of concentration manifest in the portfolios built from stocks held in three or four of the factors. The portfolio comprised of stocks held in all four factors simultaneously (“In Four”) not only goes long stretches of holding nothing at all, but is also subject to large bouts of volatility due to the extreme concentration.

We also see this for the portfolio that holds stocks held by three of the factors simultaneously (“In Three”). While this portfolio has modestly more diversification – and even appears to out-perform the equal-weight benchmark – the concentration risk finally materializes in 2018-2019, causing a dramatic drawdown.

The portfolio holding stocks held in just two of the factors (“In Two”), though, appears to offer some out-performance opportunity. Perhaps by forcing just two factors to agree, we strike a balance between confirmation among signals and portfolio diversification.

Unfortunately, our enthusiasm quickly wanes when we realize that this portfolio closely matches the results achieved just by naively equally-weighting exposure among the four factor portfolios themselves, which is far more easily implemented.

Conclusion

To achieve differentiated results, we must take a differentiated stance from the market. As systematic factor portfolios are more broadly adopted, we should consider asking ourselves if taking an anti-factor stance might lead to contrarian-based profits.

In this study, we explore the idea of factor orphans: stocks not held by any factor portfolio at a given time. Our hypothesis is that these orphaned securities may be systematically over-sold, leading to an opportunity for future out-performance if they are re-acquired by the factor portfolios at a later date.

We begin by replicating four factor indices: the S&P 500 Enhanced Value index, the S&P 500 Momentum index, the S&P 500 Low Volatility index, and the S&P 500 Quality index. Replicating these processes allows us to identify historical portfolio holdings, which in turn allows us to identify stocks not held by the factors.

We are able to closely replicate the S&P 500 Momentum and Low Volatility portfolios, create meaningful overlap with the S&P 500 Enhanced Value method, and generally capture the S&P 500 Quality index. The failure to more closely replicate the S&P 500 Quality index may have a meaningful impact on the results herein, though we believe our methodology still captures the generic return of a quality strategy.

We find that, on average, there are over 200 factor orphans at a given time. Constructing an equal-weight portfolio of these orphans, however, only seems to lead us back to an S&P 500 Equal Weight benchmark. While there does not appear to be an edge in this strategy, it is interesting that there does not appear to be a negative edge either.

Recognizing that long-only factor portfolios represent active bets expressed as over- and underweights relative to the S&P 500, we also construct a portfolio of the most underweight stocks. Not surprisingly, as this portfolio actively captures a negative factor tilt, the strategy meaningfully underperforms the S&P 500 Equal Weight benchmark. Though the relative underperformance meaningfully dissipates in recent years.

Finally, we develop portfolios to capture stocks held in just one, two, three, or all four of the factors simultaneously. We find the portfolios comprised stocks held in either three or four of the factors at once exhibit significant concentration risk. As with the orphan portfolio, the portfolio of stocks held by just one of the factors closely tracks the S&P 500 Equal Weight benchmark, suggesting that it might be over-diversified.

The portfolio holding stocks held by just two factors at a time appears to be the Goldilocks portfolio, with enough concentration to be differentiated from the benchmark but not so much as to create significant concentration risk.

Unfortunately, this portfolio also almost perfectly replicates a naïve equal-weight portfolio among the four factors, suggesting that the approach is likely a wasted effort.

In conclusion, we find no evidence that factor orphans have historically offered a meaningful excess return opportunity. Nor, however, do they appear to have been a drag on portfolio returns either. We should acknowledge, however, that the adoption of factor portfolios accelerated rapidly after the Great Financial Crisis, and that backtests may not capture current market dynamics. More recent event studies of orphaned stocks being added to factor portfolios may provide more insight into the current environment.

Risk-Adjusted Momentum: A Momentum and Low-Volatility Barbell?

By Corey Hoffstein

On October 21, 2019

In Defensive, Momentum, Weekly Commentary

This post is available as a PDF download here.

Summary

After the Great Financial Crisis, the Momentum factor has exhibited positive returns, but those returns have been largely driven by the short side of the portfolio.
One research note suggests that this is driven by increased risk aversion among investors, using the correlation of high volatility and low momentum baskets as evidence.
In contradiction to this point, the iShares Momentum ETF (MTUM) has generated positive excess annualized returns against its benchmark since inception. The same note suggests that this is due to the use of risk-adjusted momentum measures.
We explore whether risk-adjusting momentum scores introduces a meaningful and structural tilt towards low-volatility equities.
For the examples tested, we find that it does not, and risk-adjusted momentum portfolios behave very similarly to momentum portfolios.

A research note recently crossed my desk that aimed to undress the post-Global Financial Crisis (GFC) performance of the momentum factor in U.S. equities. Not only have we witnessed a significant reduction in the factor’s return, but the majority of the return has been generated by the short side of the strategy, which can be more difficult for long-only investors to access.

Source: Sharadar. Calculations by Newfound Research. Past performance is not an indicator of future results. Performance is backtested and hypothetical. Performance figures are gross of all fees, including, but not limited to, manager fees, transaction costs, and taxes. Performance assumes the reinvestment of all distributions. The Long (Alpha) strategy is a monthly rebalanced portfolio that goes long, in equal weight, the top 50 securities in the S&P 500 ranked on 12-1 month momentum and shorts an equal-weight S&P 500 portfolio. The Short (Alpha) strategy is a monthly rebalanced portfolio that goes long an equal-weight S&P 500 portfolio and shorts, in equal weight, the bottom 50 securities in the S&P 500 ranked on 12-1 month momentum.

The note makes the narratively-appealing argument that the back-to-back recessions of the dot-com bubble and the Great Financial Crisis amplified investor risk aversion to downside losses. The proposed evidence of this fact is the correlation of the cumulative alpha generated from shorting low momentum stocks and the cumulative alpha generated from shorting high volatility stocks.

While correlation does not imply causation, one argument might be that in a heightened period of risk aversion, investors may consistently punish higher risk stocks, causing them to become persistent losers. Or, conversely, losers may be rapidly sold, creating both persistence and high levels of volatility. We can arguably see this in the convergence of holdings in low momentum and high volatility stocks during “risk off” regimes.

Source: Sharadar. Calculations by Newfound Research. Past performance is not an indicator of future results. Performance is backtested and hypothetical. Performance figures are gross of all fees, including, but not limited to, manager fees, transaction costs, and taxes. Performance assumes the reinvestment of all distributions. The HI VOL (Alpha) strategy is a monthly rebalanced portfolio that goes long an equal-weight S&P 500 portfolio and shorts, in equal weight, the bottom 50 securities in the S&P 500 ranked on trailing 252-day realized volatility. The LO MOM (Alpha) strategy is a monthly rebalanced portfolio that goes long an equal-weight S&P 500 portfolio and shorts, in equal weight, the bottom 50 securities in the S&P 500 ranked on 12-1 month momentum.

Given these facts, we would expect long-only momentum investors to have harvested little out-performance in recent years. Yet we find that the popular iShares Momentum ETF (MTUM) has out-performed the S&P 500 by 290 basis points per year since its inception in 2013.

The answer to this conundrum, as proposed by the research note, is that MTUM’s use of risk-adjusted momentum is the key.

If we think of risk-adjusted momentum as simply momentum divided by volatility (which is how MTUM defines it), we might interpret it as an integrated signal of both the momentum and low-volatility factors. Therefore, risk-adjusting creates a multi-factor portfolio that tilts away from high volatility stocks.

And hence the out-performance.

Except if we actually create a risk-adjusted momentum portfolio, that does not appear to really be the case at all.

Source: Sharadar. Calculations by Newfound Research. Past performance is not an indicator of future results. Performance is backtested and hypothetical. Performance figures are gross of all fees, including, but not limited to, manager fees, transaction costs, and taxes. Performance assumes the reinvestment of all distributions. The alpha of the risk-adjusted momentum strategy is defined as the return of a monthly rebalanced portfolio that goes long, in equal weight, the top 50 securities in the S&P 500 ranked on risk-adjusted momentum (12-1 month momentum divided by 252-day realized volatility) and shorts an equal-weight S&P 500 portfolio.

To be fair, MTUM’s construction methodology differs quite a bit from that employed herein. We are simply equally-weighting the top 50 stocks in the S&P 500 when ranked by risk-adjusted momentum, whereas MTUM uses a blend of 6- and 12-month risk-adjusted momentum scores and then tilts market-capitalization weights based upon those scores.

Nevertheless, if we look at actual holdings overlap over time of our Risk-Adjusted Momentum portfolio versus Momentum and Low Volatility portfolios, not only do we see persistently higher overlap with the Momentum portfolio, but we see fairly low average overlap with the Low Volatility portfolio.

For the latter point, it is worth first anchoring ourselves to the standard overlap between Momentum and Low Volatility (green line below). While we can see that the Risk-Adjusted Momentum portfolio does indeed have a higher average overlap with Low Volatility than does the Momentum portfolio, the excess tilt to Low Volatility due to the use of risk-adjusted momentum (i.e. the orange line minus the green line) appears rather small. In fact, on average, it is just 10%.

Source: Sharadar. Calculations by Newfound Research. Past performance is not an indicator of future results. Performance is backtested and hypothetical. Performance figures are gross of all fees, including, but not limited to, manager fees, transaction costs, and taxes. Performance assumes the reinvestment of all distributions. The risk-adjusted momentum strategy is a monthly rebalanced portfolio that goes long, in equal weight, the top 50 securities in the S&P 500 ranked on risk-adjusted momentum (12-1 month momentum divided by 252-day realized volatility). The momentum strategy is a monthly rebalanced portfolio that goes long, in equal weight, the top 50 securities in the S&P 500 ranked on 12-1 month momentum. The low volatility strategy is a monthly rebalanced portfolio that goes long, in equal weight, the top 50 securities in the S&P 500 ranked on trailing 252-day realized volatility.

This is further evident by looking at the actual returns of the strategies themselves:

Source: Sharadar. Calculations by Newfound Research. Past performance is not an indicator of future results. Performance is backtested and hypothetical. Performance figures are gross of all fees, including, but not limited to, manager fees, transaction costs, and taxes. Performance assumes the reinvestment of all distributions. The risk-adjusted momentum strategy is a monthly rebalanced portfolio that goes long, in equal weight, the top 50 securities in the S&P 500 ranked on risk-adjusted momentum (12-1 month momentum divided by 252-day realized volatility). The momentum strategy is a monthly rebalanced portfolio that goes long, in equal weight, the top 50 securities in the S&P 500 ranked on 12-1 month momentum. The low volatility strategy is a monthly rebalanced portfolio that goes long, in equal weight, the top 50 securities in the S&P 500 ranked on trailing 252-day realized volatility.

The Risk-Adjusted Momentum portfolio performance tracks that of the Momentum portfolio very closely.

As it turns out, the step of adjusting for risk creates far less of a low volatility factor tilt in our top-decile portfolio than one might initially suspect. (Or, at least, I’ll speak for myself: it created far less of a tilt than I expected.)

To understand this point, we will first re-write our risk-adjusted momentum signal as:

While trivial algebra, re-writing risk-adjusted momentum as the product of momentum and inverse volatility is informative to understanding why risk-adjusted momentum appears to load much more heavily on momentum than low volatility.

At a given point in time, it would appear as if Momentum and Low Volatility should have an equal influence on the rank of a given security. However, we need to dig a level deeper and consider how changes in these variables impact change in risk-adjusted momentum.

Fortunately, the product makes this a trivial exercise: holding INVVOL constant, changes in MOM are scaled by INVVOL and vice versa. This scaling effect can cause large changes in risk-adjusted momentum – and therefore ordinal ranking – particularly as MOM crosses the zero level.

Consider a trivial example where INVVOL is a very large number (e.g. 20) due to a security having a very low volatility profile (e.g. 5%). This would appear, at first glance, to give a security a structural advantage and hence create a low volatility tilt in the portfolio. However, a move from positive prior returns to negative prior returns would shift the security from ranking among the best to ranking among the worst in risk-adjusted momentum.¹

A first order estimate of change in risk-adjusted momentum is:

So which term ultimately has more influence on the change in scores over time?

To get a sense of relative scale, we plot the cross-sectional mean absolute difference between the two terms over time. This should, at least partially, capture interaction effects between the two terms.

Source: Sharadar. Calculations by Newfound Research.

We can see that the term including the change in MOM has a much more significant influence on changes in risk-adjusted momentum than changes in INVVOL do. Thus, we might expect a portfolio driven entirely by changes in momentum to share more in common with our risk-adjusted momentum portfolio than one driven entirely by changes in volatility.

This is somewhat evident when we plot the return of MTUM versus our top 50 style portfolios. The correlation of daily returns between MTUM and our Momentum, Low Volatility, and Risk-Adjusted Momentum portfolios is 0.93, 0.72, and 0.93 respectively, further suggesting that MTUM is driven more by momentum than volatility.

Source: Sharadar. Calculations by Newfound Research. Past performance is not an indicator of future results. Performance is backtested and hypothetical. Performance figures are gross of all fees, including, but not limited to, manager fees, transaction costs, and taxes. Performance assumes the reinvestment of all distributions. The risk-adjusted momentum strategy is a monthly rebalanced portfolio that goes long, in equal weight, the top 50 securities in the S&P 500 ranked on risk-adjusted momentum (12-1 month momentum divided by 252-day realized volatility). The momentum strategy is a monthly rebalanced portfolio that goes long, in equal weight, the top 50 securities in the S&P 500 ranked on 12-1 month momentum. The low volatility strategy is a monthly rebalanced portfolio that goes long, in equal weight, the top 50 securities in the S&P 500 ranked on trailing 252-day realized volatility.

This is only one part of the equation, however, as it is possible that changes to the risk-adjusted momentum score are so small – despite being largely driven by momentum – that relative rankings never actually change. Or, because we have constructed our portfolios by choosing only the top 50 ranked securities, that momentum does drive the majority of change across the entire universe, but the top 50 are always structurally advantaged by the non-linear scaling of low volatility.

To create a more accurate picture, we can rank-weight the entire S&P 500 and evaluate the holdings overlap over time.

Source: Sharadar. Calculations by Newfound Research.

Note that by now including all securities, and not just selecting the top 50, the overlap with both the Momentum and Low Volatility portfolios naturally appears higher on average. Nonetheless, we can see that the overlap with the Momentum portfolio is consistently higher than that of the Low Volatility portfolio, again suggesting that momentum has a larger influence on the overall portfolio composition than volatility does.

Conclusion

Without much deep thought, it would be easy to assume that a risk-adjusted momentum measure – i.e. prior returns divided by realized volatility – would tilt a portfolio towards both prior winners and low-volatility securities, resulting in a momentum / low-volatility barbell.

Upon deeper consideration, however, the picture complicates quickly. For example, momentum can be both positive and negative; dividing by volatility creates a non-linear impact; and momentum tends to change more rapidly than volatility.

We do not attempt to derive a precise, analytical equation that determines which of the two variables ultimately drives portfolio composition, but we do construct long-only example portfolios for empirical study. We find that a high-concentration risk-adjusted momentum portfolio has significantly more overlap in holdings with a traditional momentum portfolio than a low-volatility portfolio, resulting in a more highly correlated return stream.

The most important takeaway from this note is that intuition can be deceiving: it is important to empirically test our assumptions to ensure we truly understand the impact of our strategy construction choices.

Yield Curve Trades with Trend and Momentum

By Corey Hoffstein

On October 14, 2019

In Momentum, Term, Weekly Commentary

This post is available as a PDF download here.

Summary

Yield curve changes over time can be decomposed into Level, Slope, and Curvature changes, and these changes can be used to construct portfolios.
Market shocks, monetary policy, and preferences of different segments of investors (e,g. pensions) may create trends within these portfolios that can be exploited with absolute and relative momentum.
In this commentary, we investigate these two factors in long/short and long/flat implementations and find evidence of success with some structural caveats.
Despite this, we believe the results have potential applications as either a portable beta overlay or for investors who are simply trying to figure out how to position their duration exposure.
Translating these quantitative signals into a forecast about yield-curve behavior may allow investors to better position their fixed income portfolios.

It has been well established in fixed income literature that changes to the U.S. Treasury yield curve can be broken down into three primary components: a level shift, a slope change, and a curvature twist.

A level change occurs when rates increase or decrease across the entire curve at once. A slope change occurs when short-term rates decrease (increase) while long-term rates increase (decrease). Curvature defines convexity and concavity changes to the yield curve, capturing the bowing that occurs towards the belly of the curve.

Obviously these three components do not capture 100% of changes in the yield curve, but they do capture a significant portion of them. From 1962-2019 they explain 99.5% of the variance in daily yield curve changes.

We can even decompose longer-term changes in the yield curve into these three components. For example, consider how the yield curve has changed in the three years from 6/30/2016 to 6/30/2019.

Source: Federal Reserve of St. Louis.

We can see that there was generally a positive increase across the entire curve (i.e. a positive level shift), the front end of the curve increased more rapidly (i.e. a flattening slope change) and the curve flipped from concave to convex (i.e. an inverted bowing of the curve).

Using the historical yield curve changes, we can mathematically estimate these stylized changes using principal component analysis. We plot the loadings of the first three components below for this three-year change.

Source: Federal Reserve of St. Louis. Calculations by Newfound Research.

We can see that –PC1– has generally positive loadings across the entire curve, and therefore captures our level shift component. –PC2– exhibits negative loadings on the front end of the curve and positive loadings on the back, capturing our slope change. Finally, –PC3– has positive loadings from the 1-to-5-year part of the curve, capturing the curvature change of the yield curve itself.

Using a quick bit of linear algebra, we can find the combination of these three factors that closely matches the change in the curve from 6/30/2016 to 6/30/2019. Comparing our model versus the actual change, we see a reasonably strong fit.

Source: Federal Reserve of St. Louis. Calculations by Newfound Research.

So why might this be useful information?

First of all, we can interpret our principal components as if they are portfolios. For example, our first principal component is saying, “buy a portfolio that is long interest rates across the entire curve.” The second component, on the other hand, is better expressed as, “go short rates on the front end of the curve and go long rates on the back end.”

Therefore, insofar as we believe changes to the yield curve may exhibit absolute or relative momentum, we may be able to exploit this momentum by constructing a portfolio that profits from it.

As a more concrete example, if we believe that the yield curve will generally steepen over the next several years, we might buy 2-year U.S. Treasury futures and short 10-year U.S. Treasury futures. The biggest wrinkle we need to deal with is the fact that 2-year U.S. Treasury futures will exhibit very different sensitivity to rate changes than 10-year U.S. Treasury futures, and therefore we must take care to duration-adjust our positions.

Why might such changes exhibit trends or relative momentum?

During periods where arbitrage capital is low, trends may emerge. We might expect this during periods of extreme market shock (e.g. recessions) where we might also see the simultaneous influence of monetary policy.
Effects from monetary policy may exhibit autocorrelation. If investors exhibit any anchoring to prior beliefs, they might discount future policy changes.
Segmented market theory suggests that different investors tend to access different parts of the curve (e.g. pensions may prefer the far end of the curve for liability hedging purposes). Information flow may therefore be segmented, or even impacted by structural buyers/sellers, creating autocorrelation in curve dynamics.

In related literature, Fan et al (2019) find that the net hedging or speculative position has strong cross-sectional explanatory power for agricultural and currency futures returns, but not in fixed income markets. To quote,

“In sharp contrast, we find no evidence of a significant speculative pressure premium in the interest rate and fixed income futures markets. Thus, albeit from the lens of different research questions, our paper reaffirms Bessembinder (1992) and Moskowitz et al. (2012) in establishing that fixed income futures markets behave differently from other futures markets as regards the information content of the net positions of hedgers or speculators. A hedgers-to-speculators risk transfer in fixed income futures markets would be obscured if agents choose to hedge their interest rate risk with other strategies (i.e. immunization, temporary change in modified duration).”

Interestingly, Markowitz et al. (2012) suggest that speculators may be profiting from time-series momentum at the expense of hedgers, suggesting that they earn a premium for providing liquidity. Such does not appear to be the case for fixed income futures, however.

As far as we are aware, it has not yet been tested in the literature whether the net speculator versus hedger position has been tested for yield curve trades, and it may be possible that a risk transfer does not exist at the individual maturity basis, but rather exists for speculators willing to bear level, slope, or curvature risk.

Stylized Component Trades

While we know the exact loadings of our principal components (i.e. which maturities make up the principal portfolios), to avoid the risk of overfitting our study we will capture level, slope, and curvature changes with three different stylized portfolios.

To implement our portfolios, we will buy a basket of 2-, 5-, and 10-year U.S. Treasury futures contracts (“UST futures”). We will assume that the 5-year contract has 2.5x the duration of the 2-year contract and the 10-year contract has 5x the duration of the 2-year contract.

To capture a level shift in the curve, we will go long across all the contracts. Specifically, for every dollar of 2-year UST futures exposure we purchase, we will buy $0.4 of 5-year UST futures and $0.20 of 10-year UST futures. This creates equal duration exposure across the entire curve.

To capture slope change, we will go short 2-year UST futures and long the 10-year UST futures, holding zero position in the 5-year UST futures. As before, we will duration-adjust our positions such that for each $1 short of the 2-year UST futures position, we are $0.20 long the 10-year UST futures.

Finally, to capture curvature change we will construct a butterfly trade where we short the 2- and 10-year UST futures and go long the 5-year UST futures. For each $1 long in the 5-year UST futures, we will short $1.25 of 2-year UST futures and $0.25 of 10-year UST futures.

Note that the slope and curvature portfolios are implemented such that they are duration neutral (based upon our duration assumptions) so a level shift in the curve will generate no profit or loss.

An immediate problem with our approach arises when we actually construct these portfolios. Unless adjusted, the volatility exhibited across these trades will be meaningfully different. Therefore, we target a constant 10% volatility for all three portfolios by adjusting the notional exposure of each portfolio based upon an exponentially-weighted estimate of prior 3-month realized volatility.

Source: Stevens Futures. Calculations by Newfound Research. Past performance is not an indicator of future results. Performance is backtested and hypothetical. Performance figures are gross of all fees, including, but not limited to, manager fees, transaction costs, and taxes. Performance assumes the reinvestment of all distributions.

It appears, at least to the naked eye, that changes in the yield curve – and therefore the returns of these portfolios – may indeed exhibit positive autocorrelation. For example, –Slope– appears to exhibit significant trends from 2000-2004, 2004-to 2007, and 2007-2012.

Whether those trends can be identified and exploited is another matter entirely. Thus, with our stylized portfolios in hand, we can begin testing.

Trend Signals

We begin our analysis by exploring the application of time-series momentum signals across all three of the portfolios. We evaluate lookback horizons ranging from 21-to-294 trading days (or, approximately 1-to-14 months). Portfolios assume a 21-trading-day holding period and are implemented using 21 overlapping portfolios to control for timing luck.

Some observations:

Time-series momentum appears to generate positive returns for the Level portfolio. Over the period tested, longer-term measures (e.g. 8-to-14-month horizons) offer more favorable results.
Time-series momentum on the Level portfolio does, however, underperform naïve buy-and-hold. The returns of the strategy also do not offer a materially improved Sharpe ratio or drawdown profile.
Time-series momentum also appears to capture trends in the Slope portfolio. Interestingly, both short- and long-term lookbacks are less favorable over the testing period than intermediate-term (e.g. 4-to-8 month) ones.
Finally, time-series momentum appeared to offer no edge in timing curvature trades.

Here we should pause to acknowledge that we are blindly throwing strategies at data without much forethought. If we consider, however, that we might reasonably expect duration to be a positively compensated risk premium, as well as the fact that we would expect the futures to capture a generally positive roll premium (due to a generally upward sloping yield curve), then explicitly shorting duration risk may not be a keen idea.

In other words, it may make more sense to implement our level trade as a long/flat rather than a long/short. When implemented in this fashion, we see that the annualized return versus buy-and-hold is much more closely maintained while volatility and maximum drawdown are significantly reduced.

Taken together, it would appear that time-series momentum may be effective for trading the persistence in Level and Slope changes, though not in Curvature.

Momentum Signals

If we treat each stylized portfolio as a separate asset, we can also consider the returns of a cross-sectional momentum portfolio. For example, each month we can rank the portfolios based upon their prior returns. The top-ranking portfolio is held long; the 2^nd ranked portfolio is held flat; and the 3^rd ranked portfolio is held short.

As before, we will evaluate lookback horizons ranging from 21-to-294 trading days (approximately 1-to-14 months) and assuming a 21-trading-day holding period, implemented with 21 overlapping portfolios.

Results – as well as example allocations from the 7-month lookback portfolio – are plotted below.

Here we see very strong performance results except in the 1- and 2-month lookback periods. The allocation graph appears to suggest that results are not merely the byproduct of consistently being long or short a particular portfolio and the total return level appears to suggest that the portfolio is able to simultaneously profit from both legs.

If we return back to the graph of the stylized portfolios, we can see a significant negative correlation between the Level and Slope portfolios from 1999 to 2011. The negative correlation appears to disappear after this point, almost precisely coinciding with a 6+ year drawdown in the cross-sectional momentum strategy.

This is due to a mixture of construction and the economic environment.

From a construction perspective, consider that the Level portfolio is long the 2-, the 5-, and the 10-year UST futures while the Slope portfolio is short 2-year and long the 10-year UST futures. Since the positions are held in a manner that targets equivalent duration exposure, when the 2-year rate moves more than the 10-year rate, we end up in a scenario where the two trades have negative correlation, since one strategy is short and the other is long the 2-year position. Conversely, if the 10-year rate moves more than the 2-year rate, we end up in a scenario of positive correlation, since both strategies are long the 10-year.

Now consider the 1999-2011 environment. We had an easing cycle during the dot-com bust, a tightening cycle during the subsequent economic expansion, and another easing cycle during the 2008 crisis. This caused significantly more directional movement in the 2-year rate than the 10-year rate. Hence, negative correlation.

After 2008, however, the front end of the curve became pinned to zero. This meant that there was significantly more movement in the 10-year than the 2-year, leading to positive correlation in the two strategies. With positive correlation there is less differentiation among the two strategies and so we see a considerable increase in strategy turnover – and effectiveness – as momentum signals become less differentiated.

With that in mind, had we designed our Slope portfolio to be long 2-year UST futures and short 10-year UST futures (i.e. simply inverted the sign of our allocations), we would have seen positive correlation between Level and Slope from 1999 to 2011, resulting in a very different set of allocations and returns. In actually testing this step, we find that the 1999-2011 period is no longer dominated by Level versus Slope trades, but rather Slope versus Curvature. Performance of the strategy is still largely positive, but the spread among specifications widens dramatically.

Taken all together, it is difficult to conclude that the success of this strategy was not, in essence, driven almost entirely by autocorrelation in easing and tightening cycles with a relatively stable back end of the curve.¹ Given that there have only been a handful of full rate cycles in the last 20 years, we’d be reluctant to rely too heavily on the equity curve of this strategy as evidence of a robust strategy.

Conclusion

In this research note, we explored the idea of generating stylized portfolios designed to isolate and profit from changes to the form of the yield curve. Specifically, using 2-, 5-, and 10-year UST futures we design portfolios that aim to profit from level, slope, and curvature changes to the US Treasury yield curve.

With these portfolios in hand, we test whether we can time exposure to these changes using time-series momentum.

We find that while time-series momentum generates positive performance for the Level portfolio, it fails to keep up with buy & hold. Acknowledging that level exposure may offer a positive long-term risk premium, we adjust the strategy from long/short to long/flat and are able to generate a substantially improved risk-adjusted return profile.

Time-series momentum also appears effective for the Slope portfolio, generating meaningful excess returns above the buy-and-hold portfolio.

Applying time-series momentum to the Curvature portfolio does not appear to offer any value.

We also tested whether the portfolios can be traded employing cross-sectional momentum. We find significant success in the approach but believe that the results are an artifact of (1) the construction of the portfolios and (2) a market regime heavily influenced by monetary policy. Without further testing, it is difficult to determine if this approach has merit.

Finally, even though our study focused on portfolios constructed using U.S. Treasury futures, we believe the results have potential application for investors who are simply trying to figure out how to position their duration exposure. For example, a signal to be short (or flat) the Level portfolio and long the Slope portfolio may imply a view of rising rates with a flattening curve. Translating these quantitative signals into a forecast about yield-curve behavior may allow investors to better position their fixed income portfolios.

Since this study utilized U.S. Treasury futures, these results translate well to implementing a portable beta strategy. For example, if you were an investor with a desired risk profile on par with 100% equities, you could add bond exposure on top of the higher risk portfolio. This would add a (generally) diversifying return source with only a minor cash drag to the extent that margin requirements dictate.

Macro Timing with Trend Following

By Nathan Faber

On October 7, 2019

In Portfolio Construction, Trend, Weekly Commentary

This post is available for download here.

Summary

While it may be tempting to time allocations to active strategies, it is generally best to hold them as long-term allocations.
Despite this, some research has shown that there may be certain economic environments where trend following equity strategies are better suited.
In this commentary, we replicate this data and find that a broad filter of recessionary periods does indeed show this for certain trend equity strategies but not for the style of trend equity in general.
However, further decomposing the business cycle into contractions, recoveries, expansions, and slowdowns using leading economic indicators such as PMI and unemployment does show some promising relationships between the forecasted stage of the business cycle and trend following’s performance relative to buy-and-hold equities.
Even if this data is not used to time trend equity strategies, it can be beneficial to investors for setting expectations and providing insight into performance differences.

Systematic active investing strategies are a way to achieve alternative return profiles that are not necessarily present when pursuing standard asset allocation and may therefore play an important role in developing well-diversified portfolios.

But these strategies are best viewed as allocations rather than trades.¹ This is a topic we’ve written about a number of times with respect to factor investing over the past several years, citing the importance of weathering short-term pain for long-term gains. For active strategies to outperform, some underperformance is necessary. Or, as we like to say, “no pain, no premium.”

That being said, being tactical in our allocations to active strategies may have some value in certain cases. In one sense, we can view the multi-layered active decisions simply as another active strategy, distinct from the initial one.

An interesting post on Philosophical Economics looked at using a variety of recession indicators (unemployment, earnings growth, industrial production, etc.) as ways to systematically invest in either U.S. equities or a trend following strategy on U.S. equities. If the economic indicator was in a favorable trend, the strategy was 100% invested in equities. If the economic indicator was in an unfavorable trend, the strategy was invested in a trend following strategy applied to equities, holding cash when the market was in a downtrend.

The reasoning behind this strategy is intuitively appealing. Even if a recession indicator flags a likely recession, the market may still have room to run before turning south and warranting capital protection. On the other hand, when the recession indicator was favorable, purely investing in equities avoids some of the whipsaw costs that are inherent in trend following strategies.

In this commentary, we will first look at the general style of trend equity in the context of recessionary and non-recessionary periods and then get a bit more granular to see when trend following has worked historically through the economic cycle of Expansion, Slowdown, Contraction, and Recovery.

Replicating the Data

To get our bearings, we will first attempt to replicate some of the data from the Philosophical Economics post using only the classifications of “recession” and “not-recession”.

Keeping in line with the Philosophical Economics method, we will use whether the economic metric is above or below its 12-month moving average as the recession signal for the next month. We will use market data from the Kenneth French Data Library for the total U.S. stock market returns and the risk-free rate as the cash rate in the equity trend following model.

The following table shows the results of the trend following timing models using the United States ISM Purchasing Managers Index (PMI) and the Unemployment Rate as indicators.

	U.S. Equities	12mo MA Trend Equity	12m MA Trend Timing Model (PMI)	12mo MA Trend Timing Model (Unemployment)
Annualized Return	11.3%	11.1%	11.3%	12.2%
Annualized Volatility	14.7%	11.2%	11.9%	12.4%
Maximum Drawdown	50.8%	24.4%	32.7%	30.0%
Sharpe Ratio	0.49	0.62	0.61	0.66

Source: Quandl and U.S. Bureau of Labor Statistics. Calculations by Newfound Research. Results are hypothetical. Results assume the reinvestment of all distributions. Results are gross of all fees, including, but not limited to, manager fees, transaction costs, and taxes. Past performance is not an indicator of future results. You cannot invest in an index. Data is from Jan 1948 – Sep 2019.

With the trend timing model, we see an improvement in the absolute returns compared to the trend equity strategy alone. However, this comes at the expense of increasing the volatility and maximum drawdown.

In the case of unemployment, which was the strongest indicator that Philosophical Economics found, there is an improvement in risk-adjusted returns in the timing model.

Still, while there is a benefit, it may not be robust.

If we remove the dependence of the trend following model on a single metric or lookback parameter, the benefit of the macro-timing decreases. Specifically, if we replace our simple 12-month moving average trend equity rule with the ensemble approach utilized in the Newfound Trend Equity Index, we see very different results. This may indicate that one specific variant of trend following did well in this overall model, but the style of trend following might not lend itself well to this application.

	U.S. Equities	Newfound Trend Equity Index	Trend Equity Index Blend (PMI)	Trend Equity Index Blend (Unemployment)
Annualized Return	11.3%	10.7%	10.9%	10.9%
Annualized Volatility	14.7%	11.1%	11.8%	13.5%
Maximum Drawdown	50.8%	25.8%	36.1%	36.0%
Sharpe Ratio	0.49	0.59	0.58	0.50

A more robust trend following model may already provide more upside capture during non-recessionary periods but at the expense of more downside capture during recessions. However, we cannot confidently assert that the lower level of down-capture in the single specification of the trend model is not partially due to luck.

If we desire to more thoroughly evaluate the style of trend following, we must get more granular with the economic cycles.

Breaking Down the Economic Cycle

Moving beyond the simple classification of “recession” and “not-recession”, we can follow MSCI’s methodology, which we used here previously, to classify the economic cycle into four primary states: Expansion, Slowdown, Contraction and Recovery.

We will focus on the 3-month moving average (“MA”) minus the 12-month MA for each indicator we examine according to the decision tree below. In the tree, we use the terms better or worse since lower unemployment rate and higher PMI values signal a stronger economy.

There is a decent amount of difference in the classifications using these two indicators, with the unemployment indicator signaling more frequent expansions and slowdowns. This should be taken as evidence that economic regimes are difficult to predict.

Once each indicator is in each state the transition probabilities are relatively close.

Source: Quandl and U.S. Bureau of Labor Statistics. Calculations by Newfound Research. Results are hypothetical. Past performance is not an indicator of future results.

This agrees with intuition when we consider the cyclical nature of these economic metrics. While not a perfect mathematical relationship, these states generally unfold sequentially without jumps from contractions to expansions or vice versa.

Trend Following in the Economic Cycle

Applying the four-part classification to the economic cycle shows where trend equity outperformed.

	PMI Indicator		Unemployment Indicator
	U.S. Equities	Trend Equity	U.S. Equities	Trend Equity
Contraction	7.6%	10.3%	1.0%	7.3%
Recovery	12.2%	9.3%	15.4%	15.0%
Expansion	14.3%	14.4%	13.9%	11.3%
Slowdown	7.2%	5.4%	10.5%	8.0%

During contraction phases, regardless of indicators, trend equity outperformed buy-and-hold.

For the PMI indicator, trend equity was able to keep up during expansions, but this was not the case with the unemployment indicator. The reverse of this was true for recoveries: trend following was close to keeping up in the periods denoted by the unemployment indicator but not by the PMI indicator.

For both indicators, trend following underperformed during slowdowns.

This may seem contradictory at first, but these may be periods of more whipsaw as markets try to forecast future states. And since slowdowns typically occur after expansions and before contractions (at least in the idealized model), we may have to bear more of this whipsaw risk for the strategy to be adaptable enough to add value during the contraction.

The following two charts show the longest historical slowdowns for each indicator: the PMI indicator was for 11 months in late 2009 through much of 2010 and the unemployment rate indicator was for 16 months in 1984-85.

In the first slowdown period, the trend equity strategy rode in tandem with equities as they continued to climb and then de-risked when equities declined. Equities quickly rebounded leaving the trend equity strategy underexposed to the rally.

In the second slowdown period, the trend equity strategy was heavily defensive going into the slowdown. This protected capital initially but then caused the strategy to lag once the market began to increase steadily.

The first period illustrates a time when the trend equity strategy was ready to adapt to changing market conditions and was unfortunately whipsawed. The second period illustrates a time when the trend equity strategy was already adapted to a supposedly oncoming contraction that did not materialize.

Using these historical patterns of performance, we can now explore how a strategy that systematically allocates to trend equity strategies might be constructed.

Timing Trend Following with the Economic Cycle

One simple way to apply a systematic timing strategy for shifting between equities and trend following is to only invest in equities when a slowdown is signaled.

The charts below show the returns and risk metrics for models using the PMI and unemployment rate individually and a model that blends the two allocations.

The returns increased slightly in every model relative to buy-and-hold, and the blended model performed consistently high across all metrics.

Blending multiple models generally produces benefits like these shown here, and in an actual implementation, utilizing additional economic indicators may make the strategy even more robust. There may be other ways to boost performance across the economic cycle, and we will explore these ideas in future research.

Conclusion

Should investors rotate in and out of active strategies?

Not in most cases, since the typical drivers are short-term underperformance that is a necessary component of active strategies.

However, there may be opportunities to make allocation tweaks based on the economic cycle.

The historical data suggests that a specification-neutral trend-equity strategy has outperformed buy-and-hold equities during economic contractions for both economic indicators. The performance during recoveries and expansions was mixed across indicators. It kept up with the buy-and-hold strategy during expansions denoted by PMI but not unemployment. This relationship was reversed for recoveries denoted by unemployment. In both models, trend equity has also lagged during economic slowdowns as whipsaw becomes more prevalent.

Based on the most recent PMI data, the current cycle is a contraction, indicating a favorable environment for trend equity under both cycle indicators. However, we should note that December 2018 through March 2019 was also labeled as a contraction according to PMI. Not all models are perfect.

Nevertheless, there may be some evidence that trend following can provide differentiated benefits based on the prevailing economic environment.

While an investor may not use this knowledge to shift around allocations to active trend following strategies, it can still provide insight into performance difference relative to buy-and-hold and set expectations going forward.

Category: Weekly Commentary Page 5 of 21

Global Growth-Trend Timing

Summary­

Growth-Trend Timing

A Candidate for Ensembling

Conclusion

Factor Orphans

Summary­

Conclusion

Risk-Adjusted Momentum: A Momentum and Low-Volatility Barbell?

Summary

Conclusion

Yield Curve Trades with Trend and Momentum

Summary­

Stylized Component Trades

Trend Signals

Momentum Signals

Conclusion

Macro Timing with Trend Following

Summary

Replicating the Data

Breaking Down the Economic Cycle

Trend Following in the Economic Cycle

Timing Trend Following with the Economic Cycle

Conclusion

Summary

Summary

Summary