Macro Timing with Trend Following

Nathan Faber

5 years ago

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.

https://blog.thinknewfound.com/2016/02/active-strategies-allocation-not-trade/

Nathan Faber

Nathan is a Portfolio Manager at Newfound Research. At Newfound, Nathan is responsible for investment research, strategy development, and supporting the portfolio management team. Prior to joining Newfound, he was a chemical engineer at URS, a global engineering firm in the oil, natural gas, and biofuels industry where he was responsible for process simulation development, project economic analysis, and the creation of in-house software. Nathan holds a Master of Science in Computational Finance from Carnegie Mellon University and graduated summa cum laude from Case Western Reserve University with a Bachelor of Science in Chemical Engineering and a minor in Mathematics.