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Summary­

  • In this commentary, we ask whether we should consider rotating factor exposure based upon the business cycle.
  • To eliminate a source of model risk, we assume perfect knowledge of future recessions, allowing us to focus only on whether prevailing wisdom about which factors work during certain economic phases actually adds value.
  • Using two models of factor rotation and two definitions of business cycles, we construct four timing portfolios and ultimately find that rotating factor exposures does not add meaningful value above a diversified benchmark.
  • We find that the cycle-driven factor rotation recommendations are extremely close to data-mined optimal results. The similarity of the recommendations coupled with the lackluster performance of conventional style timing recommendations may highlight how fragile the rotation process inherently is.

Just as soon as the market began to meaningfully adopt factor investing, someone had to go and ask, “yeah, but can they be timed?”  After all, while the potential opportunity to harvest excess returns is great, who wants to live through a decade of relative drawdowns like we’re seeing with the value factor?

And thus the great valuation-spread factor timing debates of 2017 were born and from the ensuing chaos emerged new, dynamic factor rotation products.

There is no shortage of ways to test factor rotation: valuation-spreads, momentum, and mean-reversion to name a few.  We have even found mild success using momentum and mean reversion, though we ultimately question whether the post-cost headache is worth the potential benefit above a well-diversified portfolio.

Another potential idea is to time factor exposure based upon the state of the economic or business cycle.

It is easy to construct a narrative for this approach.  For example, it sounds logical that you might want to hold higher quality, defensive stocks during a recession to take advantage of the market’s flight-to-safety.  On the other hand, it may make sense to overweight value during a recovery to exploit larger mispricings that might have occurred during the contraction period.

An easy counter-example, however, is the performance of value during the last two recessions.  During the dot-com fall-out, cheap out-performed expensive by a wide margin. This fit a wonderful narrative of value as a defensive style of investing, as we are buying assets at a discount to intrinsic value and therefore establishing a margin of safety.

Of course, we need only look towards 2008 to see a very different scenario.  From peak to trough, AQR’s HML Devil factor had a drawdown of nearly 40% during that crisis.

Two recessions with two very different outcomes for a single factor.  But perhaps there is still hope for this approach if we diversify across enough factors and apply it over the long run.

The problem we face with business cycle style timing is really two-fold.  First, we have to be able to identify the factors that will do well in a given market environment.  Equally important, however, is our ability to predict the future economic environment.

Philosophically, there are limitations in our ability to accurately identify both simultaneously.  After all, if we could predict both perfectly, we could construct an arbitrage.

If we believe the markets are at all efficient, then being able to identify the factors that will out-perform in a given state of the business cycle should lead us to conclude that we cannot predict the future state of the business cycle. Similarly, if we believe we can predict the future state of the business cycle, we should not be able to predict which factors will necessarily do well.

Philosophical arguments aside, we wanted to test the efficacy of this approach. 

Which Factors and When?

Rather than simply perform a data-mining exercise to determine which factors have done well in each economic environment, we wanted to test prevalent beliefs about factor performance and economic cycles.  To do this, we identified marketing and research materials from two investment institutions that tie factor allocation recommendations to the business cycle.

Both models expressed a view using four stages of the economic environment: a slowdown, a contraction, a recovery, and an economic expansion.

Model #1

  • Slowdown: Momentum, Quality, Low Volatility
  • Contraction: Value, Quality, Low Volatility
  • Recovery: Value, Size
  • Expansion: Value, Size, Momentum

Model #2

  • Slowdown: Quality, Low Volatility
  • Contraction: Momentum, Quality, Low Volatility
  • Recovery: Value, Size
  • Expansion: Value, Size, Momentum

Defining the Business Cycle

Given these models, our next step was to build a model to identify the current economic environment.  Rather than build a model, however, we decided to dust off our crystal ball. After all, if business-cycle-based factor rotation does not work with perfect foresight of the economic environment, what hope do we have for when we have to predict the environment?

We elected to use the National Bureau of Economic Research’s (“NBER”) listed history of US business cycle expansions and contractions.  With the benefit of hindsight, they label recessions as the peak of the business cycle prior to the subsequent trough.

Unfortunately, NBER only provides a simple indicator as to whether a given month is in a recession or not.  We were left to fill in the blanks around what constitutes a slowdown, a contraction, a recovery, and an expansionary period.  Here we settled on two definitions:

Definition #1

  • Slowdown: The first half of an identified recession
  • Contraction: The second half of an identified recession
  • Recovery: The first third of a non-recessionary period
  • Expansion: The remaining part of a non-recessionary period

Definition #2

  • Slowdown: The 12-months leading up to a recession
  • Contraction: The identified recessionary periods
  • Recovery: The 12-months after an identified recession
  • Expansion: The remaining non-recessionary period

For definition #2, in the case where two recessions were 12 or fewer months apart (as was the case in the 1980s), the intermediate period was split equivalently into recovery and slowdown.  

Implementing Factor Rotation

After establishing the rotation rules and using our crystal ball to identify the different periods of the business cycle, our next step was to build the factor rotation portfolios.

We first sourced monthly long/short equity factor returns for size, value, momentum, and quality from AQR’s data library.  To construct a low-volatility factor, we used portfolios sorted on variance from the Kenneth French library and subtracted bottom-quintile returns from top-quintile returns.

As the goal of our study is to identify the benefit of factor timing, we de-meaned the monthly returns by the average of all factor returns in that month to identify relative performance.

We constructed four portfolios using the two factor rotation definitions and the two economic cycle definitions.  Generically, at the end of each month, we would use the next month’s economic cycle label to identify which factors to hold in our portfolio.  Identified factors were held in equal weight.

Below we plot the four equity curves.  Remember that these series are generated using de-meaned return data, so reflect the out-performance against an equal-weight factor benchmark.

 Source: NBER, AQR, and Kenneth French Data Library. Calculations by Newfound Research. Returns are backtested and hypothetical. Returns assume the reinvestment of all distributions.  Returns are gross of all fees.  None of the strategies shown reflect any portfolio managed by Newfound Research and were constructed solely for demonstration purposes within this commentary.  You cannot invest in an index.

It would appear that even with a crystal ball, conventional wisdom about style rotation and business cycles may not hold.  And even where it might, we can see multi-decade periods where it adds little-to-no value.

Data-Mining Our Way to Success

If we are going to use a crystal ball, we might as well just blatantly data-mine our way to success and see what we learn along the way.

To achieve this goal, we can simply look at the annualized de-meaned returns of each factor during each period of the business cycle.

Source: NBER, AQR, and Kenneth French Data Library.  Calculations by Newfound Research.  Returns are backtested and hypothetical.  Returns assume the reinvestment of all distributions.  Returns are gross of all fees.  None of the strategies shown reflect any portfolio managed by Newfound Research and were constructed solely for demonstration purposes within this commentary.  You cannot invest in an index.

Despite two different definitions of the business cycle, we can see a strong alignment in which factors work when.  Slow-downs / pre-recessionary periods are tilted towards momentum and defensive factors like quality and low-volatility.  Momentum may seem like a curious factor, but its high turnover may give it a chameleon-like nature that can tilt it defensively in certain scenarios.

In a recession, momentum is replaced with value while quality and low-volatility remain. In the initial recovery, small-caps, value, and momentum are favored.  In this case, while value may actually be benefiting from multiple expansion, small-caps may simply be a way to play higher beta.  Finally, momentum is strongly favored during an expansion.

Yet even a data-mined solution is not without its flaws.  Below we plot rolling 3-year returns for our data-mined timing strategies.  Again, remember that these series are generated using de-meaned return data, so reflect the out-performance against an equal-weight factor benchmark.

Source: NBER, AQR, and Kenneth French Data Library.  Calculations by Newfound Research.  Returns are backtested and hypothetical.  Returns assume the reinvestment of all distributions.  Returns are gross of all fees.  None of the strategies shown reflect any portfolio managed by Newfound Research and were constructed solely for demonstration purposes within this commentary.  You cannot invest in an index.

Despite a crystal ball telling us what part of the business cycle we are in and completely data-mined results, there are still a number of 3-year periods with low-to-negative results.  And we have not even considered manager costs, transaction costs, or taxes yet.

A few more important things to note.

Several of these factors exhibit strong negative performance during certain parts of the market cycle, indicating a potential for out-performance by taking the opposite side of the factor.  For example, value appears to do poorly during pre-recession and expansion periods.  One hypothesis is that during expansionary periods, markets tend to over-extrapolate earnings growth potential, favoring growth companies that appear more expensive.

We should also remember that our test is on long/short portfolios and may not necessarily be relevant for long-only investors.  While we can think of a long-only portfolio as a market-cap portfolio plus a long/short portfolio, the implicit long/short is not necessarily identical to academic factor definitions.

Finally, it is worth considering that these results are data-mined over a 50+ year period, which may allow outlier events to dramatically skew the results.  Momentum, for example, famously exhibited dramatic crashes during the Great Depression and in the 2008-crisis, but may have actually relatively out-performed in other recessions.

Conclusion

In this commentary we sought to answer the question, “can we use the business cycle to time factor exposures?”  Assuming access to a crystal ball that could tell us where we stood precisely in the business cycle, we found that conventional wisdom about factor timing did not add meaningful value over time.  We do not hold out much hope, based on this conventional wisdom, that someone without a crystal ball would fare much better.

Despite explicitly trying to select models that reflected conventional wisdom, we found a significant degree of similarity in these recommendations with those that came from blindly data-mining optimal results.  Nevertheless, even slight recommendation differences lead to lackluster results.

The similarities between data-mined results and conventional wisdom, however, should give us pause.  While the argument for conventional wisdom is often a well-articulated economic rationale, we have to wonder whether we have simply fooled ourselves with a narrative that has been inherently constructed with the benefit of hindsight.

Corey is co-founder and Chief Investment Officer of Newfound Research, a quantitative asset manager offering a suite of separately managed accounts and mutual funds. At Newfound, Corey is responsible for portfolio management, investment research, strategy development, and communication of the firm's views to clients. Prior to offering asset management services, Newfound licensed research from the quantitative investment models developed by Corey. At peak, this research helped steer the tactical allocation decisions for upwards of $10bn. Corey holds a Master of Science in Computational Finance from Carnegie Mellon University and a Bachelor of Science in Computer Science, cum laude, from Cornell University. You can connect with Corey on LinkedIn or Twitter. Or schedule a time to connect.