By Corey Hoffstein, Newfound Research
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.