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Summary­

  • Systematic value strategies have struggled in the post-2008 environment, so one that has performed well catches our eye.
  • The Barclays Shiller CAPE sector rotation strategy – a value-based sector rotation strategy – has out-performed the S&P 500 by 267 basis points annualized since it launched in 2012.
  • The strategy applies a unique Relative CAPE metric to account for structural differences in sector valuations as well as a momentum filter that seeks to avoid “value traps.”
  • In an effort to derive the source of out-performance, we explore various other valuation metrics and model specifications.
  • We find that what has actually driven performance in the past may have little to do with value at all.

It is no secret that systematic value investing of all sorts has struggled as of late.  With the curious exception, that is, of the Barclays Shiller CAPE sector rotation strategy, a strategy explored by Bunn, Staal, Zhuang, Lazanas, Ural and Shiller in their 2014 paper Es-cape-ing from Overvalued Sectors: Sector Selection Based on the Cyclically Adjusted Price-Earnings (CAPE) Ratio.  Initial performance suggests that the idea has performed quite well out-of-sample, which stands out among many “smart-beta” strategies which have failed to live up to their backtests.

Source: CSI Data.  Calculations by Newfound Research.  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.  

Why is this strategy finding success where other value strategies have not?  That is what we aim to explore in this commentary.

On a monthly basis, the Shiller CAPE sector rotation portfolio is rebalanced into an equal-weight allocation across four of the ten primary GICS sectors.  The four are selected first by ranking the 10 primary sectors based upon their Relative CAPE ratios and choosing the cheapest five sectors.  Of those cheapest five sectors, the sector with the worst trailing 12-month return (“momentum”) is removed.

The CAPE ratio – standing for Cyclically-Adjusted Price-to-Earnings ratio – is the current price divided by the 10-year moving average of inflation-adjusted earnings.  The purpose of this smoothing is to reduce the impact of business cycle fluctuations.

The potential problem with using the raw CAPE value for each sector is that certain sectors have structurally higher and lower CAPE ratios than their peers.  High growth sectors – e.g. Technology – tend to have higher CAPE ratios because they reinvest a substantial portion of their earnings while more stable sectors – e.g. Utilities – tend to have much lower CAPE ratios.  Were we to simply sort sectors based upon their current CAPE ratio, we would tend to create structural over- and under-weights towards certain sectors.

To adjust for this structural difference, the strategy uses the idea of a Relative CAPE ratio, which is calculated by taking the current CAPE ratio and dividing it by a rolling 20-year average CAPE ratio1 for that sector.  The thesis behind this step is that dividing by a long-term mean normalizes the sectors and allows for better comparison.  Relative CAPE values above 1 mean that the sector is more expensive than it has historically been, while values less than 1 mean it is cheaper.

It is important to note here that the actual selection is still performed on a cross-sector basis.  It is entirely possible that all the sectors appear cheap or expensive on a historical basis at the same time.  The portfolio will simply pick the cheapest sectors available.

Poking and Prodding the Parameters

With an understanding of the rules, our first step is to poke and prod a bit to figure out what is really driving the strategy.

We begin by first exploring the impact of using the Relative CAPE ratio versus just the CAPE ratio.

For each of these ratios, we’ll plot two strategies.  The first is a naïve Value strategy, which will equally-weight the four cheapest sectors.  The second is the Shiller strategy, which chooses the top five cheapest sectors and drops the one with the worst momentum.  This should provide a baseline for comparing the impact of the momentum filter.

Strategy returns are plotted relative to the S&P 500.

Source: Siblis Research; Morningstar; CS Data.  Calculations by Newfound Research.  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.  

For the Relative CAPE ratio, we also vary the lookback period for calculating the rolling average CAPE from 5- to 20-years.

Source: Siblis Research; Morningstar; CSI Data.  Calculations by Newfound Research.  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.  

A few things immediately stand out:

  • Interestingly, standard CAPE actually appears to perform better than Relative CAPE for both the traditional value and Shiller implementations.
  • The Relative CAPE approach fared much more poorly from 2004-2007 than the simple CAPE approach.
  • There is little difference in performance for the Value and Shiller strategy for standard CAPE, but a meaningful difference for Relative CAPE.
  • While standard CAPE value has stagnant relative performance since 2007, Relative CAPE appears to continue to work for the Shiller approach.
  • A naïve value implementation seems to perform quite poorly for Relative CAPE, while the Shiller strategy appears to perform rather well.
  • There is meaningful performance dispersion based upon the lookback period, with longer-dated lookbacks (darker shades) appearing to perform better than shorter-period lookbacks (lighter shades) for the Relative CAPE variation.

The second-to-last point is particularly curious, as it implies that using momentum to “avoid the value trap” creates significant value (no pun intended; okay, pun intended) for the strategy.

Varying the Value Metric (in Vain)

To gain more insight, we next test the impact of the choice of the CAPE ratio. Below we plot the relative returns of different Shiller-based strategies (again varying lookbacks from 5- to 20-years), but use price-to-book, trailing 12-month price-to-earnings, and trailing 12-month EV/EBITDA as our value metrics.

A few things stand out:

  • Value-based sector rotation seems to have “worked” from 2000 to 2009, regardless of our metric of choice.
  • Almost all value-based strategies appear to exhibit significant relative out-performance during the dot-com and 2008 recessions.
  • After 2009, most value strategies appear to exhibit random relative performance versus the S&P 500.
  • All three approaches appear to suffer since 2016.

Source: Siblis Research; Morningstar; CSI Data.  Calculations by Newfound Research.  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.  

At this point, we have to ask: is there something special about the Relative CAPE that makes it inherently superior to other metrics?

A Big Bubble-Based Bet?

If we take a step back for a moment, it is worth asking ourselves a simple question: what would it take for a sector rotation strategy to out-perform the S&P 500 over the last decade?

With the benefit of hindsight, we know Consumer Discretionary and Technology have led the pack, while traditionally stodgy sectors like Consumer Staples and Utilities have lagged behind (though not nearly as poorly as Energy).

As we mentioned earlier, a naïve rank on the CAPE ratio would almost certainly prefer Utilities and Staples over Technology and Discretionary.  Thus, for us to outperform the market, we must somehow construct a value metric that identifies the two most chronically expensive sectors (ignoring back-dated valuations for the new Communication Services sector) as being among the cheapest.

This is where dividing by the rolling 20-year average comes into play.  In spirit, it makes a certain degree of sense. In practice, however, this plays out perfectly for Technology, which went through such an enormous bubble in the late 1990s that the 20-year average was meaningfully skewed upward by an outlier event.  Thus, for almost the entire 20-year period after the dot-com bubble, Technology appears to be relatively cheap by comparison.  After all, you can buy for 30x earnings today what you used to be able to buy for 180x!

The result is a significant – and near-permanent tilt – towards Technology since the beginning of 2012, which can be seen in the graph of strategy weights below.

One way to explore the impact of this choice is calculate the weight differences between a top-4 CAPE strategy and a top-4 Relative CAPE strategy, which we also plot below.  We can see that after early 2012, the Relative CAPE strategy is structurally overweight Technology and underweight Financials and Utilities.  Prior to 2008, we can see that it is structurally underweight Energy and overweight Consumer Staples.

If we take these weights and use them to construct a return stream, we can isolate the return impact the choice of using Relative CAPE versus CAPE has.  Interestingly, the long Technology / short Financials & Utilities trade did not appear to generate meaningful out-performance in the post-2012 era, suggesting that something else is responsible for post-2012 performance.

Source: Siblis Research; Morningstar; CSI Data.  Calculations by Newfound Research.  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.  

The Miraculous Mojo of Momentum

This is where the 12-month momentum filter plays a crucial role.  Narratively, it is to avoid value traps.  Practically, it helps the strategy deftly dodge Financials in 2008, avoiding a significant melt-down in one of the S&P 500’s largest sectors.

Now, you might think that valuations alone should have allowed the strategy to avoid Technology in the dot-com fallout.  As it turns out, the Technology CAPE fell so precipitously that in using the Relative CAPE metric the Technology sector was still ranked as one of the top five cheapest sectors from 3/2001 to 11/2002.  The only way the strategy was able to avoid it?  The momentum filter.

Removing this filter makes the relative results a lot less attractive.  Below we re-plot the relative performance of a simple “top 4” Relative CAPE strategy.

Source: Siblis Research; Morningstar; CSI Data.  Calculations by Newfound Research.  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.  

Just how much impact does the momentum filter have?  We can isolate the effect by taking the weights of the Shiller strategy and subtracting the weights of the Value strategy to construct a long/short index that isolates the effect.  Below we plot the returns of this index.

It should be noted that the legs of the long/short portfolio only have a notional exposure of 25%, as that is the most the Value and Shiller strategies can deviate by.  Nevertheless, even with this relatively small weight, when isolated the filter generates an annualized return of 1.8% per year with an annualized volatility of 4.8% and a maximum drawdown of 11.6%.

Scaled to a long/short with 100% notional per leg, annualized returns jump to 6.0%. Though volatility and maximum drawdown both climb to 20.4% and 52.6% respectively.

Source: Siblis Research; Morningstar; CSI Data.  Calculations by Newfound Research.  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.  

Conclusion

Few, if any, systematic value strategies have performed well as of late.  When one does – as with the Shiller CAPE sector rotation strategy – it is worth further review.

As a brief summary of our findings:

  • Despite potential structural flaws in measuring cross-sectional sector value, CAPE outperformed Relative CAPE for a naïve rank-based value strategy.
  • There is significant dispersion in results using the Relative CAPE metric depending upon which lookback parameterization is selected.Initial tests suggest that the longer lookbacks appear to have been more effective.
  • Using valuation metrics other than CAPE – e.g. P/B, P/E (TTM), and EV/EBITDA (TTM) – do not appear as effective in recent years.
  • Longer lookbacks allow the Relative CAPE methodology to create a structural overweight to the Technology sector over the last 15 years.
  • The momentum filter plays a crucial role in avoiding the Technology sector in 2001-2002 and the Financial sector in 2008.

 

Taken all together, it is hard to not question whether these results are unintentionally datamined.  Unfortunately, we just do not have enough data to extend the tests further back in time for truly out-of-sample analysis.

What we can say, however, is that the backtested and live performance hinges almost entirely a few key trades:

  • Avoiding Technology in 2001-2002 due to the momentum filter.
  • Avoiding Financials in 2008 due to the momentum filter.
  • Avoiding a Technology underweight in recent years due to an inflated “average” historical CAPE due to the dot-com bubble.
  • Avoiding Energy in 2014-2016 due to the momentum filter.

 

Three of these four trades are driven by the momentum filter.  When we further consider that the Shiller strategy is in effect the returns of the pure value implementation – which suffered in the dot-com run-up and was a mostly random walk thereafter – and the returns of the isolated momentum filter, it becomes rather difficult to call this a value strategy at all.


As of the date of this document, neither Newfound Research nor Corey Hoffstein holds a position in the securities discussed in this article and do not have any plans to trade in such securities.  Newfound Research and Corey Hoffstein do not take a position as to whether this security should be recommended for any particular investor.  


  1. Winsorized at the 5thpercentile

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.