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  • Sector rotation is a popular investment strategy whereby managers actively reallocate capital from one investment sector to another based upon changing market conditions.
  • There are many ways to run sector rotation strategy, including: business cycle indicators, macroeconomic indicators, value-based, momentum-based, trend-following, et cetera.
  • Anecdotally, relative strength (momentum) systems are one of the most popular approaches.
  • Utilizing a construction technique similar to the single-stock momentum factor, we ask whether sector rotation is meaningfully different than the momentum factor.
  • We find that sector rotation is entirely subsumed by the momentum factor, but may still be an interesting approach for investors willing to sacrifice outperformance potential in effort to reduce exposure to momentum crashes.

Sector rotation is an investment strategy where a manager actively re-allocates capital from one investment sector to another.  In this case, by sector we mean a group of securities that share a common line of business – or set of risk factors – and are therefore expected to perform similarly to one another.

What drives the rotation decisions is unique to each manager; there are a variety of possible approaches.  Some managers use macro-economic indicators.  Others use market cycle analysis (an approach we think has little merit).  Others look at long-term value opportunities.  Some even employ discretionary, thematic views.

At Newfound, we leverage a trend-following approach in our sector strategies that focuses specifically on trying to mitigate downside risk.  Since our approach has the ability to rotate to short-term Treasuries, it is not comparable to the equity-only strategies that we consider in this piece.

Anecdotally, the most popular form of sector rotation we encounter is driven by relative strength (or, more academically, cross-sectional momentum).  In a relative strength system, securities are ranked by their recent relative performance and winners are purchased and losers are avoided (or sold short).

Part of the allure of sector rotation is not only the empirical evidence that supports it, but also the ease with which it can be implemented.  There are a number of mutual funds and ETFs available for investors to express sector exposure and most simple relative strength rotation systems are only rebalanced monthly.

However, with the “true” momentum factor – relative strength applied to individual securities – becoming more easily accessible today, we have to ask: is there a reason to keep running sector rotation strategies?

After all, how different could the two be?


Data & Methodology

Industry data for this study comes from the Kenneth French Data Library.  Factor data comes from AQR’s data library.

Long-short sector rotation portfolios are built by investing in the top 30% of sectors ranked on their trailing “12-1” total return and shorting the bottom 30% of sectors ranked on this same metric.  Sectors are given equal weight within the portfolio such that the long and short portfolios are dollar neutral.  Portfolios are reformed monthly.


Starting with 49 Sectors

As a first step, we want to explore a sector rotation system that has the least amount of clustering: i.e. the one that is closest to the standard definition of the momentum factor.

Our hypothesis is that because stocks within the same narrowly defined industry groups share very similar risk factors, we would expect that they would be relative winners and losers at the same time.  Furthermore, since theoretical finance argues that investors should not be compensated for taking on idiosyncratic risk, implementing momentum with fine-grained industry groups may provide a better risk-adjusted return through the benefits of diversification.

Source: Kenneth French Data Library and AQR.  Calculations by Newfound Research.  Returns are gross of management and transaction fees and assume the reinvestment of all dividends.  Past performance is not a guarantee of future returns.


Surprisingly, the 49ROT system actually outperforms the UMD system, with annualized returns of 7.54% and 6.95%, respectively.  Perhaps more impressively is that 49ROT also exhibits lower annualized volatility: 15.29% versus 17.96%.

That said, we can clearly see that the performance of 49ROT was significantly helped by the outlier period of 1943-1944, and if we begin our analysis after 12/1944, 49ROT actually underperforms UMD by about 0.8% a year.  However, this reduction in return is met with a commensurate reduction in risk, with both strategies exhibiting nearly identical annualized Sharpe ratios.

Perhaps most attractive, however, is the significant reduction in risk the 49ROT system exhibited in the 2008-2009 momentum crash, when UMD fell 58.26%.  Over the same period, 49ROT only fell 40.46%: still a significant drawdown, but nearly 1/3rd less than the traditionally defined momentum factor.

Regressing the 49ROT system on the size, value (AQR’s “HML Devil”), and momentum factors, we find a significant negative loading on size (-0.12; 99%), a non-significant loading on value, and a significant positive loading on momentum (0.68; 99%).  Most importantly, 49ROT has a significantly positive intercept of 0.0026 per month (99%), indicating unexplained alpha of 0.26% per month.

If we evaluate only the post-1944 time period, however, we find that the alpha becomes non-significant.  Therefore, a more fair expectation for 49ROT might be a similar long-term risk-adjusted return profile as UMD, but with slightly lower total return and potentially less risk during momentum crashes (though 1932 stands in stark contrast to 2008).


Working with Fewer Sectors

While the 49 industry groups may be our closest approximation to the standard UMD factor as implemented via sectors, it is likely non-tractable for investors looking to implement via mutual funds or ETFs.  A more realistic version may be the 30- or 17-group definitions.

Source: Kenneth French Data Library and AQR.  Calculations by Newfound Research.  Returns are gross of management and transaction fees and assume the reinvestment of all dividends.  Past performance is not a guarantee of future returns.


We can see that as we decrease the number of sectors, we also decrease return.  While UMD has an annualized return of 6.95%, 30ROT and 17ROT have annualized returns of 6.03% and 3.86%, respectively.  Interestingly, while 30ROT exhibits a commensurate reduction in risk, 17ROT does not, and therefore has significantly reduced risk-adjusted returns.

Focusing again on the post-1944 period, we see that the sector rotation strategies again shine in the 2008-2009 momentum crash.  Furthermore, as we reduce the number of sectors – and therefore reduce our exposure to the pure momentum factor – the strategy drawdown is reduced.  Interestingly, however, the 30ROT system has a lower drawdown than the 17ROT system: 36.12% and 39.06%, respectively.

It is worth pointing out, however, that both the 17ROT and 30ROT (“nROT”) systems have significant alphas when regressed against size and value factors, but non-significant alphas when momentum is introduced.  Furthermore, while the nROT systems lose their alpha with the introduction of momentum, the reverse is not true: when momentum is regressed against size, value, and nROT, statistically significant alpha remains.

We can say that while the nROT systems are subsumed by momentum, momentum is not subsumed by nROT.  Therefore, while the nROT systems have alpha, the alpha exhibited is not novel or unique and is simply a proxy for traditional momentum.


The Primary 10 Sectors

As a final test, we will evaluate a sector rotation system built using only the primary 10 sectors of the market.  Most popular ETF-based systems we see today are similar to this approach.

Source: Kenneth French Data Library and AQR.  Calculations by Newfound Research.  Returns are gross of management and transaction fees and assume the reinvestment of all dividends.  Past performance is not a guarantee of future returns.


What we can see is that while the 10-sector rotation method has been a generally profitable long/short strategy over the last 90 years, it has dramatically underperformed the single-stock momentum factor.

Over the full period, 10ROT only returned 3.72% per year.  Furthermore, 10ROT did not significantly reduce volatility: UMD had an annualized volatility of 17.96% while 10ROT had an annualized volatility of 14.02%.  So the drop in annualized return was not met with a commensurate drop in volatility.

However, where 10ROT may shine may be in the reduction of downside risk.  During the “momentum crash” periods, 10ROT had max drawdowns -57.53% and -34.62%: superior to all the other strategies evaluated.

Of course, whether that reduction in drawdown is worth the significant reduction in return potential is up for the investor to decide.  Particularly since 10ROT has a non-statistically significant alpha in the same sense that 30ROT and 17ROT did not.



We believe that relative strength-based sector rotation strategies are popular because they have significant empirical evidence, follow a seductive narrative about business cycles, and are fairly simple to implement.

In this study, however, we find that momentum-based sector rotation strategies are largely explained by the momentum factor, with no significant novel or unique alpha contributed by the strategies themselves.

We generally find that as the number of sectors decreases – with the implementation moving further away from a traditional momentum implementation and increasing internal diversification – total return drops, as does drawdown.

However, volatility and drawdown reduction are not necessarily commensurate with return reduction.  We can see – particularly in 1932 – that sector rotation is still susceptible to momentum crashes.  Based upon this, we could argue that sector rotation dilutes momentum returns without significantly diluting the risks.

That said, a -57.53% drawdown and a -34.62% drawdown seen by the UMD and 10ROT strategies in 2008-2009 are significantly different results, and investors looking to access the momentum factor while diversifying away some of the crash risk may find sector rotation strategies well suited.

We want to reiterate that our analysis herein has only been about relative strength based sector rotation systems and does not necessarily hold true for those based upon macro-economic indicators, value, trend-following, or other factor approaches.

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 is a frequent speaker on industry panels and contributes to, ETF Trends, and’s Great Speculations blog. He was named a 2014 ETF All Star by

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.