EquitiesFundamental Analysis

Sector Rotation

Can a sector rotation strategy improve performance of investment returns? In this article the author looks at sector rotation within the US markets.

Investors can most certainly use a sector rotation strategy to produce returns which outperform the market, or even some hedge funds, despite what was described in a recent article. Many hedge funds would be wise to consider such a strategy. However it’s not as easy as the plan described in the article?s abstract. Nor should we expect it to be; the market is not easy-pickings. But recognize what market returns we are talking about: since say 1990, the S&P has had about an 8 pct compounded annual rate of return, while experiencing about a 46 pct maximum drawdown. Those numbers can be beaten, and not just by the pros. As with any game, being an informed participant enhances your success. Without getting too far into proprietary methodologies, let me provide some insight as to what a sector rotator must consider (with a few tips included):

Is the investor going to be fully invested all the time, or does he have an escape? That is, should the first set of sectors be equities vs. treasuries. Let’s call that a strategic overlay, or the first on-off switch. Then, is the investment going to be long-only, long with a hedge, or long-short?

Next comes the equities universe. What group are you considering: 10 S&P economy Sectors, the 24 S&P Industry Groups, the larger (ever-changing) number of Industries and Sub-Industries (GICs), high liquidity ETFs of economic sectors, country ETFs, Fidelity funds, Dow Jones 18 European sectors, or small cap funds accepting new money. The list can be endless. We have rotated all of the above, and can attest that the hardest of the lot is the 10 S&P Sectors. We would avoid as much as possible rotating a list of just four, as was done in the referenced article.  If you are going to rotate sectors profitably, you need more focus.

Once you have chosen your universe, you have to pick an out-of-sample period and run cross correlations on the assets. The cross correlations should not be on the levels, but either the changes or preferably the scoring that you will eventually use. Your purpose here is to reduce the likelihood of always being in the same combinations of assets. You wouldn’t mind doing so if they were all moving up together, but the opposite would destroy you, and must be avoided. This is the only subjective part of the entire process: you will personally have to decide the level of cross correlation you will accept. You can of course test this also (a huge amount of work), but there will be obvious breakpoints that make sense to the experienced researcher. Thus if you start out with the 67 S&P Industry GICs, cross correlation may reduce that number down to about half. This should be done blind. Also, some of the 67 have only one stock, and you may want to eliminate such a sector.

The choice of ranking or scoring device is the most critical part of the entire process. Most of the industry professionals we know use a "quarterly" rate of change or relative strength calculation. For example, we have been told that R***x uses a moving 63-day rate of change for their sector rotation fund on 67 GICs culled down to 56. They have passable results which outperform the market, and several hundred million in that fund alone. The problem with either moving rate of change or relative strength is that those calculations produce fairly erratic scores. They can be improved by some slight smoothing prior to ranking, or by skipping days (e.g. scoring and ranking every other day), but your task is to find what works best, and there are lots of things that work better than rate of change.   We have done some work with counting as a ranking device, but our experience is that using counting works best only if we choose to be particularly risk-averse in a long-only program. Oh, and don’t assume that a ranking/scoring device is just one indicator, as combinations work best.

One ranking device we have found absolutely knocks the cover off the ball, but it is not obvious. Although it is very robust and clearly non-random, we don’t yet understand why it works, and are reluctant to ?bet the farm? until we do.  You should be equally cautious in a similar situation.

Should the sectors be risk-adjusted? If telecoms and utilities have equal rankings on a given day, do you want to discriminate in favor of the least volatile, say by subtracting half a moving standard deviation? Our results show that doing so on sectors reduces both returns and drawdowns. That’s unexpected, as usually reducing drawdowns and increasing returns are handmaidens. However if you are ranking proprietary funds, risk-adjusting outperforms not doing so across the board. That is, penalizing managers for bad behavior really works. This suggests that (a) certain managers really have talent and fat tails, and (b) they can be discovered by some quant work.

Then comes the question as to how many assets out of our population are going to be traded. This can be tested empirically. R***x conducted research which suggested that 3-5 sectors was optimal. Yet R***x uses 8 out of their 67-culled-to-56, probably for marketing reasons (so we’ve been told). Our results show that whatever number you pick, numbers in that vicinity work well too, so it’s robust. We generally recommend using at least 3 assets to reduce volatility. But using more than 10 percent of the population curtails returns. Some of the other professionals we have spoken with have told us that their "second quadrant" outperforms their "first quadrant". Should that happen to you, you need to do more work at finding a better scoring method.

However many assets you choose to hold, add an equal number of money-market assets. That is, if you choose to buy 3 assets, then to your culled population, add another 3 assets, all consisting of money-market. Then rank the whole lot. If your top 3 assets are X, Y, and money market, then buy that mix. If an equity asset cannot outperform money-market, don’t buy it. This means you will have to construct an asset consisting of the compounded effect of money-market, which is a great thing to have in your toolbox anyway. Some professionals do not like holding money-market assets in a client’s portfolio for marketing reasons. If a client sees a large portion of his assets in cash, he’s inclined to find something else (usually wrong) to buy with it. There are also some programs that rank say 15 assets side-by-side with 15 money-markets. Then if the market tanks, you will probably be in mostly cash before that happens. That is a fairly conservative way to go, usually producing acceptable, albeit low, returns, but with very low drawdowns. Risk-averse types take notice.

How frequently do you look to change the assets? Recasting the investments everyday is not typically the best choice (as you will choose a lot of one-day-wonders), but there is a sweet spot that is robust. Once you get beyond the sweet spot, performance does degrade.  Interestingly, on some programs, recasting as infrequently as monthly isn’t all that bad. That is, it still beats the S&P hands down, and a whole lot of hedge funds to boot.
A variation on the question as to how many assets to hold, is the percentage allocation among those held assets. Equally weighting the allocations may be the first choice to consider, but it is certainly not the last. Then you have to deal with rebalancing the equity among the assets and the frequency of that rebalancing. The academic literature just on the frequency of rebalancing is quite substantial.  In other words, you can use academic literature to shortcut some research, but expect to be doing a fair amount of reading.
If you consider a hedged strategy, you have to choose whether you are going to hedge initial equity only (never readjusted for equity changes), a full hedge (adjusted daily for all changes), or somewhere in-between. The frequency of hedging is also a variable that should be tested. Upon doing so you will also find a sweet spot that is robust. Readjusting the hedge can also be subjected to an on-off switch. That is, if your strategic overlay says that equities look weak, the switch forces you to go to a fully hedged condition from less so.  Then consider the hedging vehicle:  will you use an inverse fund or derivatives?  Think of the hedging vehicle as insurance, and the price of that insurance determined by the leverage of the hedge.  Thus, an inverse fund (preferred by individuals) is high-priced insurance, and derivatives (favored by the pros) are inexpensive.  Both work equally well, but lower costs produce higher returns.

What is best depends on the yardstick used by the investor. The investor may seek to maximize returns, minimize drawdowns, maximize the ratio of the two, or some other statistic. Success is absolutely achievable. For example, it is certainly possible to create a program in which the compound annual rate of return exceeds twice the maximum drawdown, or with a Sharpe Ratio north of 2.

Given all of the degrees of freedom discussed above, it would certainly be naive to expect a simple four-sector program chosen on the basis of relative strength to produce hedge fund returns. It is wrong for the abstract to imply that investment success cannot be achieved. However, the author is right in that the typical investor cannot use such a simple strategy to produce superior performance. But that’s not because of a flawed concept.  Rather it’s because the typical investor is not up to the task of doing the research necessary.

The author has chosen not to have their article rated or discussed.

William Rafter has managed investment funds for over 30 years, running the gamut from futures to equities to debt.  He is President of Mathematical Investment Decisions, Inc. a trader of proprietary funds, and a developer of Financial Data Calculator financial research software for the investment community.  Financial research is his main interest, and he has authored several articles in that area.  info@mathinvestdecisions.comwww.mathinvestdecisions.com

William Rafter has managed investment funds for over 30 years, running the gamut from futures to equities to debt.  He is President of Mathematical I...