Exactly 6 months ago we released the Relative Strength Reader application.    We review the example done then in the video below:

 

 

(original video is on the Tools Tab if didn't see the original).

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The business cycle favors different segments of the investment landscape at different times. Good relative strength analysis helps read money flows to give a view on how the market is indicating where we are in the business cycle in terms of stocks, bonds & commodities.

We highlighted the Deutsche Bank Commodity Index ETN (DBC) late last month and let's now take a closer look. One thing about the exchange traded market is that unlike being a stock generalist where various names turn over and you may never come back to some of them, what you learn now about the core ETF/ETN products will likely remain relevant for the rest of your investing life. One such family of indexes to get to know is the Deutsche Bank Commodity indices.

The DB Commodity index has four distinct components: Energy, Agriculture, Base Metals & Precious Metals. Each component has the convenient symbol structure of DB_ with the final letter telling you what it is. ie, DB[E] is Energy.

Deutsche Bank built DBC based on fourteen of the most heavily traded and important physical commodities in the world. "The Index commodity components were chosen based on the depth and liquidity of their markets and to provide diversified commodity performance." It makes sense that since Energy is the most economically important commodity group in the world, it should get the largest weight. That said, Deutsche Bank found a balance for diversification purposes and kept the energy component at 55%.

Below is a graph of the components and performance for this month. You can see that while precious metals have been strong (particularly Silver SLV), that the 10% weighting of Precious Metals is relatively small. The real driver here has been energy. Agriculture ( DBA) is down for the month but this has been dwarfed by the strength in the energy complex.

 

While commodities are less correlated to stocks than many other indices are to stocks, it should be pointed out that both commodities and stocks are at the core related to economic strength. That is, a bad economy reduces demand for commodities and also hurts corporate earnings, which negatively affects stocks. Correlations over time can be erratic so we need to be careful on assuming too much with regard to the diversification benefits of commodities.

 

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Last summer, we added a feature to the portfolio relative strength backtest application. We added a line that showed what the 'provisional picks' would be if the update period had just ended. The provisional picks listed are the exact same as if you ran the ETF screener using the same list of ETFs and same parameters. We added this feature just as a convenience so it wouldn't be necessary to open another tab and check what was strongest now -- rather than at the last update period.

Depending on the list you use, it is of course possible that the 'provisional pick' is not the final pick (hence the term 'provisional'). But what if we just looked up the provisional pick on the next-to-last day of a given month and bought that ETF the next day on the close and held it for the subsequent period? What would the performance look like assuming we did that? Would it be similar?

Let's look at a simple example just to discuss the mechanics of what we mean. We will compare using the 'next-to-last day picks' with the 'last day picks.' The holding periods will be the exact same, we are just reading the picks of the ETF screener with a one day offset. Of course, for many periods the picks (and therefore performance) will be the exact same.

In this example, we will use one of the Ivy Portfolio lists of 5 basic ETFs and a semi-monthly update period. We are choosing the top 1 of 5 and holding it on 2-week intervals. The settings are the exact same except we are checking the box "Invest in next to last day pick(s) near the top of images below.

Using Regular 'Last Day' Picks:

Using Next To Last Day Picks:

We can see that the returns follow similar paths but that there was a difference -- even for this list of just 5 ETFs. In this case, the next-to-last day picks actually performed better -- which means very little in and of itself. We simply are pointing out the mechanics of how it works. Users should test this using their lists across various other settings and draw their own conclusions. Our view is that this doesn't change anything -- if your backtest is well thought-out, then this extra analysis will very likely show a result that is in the same ballpark as the original method.

Final note is that today is Friday, February 25th -- so the next-to-last days picks will be locked in and known after todays close. The final picks will update using Monday's (Feb 28) closing price. In both cases, the backtests will assume the cost-basis for March performance is the closing price as of Feb 28.

 

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We have added an optional moving average (MA) filter feature to the RS backtest app.  With the recently expanded date start and stop functionality, the applications continue to get more versatile.  

Combining a long-term moving average within the construct of relative strength has been highly requested and we wanted to discuss one idea when considering whether to use it (note that you can just leave it set to ‘off’ as well).  

If you build a relative strength list of say 10 ETFs and you are choosing the top 2,  you could protect your portfolio by including 2 bond funds.   You don’t need a moving average filter because the bond funds will naturally be the ones with the relative strength when equity markets are dropping.   This method actually can get more interesting because you can make better use of more type of ETFs.   Rather than just use cash-like bond funds, you might want to extend the potential holdings to an intermediate bond fund like IEF (7-8 year duration) or others. You don't HAVE to restrict yourself to just stocks and cash.   

Another way to test is by using a moving average.   If you do it this way, then you will inherently be out of ETFs as they go into extended downtrends.  You don’t have to proportionally keep X number of bond funds in your list if you do it this way.    

But a lot of indexes can go above or below a long-term moving average and still not really be a source of market leadership and enhance your return.   Moreover, you may save a lot of money between the time ETFs lose relative strength and the time they actually cross below the moving average.   For these reasons, we believe adding relative strength to a MA strategy will generally be more robust.   Adding MA to a RS strategy is optional -- and you may find works better or worse than your existing method.   Continuous testing leads to better decisions.

 

 

 

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Some links to published research on various forms of relative strength concepts. The concept is over a century old. (I am not aware of any studies that involve ETFs directly -- let us know if you know of one).

 

 

http://pages.stern.nyu.edu/~lpederse/papers/TimeSeriesMomentum.pdf

http://www.cambriainvestments.com/research/

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1363476 Copy-paste URL into browser.

http://www.aqr.com/Download.aspx?link_id=4

 

Abstract from paper #1 above 'Time Series Momentum':

 

"We document significant "time series momentum" in equity index, currency, commodity, 

and bond futures for each of the 58 liquid instruments we consider. We find persistence 

in returns for 1 to 12 months that partially reverses over longer horizons, consistent with 

sentiment theories of initial under-reaction and delayed over-reaction. A diversified 

portfolio of time series momentum strategies across all asset classes delivers substantial 

abnormal returns with little exposure to standard asset pricing factors, and performs best 

during extreme markets.  We show that the returns to time series momentum are closely 

linked to the trading activities of speculators and hedgers, where speculators appear to 

profit from it at the expense of hedgers.  "

 

 

A typical buyside analyst looks at the world in terms of business fundamentals, earnings estimates, quality of management, competitive position, competitive advantage, return on invested capital, P/E multiples and so forth.  

You will often hear analysts talk about P/E’s being low relative to growth rates or low vs 10-year bond yields or low vs a group of comparable companies or the overall market etc.

Is the price/earnings (P/E) multiple a good indicator for future performance?   One interpretation of this fundamental valuation measure is rather than thinking in terms of the current valuation vs ‘fair value’ --- is simply to observe whether the valuation is expanding or contracting and decide if you believe this will continue.

If a P/E has just gone from 14x to 12x, its cheaper – but is that bullish?  How do we know its not headed lower?  We should probably test this and see if it works and then create some guidelines for portfolio strategy based on this.   Wouldn’t that make sense rather than just blindly believing that a lower multiple is bullish?

Relative strength can actually be thought of as fundamental analysis if you wish to think about it like that.   It would not be unusual for a fundamental analyst to say during a rally “the P/E multiple is still too low at 12x next years earnings” -- even though it’s up from a low of 9x and you bought the stock at 10x.  But this is not any different than watching relative strength and buying at the same price and looking for further valuation expansion.

Good relative strength analysis captures when a market segment is experiencing valuation expansion.  A low P/E is not actually bullish unless its low and then expands higher.  This is the crucial aspect – not the absolute level of valuation. 

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We have received this question a number of times in email and so we wanted to clarify something.

R.S.I. (known as the Relative Strength Index) and the way we at ETFreplay.com discuss ‘Relative Strength’ are not the same thing.   We wanted to briefly explain this.

R.S.I. is a technical analysis tool that involves only a single security  --  it measures the average amount of up closes vs the average down closes over a given period of time.   The most important distinction here is that R.S.I. only looks at the closing values of one security.  There is nothing in the calculation of R.S.I. that involves anything but the historical prices of this SINGLE security.

The way ETFreplay uses relative strength has nothing to do with R.S.I.   We use relative strength as a way to determine which among multiple market segments is relatively strong.

Many of you are probably familiar with Investors Business Daily.   The paper has for a long period of time used a ‘RS Rank’ --- this is more like what we use.   Note that IBD certainly did not invent the concept of relative strength  --  but they built a database of securities and then ranked everything relative to each other.   Institutional-oriented software programs do the same thing.  A RS Rank of 90 in IBDs method means that a stock has outperformed 90% of the other securities in their database over a given period of time.  High-end institutional software does something similar – except they proceed more mathematically by instead expressing the strength of the security as the distance from the average of a group  --- and this is usually stated in terms of the # of standard deviations away from the mean (think z-score).

This is all similar to what we have done – except we do it with ETFs and then allow you to backtest it yourself rather than just saying ‘you should buy relative strength because it works.’   We also allow the user to define relative strength themselves – using easy browser controls like drop-down menus and text boxes.   So for example you could simulate IBD’s method by using 12-month performance and ranking ETFs in a given universe like this:

ETFreplay Screener

Then you could go the ETFreplay.com backtesting module and see how 12-month relative strength has been holding up over the past 7+ years and see what kind of drawdowns its had.  

This is all a research process – its just that we are performing research that comes in a very practical form.   We aren’t researching stocks, we are researching strategies --- strategies based on baskets of stocks.   Backtesting is not the only thing that matters --- but its pretty darn good information for you to factor into your decision-making process.   Without some historical testing, you could easily go a lifetime of doing things that you thought worked – but actually don’t -- and never really did.

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We showed a few examples of some very basic relative strength techniques at a recent investor event.

This example was meant to show a case of a relative strength strategy that easily could have been thought of as unbiased at any point in past 10 years -- or right now for that matter.

For simplicity, we have used the global regional ETFs and have simply covered the major parts of the 'developed world'.   We know that emerging markets have been excellent performers over the past 7-8 years -- as have selected other assets like Gold -- but we have intentionally left these out.   We wanted to show something that had some poor performing picks, a few good ones and therefore representative of someone who didn't pick ETFs particularly well but implemented a sound technique to stay with leaders and avoid large underperformers.

Importantly, we believe investors SHOULD decide first which ETFs they would like to be involved with and exclude those they have no interest in on a fundamental basis -- this is value-add to a basic, mechanical technique such as this.   

So for this example we use the World Index as a starting point and seek to just 'cover' the developed regions:

Current Approximate Weightings in Global Indexes are:

1) United States                 42.0%

2) Developed Europe         25.0%

3) Japan                              7.0%

4) Developed Pacific [1]       7.0%

5) Canada                          4.5%

***Emerging Markets       Excluded

[1] Developed Pacific is ~97% Australia/Hong Kong/Singapore and 3% 'other' (New Zealand etc..)

From an indexing perspective, Canada is always kind of the lonely child.   Canada is generally not included within a broader regional ETF -- and is not generally lumped with the United States for a North American ETF.   For conservatism here, I will exclude Canada and use the first 4 only.  These are logical regions in our view.   We get coverage of many different countries within this regional framework so this seems quite fair and hardly something that could not have been thought of many years ago -- or even today.

We will include the first 4 from above and SHY, a <2-year duration U.S. Treasury ETF which will act as a benchmark for positive returns.   If no region of the world is beating 2-year maturity short-term fixed-income -- then SHY will by default be thought to be the highest Relative Strength ETF.  

Here is the result using a 6-mo/3-mo 2-factor relative strength model with monthly re-balancing.

Note all historical results are purely hypothetical and meant to show the mechanics of the backtesting application.  This does not represent investment advice.

Symbol list here is:  SPY,EWJ,IEV,EPP,SHY

 

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The issue with ‘value-add’ in investment management is complicated.  Strong performance by stock-pickers is in many cases due to implementation of a particular strategy (such as strongly overweighting ‘financials’ or ‘foreign stocks’). 

If a manager does a good job in finding good strategies such as ‘financials’ -- that is, financial stocks do well – then do we really care that much about the extra return on top of that of stock-picking?   Remember, stock-pickers who perform in one era don’t often repeat it – usually because their particular ‘strategy’ goes out of favor.  

If we pick 2 managers that have a core ‘financials strategy’ --- and the financials sector goes up 45% --- do we really care that one manager did +48% and the other did +42%?   Your allocation made a lot of money as you captured that 45% gain, on average.   But we could also just do the same thing buying a low-cost financial index ETF and get the same +45% index result and have zero risk of underperformance.  

Core ‘beta strategies’ like this can all be replicated with ETFs at very low cost.  If a portfolio manager or investment advisor is good at picking market segments (good at picking ETFs), then they should be paid a fee for capturing the return of a good idea -- even if they don’t outperform that segment.  An investment advisor that picks good ETFs is adding significant value.      

For a private investor, doing it yourself makes a lot of sense in a world of low-cost ETFs.  You will of course need good, well-structured data in order to make good investment decisions.  Everyone does  -- from high-end hedge fund managers to individual investors to professional investment advisors.   We provide users with good ‘institutional-grade’ analysis apps that focus on precise methods and techniques for helping you with specific, backtested entries and exits on timing investment STRATEGIES (ETFs).

 

Snapshot of an ETF Relative Strength Strategy Using Bond ETFs (Starting Point End of 2007 -- near inception date of JNK -- and INCLUDES the credit crisis that hit Junk Bonds in 2008). 

 

 

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Try the Relative Strength App It's free.

 

 

The Investors Business Daily (IBD) 100 is at its core a Relative Strength backtest of the IBD stock universe. Note that IBD's method is to also use its larger market BUY and SELL signals and these do not show up in the chart below.

If you use fixed-income ETFs in your ETFreplay.com screening/backtesting lists -- you effectively automate the 'Buy-Sell' signals by naturally rotating into the higher relative strength bond ETFs as stocks breakdown and bonds rally. We suggest using intermediate or short-term bond ETFs in your lists. Long-dated treasury ETFs can suffer from very high volatility -- and a key concept is to avoid highly volatile securities UNLESS they offer high expected returns. This is unlikely the case for most bond ETFs -- bond ETFs best attribute is their stability in times of turbulence.

Note also that while the IBD 100 is compared to the S&P 500 below, it does often own foreign stocks that trade on U.S. exchanges. While we cannot calculate the volatility of the IBD-100 since we don't have the data series, we suspect its volatility is far greater than the S&P 500. Remember that when volatility is HIGHER, you would EXPECT larger relative drawdowns.

 

This image captures the cumulative return of the IBD 100 index up through June 18, 2010 (note the massive drawdown in 2008).