Notice anything unusual?

Posted by – October 6, 2014

We have. First, the program is primarily for developing ETF strategies via back-testing the selected parameters against up to 10 years of historical data. The “Forecast” button, which may be present, is not fully functioning. We will correct this by adding/correcting a forecast capability and issue an update soon. Check back for an announcement of a new version. Second, under some circumstances, CAGR results can either appear to be, or actually be incorrect.

We attempt to “annualize” CAGR results by pro-rating partial year results to an interpreted CAGR.

As an example of an incorrect interpretation, when using “Days” mode, if the resulting output list contains two entries in December – e.g. Dec 01 and Dec 31, the CAGR is being incorrectly calculated. There are other situations where our attempt to “Annualize” partial year outputs results in either “unusual” appearing or even incorrect results. We may have to resort to only reporting CAGR for full year periods rather than attempting to calculate annualized results from periodic data.

In all cases we’ve observed, the underlying data reported, has been correct. It’s merely the CAGR calculation which is suspect.

Sorry for any confusion this may have caused. We’re sorry to inform you that 2003 and 2004 Global results for a 21 day investment cycle were not 120+% :(


Posted by – October 3, 2014

As you develop your ETF momentum strategies, keep in mind.

The objective is not to develop a single non reproducible high performing set of selection criteria, but rather develop criteria that can be fairly closely replicated by yourself and others utilizing both your selection criteria and selection criteria which employ very close equivalents.

For example, if you determine a result set for a portfolio utilizing SPY which can not be approximated utilizing MDY in place of SPY, your criteria may contain selection bias. You should be able to approximate (not necessarily replicate) a valid strategy utilizing equivalent fund selections.

Momentum? How’s that working out for you? Introducing dmdemo v2

Posted by – October 2, 2014

As we’ve stated on numerous occasions (see below) – “Momentum strategies rely on momentum, which is great as long as the market is exhibiting positive (or negative) momentum. However, during turbulent periods or times of very low or shifting momentum, the strategies need to be monitored more closely.

Under certain market conditions, i.e. a momentum-exhibiting market, a properly constructed and implemented momentum-based strategy may add-value over a more general buy and hold strategy. However, since no one can accurately predict which set of conditions will prevail for a given future period, momentum should not be relied upon for consistent, superior results.”

Now might be a good time to evaluate your current strategy and develop your future strategy. I’ve personally been out of the market since May, but I’ve used the time to continue to evaluate “momentum” as a strategy and prepare myself for the time I once again feel comfortable employing a momentum-based strategy.

To help you evaluate/prepare, we’re introducing our DualMomentum analysis tool, dmdemo v2. It is a self-contained toolset which should permit you to analyze various momentum strategy combinations via both ETF portfolio selection and performance/volatility ranking.

This is very much a beta release, but it can not harm your system in any way. It makes no system changes outside of installing it’s own self-contained database. It does not access anything outside of the database. The worse thing that could happen would be it hanging and needing to be killed via task-manager. In that event, please report the circumstances.

Please report your experiences, positive or negative, so we can learn and improve the toolset.

Current version suspended. A new utility will be coming soon.

Back-test utility available

Posted by – September 19, 2014

This is a free, Windows™, utility which allows you test ETF momentum using various combinations of performance and 20 day volatility.

This utility uses a methodology similar to that described in Frank Grossman’s Seeking Alpha GMR, GMRE and Bond ETF strategies.

This utility is not equivalent to Mr. Grossman’s published newsletters, nor does it provide the same detailed analysis provided in his newsletters. It provides an analysis of past performance similar to the methodology as interpreted from his “SeekingAlpha” articles, however it does not duplicate his methodology. Please note that the included Bond portfolio is back tested utilizing a single ETF selection rather than possibly two selections as in Mr Grossman’s described method.

The primary purpose of the utility is to demonstrate the somewhat dramatic impact on results possible with relatively small changes in selection parameters. These changes may cause you to question the veracity of some of the current claims of 40% or greater annual returns. While we agree 40% and greater returns are certainly occasionally possible, we would also like you to see that much lower returns are also possible and much more probable than the returns being predicted. We contend that these excessive returns of 40% and greater are likely the result of selection bias rather than a predictable phenomenon.

unavailable – see dmdemo above

Monthly rankings

Posted by – August 18, 2014

The objective of the published portfolio forecasts is to provide an ETF prediction similar to that described in a series of SeekingAlpha articles. As we’ve previously stated, we’ve been unable to duplicate the published results but are publishing a forecast based on our interpretation from the published article(s).

We are somewhat skeptical that this (or any) system will achieve long-term results consistent with those published (40+%). However our initial back-tests did indicate a value-adding component of a momentum-based strategy (versus pure buy-and-hold of a single ETF), albeit much less than that reported in the published article.

While we still believe momentum very likely adds value, we’ve since come to believe that under market conditions outside those tested, it likely adds less than most would like to believe, or have us believe. We are investigating various momentum strategies, attempting to minimize or eliminate selection-bias, or at least quantify it’s likely magnitude, while also seeking to quantify what we feel to be a more realistic expected return. We will publish our findings as we complete them. We sincerely hope our skepticism proves unwarranted.

During this interim period, per user-request, we will continue to publish a monthly dual-momentum forecast for the portfolios based on our interpretation of the original publication(s) along with a performance based forecast of another popular portfolio.

Bond normalized momentum

Posted by – May 14, 2014

Edited Aug 05, 2014:
While our back-tests did achieve the results listed below, we’ve since come to believe the results may have been augmented by over-fitting or selection-bias.

Simply stated, the results, as with most momentum-based systems we’ve seen, were achieved based on selection criteria that may not have been representative of the data population in general. While it’s debatable that the selection criteria themselves added the value, it’s at least equally, if not more likely that the results contain selection-bias.

Original post follows:

We’ve back-tested a Bond strategy for the 2008-2013 period. Results. CAGR=18% with a 6.85% Max draw-down. Note: Unlike some other bond strategies, this strategy was back-tested utilizing the single top ranked fund each month.

The rankings are based on 67% momentum 33% volatility, both are normalized for the final ranking calculation and are based on the results of a “calendar month” strategy. Bond 2008-2013 results.

Momentum ranking updated

Posted by – May 13, 2014

Aug 05, 2014 see edit to “Bond normalized momentum” post above.

Original post below.

We’ve incorporated a performance and volatility based momentum ranking into the ranking report which back-tests well (CAGR of 35%, Max draw-down 23%) across the entire 2003-2013 period and also tests well across most sub-periods – Note: 2013 result=less than 10% CAGR versus 30+% in other systems. A large portion of the discrepancy is the inclusion/avoidance of EDV for May, 2013. We’ve looked into the details of the calculations involved and so far, are unable to arrive at any reasonable calculation which avoids EDV being the selection. This also helps confirm our findings concerning the susceptibility of back-testing to very small changes in timing. For example if an event occurred yesterday, instead of today, you obtain a (sometimes dramatically) different result. This tends to make some results seem more like synchronicity than science. If you have suggestions, please let us know. If we can develop a reasonable solution, which holds across other periods, we may have our “final” solution.

The rankings are based on 67% momentum 33% volatility, both are normalized for the final ranking calculation. Global 2003-2013 results.

Momentum back-testing – Update and conclusion.

Posted by – May 6, 2014

May-01-2014. Extensive back testing has confirmed what we had come to suspect. While momentum may be a useful ETF strategy under some market conditions, any specific strategy may also under-perform others (or even a performance-only strategy) under other conditions. Since no one can predict which set of conditions will prevail for a given future period, a specific momentum strategy should not be relied upon for consistent, superior results under all market conditions. However, properly constructed momentum strategies may offer superior results to simple “buy and hold” strategies – e.g. SPY, DIA, etc. – when utilized over market horizons which span Bear and Bull periods. However, the performance advantage is likely much less than some people would have you believe.

We will continue to post the results of our performance and volatility based ranking on the site. Please understand these ranking are based on our own performance and volatility parameters, currently 67% performance, 33% 20 day volatility, and may not correspond to other momentum strategy rankings. We caution you to use due diligence before making any investment decision.

Momentum back-testing – Macro results

Posted by – May 6, 2014

Like many who visit us here, we became intrigued by momentum strategies as a result of a series of articles in Seeking Alphaby Frank Grossman. Specifically his articles on ETF market rotation strategies – GMR, GMRE and Bond. We were early subscribers to his newsletter(s) and experienced favorable returns using the strategy in the latter part of 2013. Like some others, the newsletter wasn’t enough, we wanted more. So we began to run our own back tests of various performance/volatility combinations, at first merely to see if we could accurately duplicate his predictions.

If you’ve read the comments associated with the original articles, you’ll know that “duplicating” Mr Grossman’s methods proved difficult, if not, impossible, for most who tried. He provided an outline of the methodology, but as they say, the devil is in the details, and there are just too many details we do not have. While many have tried, we’ve not seen anyone who has successfully duplicated Mr Grossman’s published back test results. Some may have come close, but we’ve not seen them duplicated. We certainly have not been able to.

While we’ve been unable to duplicate Mr Grossman’s results, our test results have indicated that his strategies are certainly among the most promising of those we’ve tested. His selection of funds provides an excellent foundation on which to construct a strategy. And his strategy is augmented by timely market analysis provided to his newsletter subscribers.

Another reason we began to do our own back testing was that there were elements of several momentum-based strategies about which we had reservations. For example, to be effective, you should only trade on the first trading day of the month, based on the ranking determined using closing data from the last. While that may be the case for the back-tests used to develop the strategies, is it some particular proven market characteristic? (We were actually able to determine other selection criteria which out-performed “first day/last day” in back-tests, but similar to “first day/last day”, they did not hold up across our stress tests for all periods tested.)

Why trust our findings/results? Simple answer – DON’T. We don’t. We keep testing and refining. We’ve reached a point where we’re comfortable that our observations/results will not be totally refuted, while at the same time, hoping they will be and a consistently high performing momentum strategy will be found. Please, do your own due diligence. Our general findings relate to thousands of back tests run across dozens of time periods and make NO specific recommendation regarding a specific strategy recommendation. We believe we’ve seen enough results and systems posted since Mr Grossman’s articles to realize, beating the market can’t really be that easy. As someone asked in response to one of the published momentum-based strategies, “If it’s that easy, why isn’t it more mainstream”. In other words, why isn’t everyone doing it? Why aren’t fund managers trouncing the market by employing relatively simple, momentum-based strategies? Our response to that question is “It’s not mainstream because in the real world, you can’t trade in the past, which is where the systems have been proven to be effective.” However, since it’s so appealing, we continue trying to develop a market-beating system based on momentum. We want people to understand up-front – there is NO single best strategy, nor is there any guaranteed winning momentum strategy that we’ve been able to develop or validate.

Efficient market theory contends “future prices cannot be predicted by analyzing prices from the past. Excess returns cannot be earned in the long run by using investment strategies based on historical share prices or other historical data. Technical analysis techniques will not be able to consistently produce excess returns, though some forms of fundamental analysis may still provide excess returns.”

And while we’re talking disclaimers, we must add regarding our own back tests: our testing relied on numerous external (downloaded) and computed values based on historical ETF performance, mainly obtained from yahoo. The values used as input and/or computed, may not be statistically significant, and may be subject to extrapolation or programming error. Of course, everyone published their results as if everything is “fact”, when we’ve seen too many contradictory and questionable results to blindly accept any as “fact”.

Our first attempts were to validate Mr Grossman’s findings, as closely as we could. Using yahoo quotes and software which we developed specifically for the back tests, we ran hundreds (eventually thousands) of permutations across a 2002 – 2013 period. We later split the time periods into several periods which we found to exhibit differing characteristics to see if results/predictions from one period would hold into another with differing characteristics.

We ran “sub tests” on every candidate strategy to determine if it was good across all time periods or only some. While we will publish additional detail regarding the specific tests and results, we characterize the results as follows:

Momentum strategies rely on momentum, which is great as long as the market is exhibiting positive (or negative) momentum. However, during turbulent periods or times of very low or shifting momentum, the strategies need to be monitored more closely.

Under certain market conditions, i.e. a momentum-exhibiting market, a properly constructed and implemented momentum-based strategy may add-value over a more general buy and hold strategy. However, since no one can accurately predict which set of conditions will prevail for a given future period, momentum should not be relied upon for consistent, superior results.

There is NO best strategy. A strategy which out-performs for any specific period will under perform in other periods.

Nor have we been able to determine a performance advantage obtained by developing a strategy backtested over several decades versus tested from 2002 forward. Back-testing using “fund equivalents” across very long time frames, does not appear to offer meaningful equivalency to stress testing utilizing multiple, shorter testing periods using the actual funds employed in the strategy.