Manage Your Luck

June 21, 2016

“Luck is the residue of design”

                      –Branch Rickey

There is a lot more luck involved in investing than people think.  I’m not saying there isn’t skill involved in investing or that there aren’t ways to outperform the market over time.  Even if you have a process that can be shown to outperform the market over long time periods, there can be a great deal of variation in returns from year to year.  A well designed investment model can certainly help manage some of that luck, but it is difficult to eliminate it entirely.

Several years ago I looked at some momentum models in a unique way.  Most research on equity momentum involves buying a large basket of stocks (the top decile or top quintile, for example) and rebalancing on some preset schedule (like monthly).  There are a lot of active strategies that aren’t run that way so we wanted to find out how much variation there could be in owning a sub set of the high momentum stocks and rebalancing more frequently.  Do you need to own all of the big winners in order for a momentum strategy to work?

In order to attempt to answer that question I created a process that picked stocks at random out of a high momentum basket and held them until they were weak.  (You can read about it in more detail in the original whitepaper I published by clicking here.)  In the test shown in this post, I am using a universe of the top 1000 market capitalization stocks traded on US exchanges.  That eliminates the problem of holding very illiquid stocks; every stock in that universe should have sufficient liquidity to trade without major slippage costs.  Each week the stocks were ranked by their trailing 12 month performance, which is a very standard way to measure momentum.  Anything that ranked in the top decile based on the trailing 12 month performance rank was considered to be “eligible” for the portfolio.  Anything that ranked below the top quartile of the ranks was eliminated immediately from the portfolio.  The portfolio was set up to hold 50 stocks.

Most tests would just pick the top ranked stock when something needed to be bought.  The difference in my test was we picked something at random from the “eligible” list.  There were about 100 eligible stocks each week – the top 10% of the 1000 stock universe (excluding buyouts, etc…).  Then I ran the process 100 times to create 100 different equity curves.  It would be the same thing as giving the eligible list to 100 different people each week and telling them they can pick anything they want off the list as long as they don’t already own it.  You are going to wind up with 100 totally different portfolios over time with the only thing in common being the process of buying high momentum stocks and selling them when they get weak.

The results of the 100 trials are summarized in the table and graph below (click the image to enlarge).  The table shows the return of the S&P 500 as well as the average return of the 100 trials each year.  There is also a section that shows where the quartile breaks occur each year.  The graph shows the returns year by year with the red bar being the average return, the box showing where the mid quartiles are, and the whiskers extending to the min and max returns.  The green dot is the S&P 500.

Random Numbers

Random Graphs

The biggest thing that should jump out at you is that even by picking stocks at random, all 100 trials outperform the S&P 500 Total Return Index.  That is pretty amazing.  The actual stocks you put into the portfolio don’t matter as much as you would think.  The process is what is important.  Constantly cutting the losers and buying winners is what drives the performance.  The process helps to manage the luck of stock picking over time!

You can also see that from year to year the returns can vary quite a bit.  So what is the difference?  Literally, luck.  Some years the process is lucky, some years it isn’t, but when the process is solid it works out over time.  It is also a good reminder of why it is so important to focus on the process rather than the results over a short time period.  Just because a process underperforms for a year it doesn’t mean it is “broken.”  This, unfortunately, is how most investors think.  There is so much research on poor investor behavior I’m not even going to attempt to address it here!

A solid investment process winds up managing the luck that exists in implementing the system over short time periods.  Momentum is a robust enough factor to handle picking stocks and random from a highly ranked sub set of securities and then selling them when they are weak.  What happens from year to year is a lot about luck, but over time the design of the process overcomes the luck.

The returns used within this article are the result of a back-test using indexes that are not available for direct investment.  Returns do include dividends, but do not include transaction costs.  Back-tested performance is hypothetical (it does not reflect trading in actual accounts) and is provided for informational purposes to illustrate the effects of the discussed strategy during a specific period.  Back-tested performance results have certain limitations.  Such results do not represent the impact of material economic and market factors might have on an investment advisor’s decision making process if the advisor were actually managing client money.  Back-testing performance also differs from actual performance because it is achieved through retroactive application of a model investment methodology designed with the benefit of hindsight.  Dorsey, Wright & Associates believes the data used in the testing to be from credible, reliable sources, however; Dorsey, Wright & Associates, LLC (collectively with its affiliates and parent company, “DWA”) makes no representation or warranties of any kind as to the accuracy of such data. Past performance is not indicative of future results.  Potential for profits is accompanied by possibility of loss.  

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Ode to Smart Beta

June 7, 2016

Nir Kaissar at Bloomberg recently published a nice summary of the historical returns of a number of Smart Beta factors, including Value, Size, Quality, Momentum, and Low Volatility:

In the brave new world of next generation, active investment management, smart beta is all the rage. Smart beta relies on five major strategies — or “factors” — to fine tune market returns:

Value – buying cheap companies

Size – buying small companies

Quality – buying highly profitable and stable companies

Momentum – buying the trend

Low Volatility – buying defensive companies

Investments built around each of those factors have historically beaten the market, as the following chart shows:

smart beta returns


A couple of observations:

  • The market is efficient?  Really?  I don’t think so.  Excess returns are available if investors focus on the right factors.
  • These factors move in and out of favor so nobody should expect each factor to outperform all the time.  In fact, Kaissar highlighted the benefits of mixing these factors in order to smooth out the ride.
  • It’s hard to miss the fact that Momentum had the highest returns of any of the factors listed in this study.  Yes, Momentum had the highest volatility, but it also had the highest Sharpe ratio.

Smart Beta is far more than just a catchy marketing phrase.  It is a much more effective way to seek excess returns than traditional active management.

The relative strength strategy is NOT a guarantee.  There may be times where all investments and strategies are unfavorable and depreciate in value.

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The Minority That Matter

May 23, 2016

Eric Crittenden has recently updated his research on the distribution of stock returns over time—research that underscores the rationale for trend following strategies.  His study now covers the period 1989-2015.  See below for further explanation:

Longboard’s original research proves that over the long term, a small minority of stocks drive returns for the overall market.

If you’ve heard of the Pareto Principle before, this might not surprise you.

What does this mean for investors? It may be more efficient to navigate this reality by getting defensive, and strategically avoiding the majority in this equation: the underperforming investments.

Be aware of disproportionate rewards

Here’s a closer look at our research on this competition gap in action in the U.S. stock market.


We analyzed 14,455 active stocks between 1989 and 2015, identifying the best performing stocks on both an annualized return and total return basis.

Looking at total returns of individual stocks, 1,120 stocks (7.7% of all active stocks) outperformed the S&P 500 Index by at least 500% during their lifetimes. Likewise, 976 stocks (6.8% of all active stocks) lagged the S&P 500 by at least 500%. The remaining 12,404 stocks performed above, at or below the same level as the S&P 500.

The principle of the competition gap remains true in practice: The minority accumulates a disproportionate amount of the total rewards, creating a “fat tail” distribution of extreme outperformers and underperformers with a large gap between the two.

Focus on the minority

What’s more, the left tail in the stock market’s competition gap (or distribution) is significant. 3,431 stocks (23.7% of all) dramatically underperformed the S&P 500 by 200% or more during their lifetimes.


So, let’s say an investor’s portfolio missed the 20% most profitable stocks between 1989 and 2015. Instead, he invested in only the other 80%. His total gain would have been 0%.

Once again, the principle holds true: Over the long term, the more efficient approach is to strategically avoid the many underperformers.

Seek alternative long-term returns
To get more benefits from alternative allocations, investors can seek long-term trend following strategies that proactively trim investments that don’t perform over time. These more defensive strategies are better positioned to avoid sustained downtrends — and a diversified portfolio with fewer strategies trapped in sustained downtrends can recover more quickly.

What’s more, some of the same strategies that can deliver this downside protection can add further diversification, potentially delivering results that are uncorrelated to the market and to other alternatives.

If ever there was a need to highlight the need for relative strength analysis, this is it!  It is no small thing to have a discipline for weeding out underperforming stocks from the portfolio and Dorsey Wright is uniquely positioned to help you with this task.  Subscribers of our research can use our technical attribute ratings and our matrix tools to weed out weak stocks.  Users of our investment products can access strategies that have defined sell disciplines in place.  The sell discipline will differ by strategy, but it is there for each of them.

Sitting on losing positions with the belief that they will eventually turn around is a fool’s errand.  But isn’t patience the key to long-term investment success?  Yes and no.  Patience in a well-designed investment strategy is one thing.  Patience in losing positions is another thing entirely.  Individual stocks are under no obligation to provide a profitable experience for their investors.  Stocks don’t know when or at what price you bought them.  As the research above demonstrates, many–in fact most–stocks are losers relative to a broad market benchmark.  It is up to you to successfully navigate the very fat-tailed distribution of stock returns.  Relative strength can help.

Click here for disclosures.  The relative strength strategy is NOT a guarantee.  There may be times where all investments and strategies are unfavorable and depreciate in value.

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Combining Momentum & Low Volatility for Enhanced Alpha

May 2, 2016

Most market participants would agree that four of the most popular factor based investment methods used today are often considered to be momentum, value, growth, and low volatility.   At Dorsey Wright, we are often asked the best way to combine Dorsey Wright strategies (momentum/relative strength) with these other commonly used factor strategies.      Proponents of any smart beta strategy will often support all of these strategies, even admitting that there are pros and cons to each factor.  Momentum, for example, is the idea of investing in securities or asset classes using a previous time period that has performed well, most commonly a 12 month trailing return.  This type of strategy tends to do well during periods of sustained trends, but lags others such as value and low volatility during choppy markets.

During the 1st quarter of 2015, the majority of momentum/relative strength based strategies tended to fare better than the other factor based strategies mentioned above.  One of the largest contributing factors to this alpha generation during Q1 of 2015 was the dispersion which existed amongst US equities.   For example, the energy sector saw a sharp decline while sectors such as healthcare, biotech, and consumer discretionary fared much better.    Fast forward to the Q1 of 2016 and we have a different story on our hands.   Momentum strategies have struggled due to a lack of sustained sector leadership, while investment themes such as low volatility and value have performed much better.

Given the recent changes in sector leadership, we thought it would be interesting to go back and take a look how a few of these factor methods have compared to each other in terms of performance, volatility, etc.  The table below is a simple study complied over the last 18 years comparing PDP (Momentum), SPLV (low volatility), and SPY (benchmark).   Although momentum (PDP) outperforms both SPLV (low volatility) and SPY (benchmark), the added alpha generation came with a few drawbacks (mainly the potential for elevated volatility).   While periods such as these can be difficult, there are certainly ways to minimize the downside when they do come about in the market.   For example, using a systematic process can be a huge advantage, as it helps remove the human emotion which often times is magnified during periods of heightened market volatility.   Let’s take a look at the table below and see what type of results each of these portfolios (momentum, low volatility, and the equity index) generated over the allotted time period.


PDP inception date: March 1, 2007 – data prior to inception is based on a back-test of the underlying index.

SPLV inception date: May 5, 2011 – data prior to inception is based on a back-test of the underlying index.

When we take a closer look at the returns, we can see just how much the momentum and low volatility factors  differ in terms of performance at various cycles in the market (note 1999, 2003, and 2008 just to name a few).   The idea of implementing both momentum and low volatility into a portfolio would then sure seem logical to most money managers.   After all, any type of low volatility factor investing can typically be thought of as a reversion to the mean type of trade, which most would agree is the exact opposite goal of momentum investing (i.e.  looking for “fat tail” trades that deviate from the mean).    More simply stated, combining two different factor allocations in a portfolio which tend to do well during different market cycles would certainly seem to be an added benefit for any portfolio manager looking to reduce volatility and continue to generate alpha.


The graphic above does a good job of displaying the differences returns year in and year out.  In fact, the correlation of excess returns between momentum and low volatility ends up at roughly-.70.    We plan to further visit this topic in our next blog post in order to give readers an a better idea on the type of results seen when combining these two factors in both a static and flexible allocations.   For now, the important thing to keep in mind is that in today’s investment world market participants should take full advantage of the full suite of products out there in order to help achieve alpha for their clients.    Using momentum and low volatility is just one way this can be done.   More detailed performance and risk analysis to follow in our next post on this topic.

Performance data for SPLV prior to 05/05/2011 and PDP prior to 3/01/2007 is the result of backtested underlying index data.  Investors cannot invest directly in an index.  Indexes have no fees.  The returns of the ETFs above do not include dividends, or all transaction costs.  Back-tested performance is hypothetical (it does not reflect trading in actual accounts) and is provided for informational purposes to illustrate the effects of the strategy during a specific period.  Back-tested performance results have certain limitations. Back-testing performance differs from actual performance because it is achieved through retroactive application of an investment methodology designed with the benefit of hindsight. Back-tested performance does not represent the impact of material economic and market factors might have on an investment advisor’s decision making process if the advisor were actually managing client money. Past performance is not indicative of future results. Potential for profits is accompanied by possibility of loss.

Neither the information within this post, nor any opinion expressed shall constitute an offer to sell or a solicitation or an offer to buy any securities  This article does not purport to be complete description of the securities to which reference is made.

DWA provides the underlying index for the PDP, discussed above, and receives licensing fees from PowerShares.


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RS in Rising Rate Environments

December 4, 2015

With wide expectation that the Fed will raise interest rates this month, it is worth considering how a momentum strategy tends to perform in a rising interest rate environment.  Invesco PowerShares addressed this topic in their September 2015 paper Harnessing the Power of Factor Investing. According to their findings, momentum was able generate excess returns in both rising rate and declining rate environments.  However, the excess returns were higher in rising rate environments.

rising rate_12.04.15

falling rate_12.04.15 

Some thoughts on why this pattern may occur:

  • By the time rates rise you are typically well off the market bottom and well out of a recession.  On average, stocks are at least fairly valued at that point and there aren’t a ton of bargains to be had that are really cheap for obvious reasons.  At that point investors look for growth and that is what momentum is good at picking up.
  • Late cycle also means fewer stocks participating in the rally, which is also good from a momentum perspective.
  • Good momentum stocks usually don’t have to rely on cheap financing (they can generate cash flow organically) so they don’t get crimped like value stocks do when rates rise.

While many seem to fear what affect a rising interest rate environment will have on stocks, it is worth remembering that rising rates have tended to be good for a momentum strategy.

The relative strength strategy is NOT a guarantee.  There may be times where all investments and strategies are unfavorable and depreciate in value.

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Buy the Winners

October 26, 2015

People come up with all kinds of reasons not to buy stocks with strong momentum.  Some of the most common reasons that I hear:

  • Stocks with high momentum are risky
  • Stocks with high momentum are overvalued
  • Stocks with high momentum are susceptible to reversals

As for the first point, yes, buying stocks with high momentum is risky.  So is buying stocks with weak momentum.  As far as that goes, buying any stock is risky (stocks with good valuations, bad valuations, small cap, mid cap, large caps….)  The stock market is a risky place.  It can also be a very rewarding place.

As for the second point, yes, sometimes high momentum stocks have higher valuations than low momentum stocks.  But not always.  Also, it is not uncommon for stocks with high price momentum to far exceed earnings expectations and lo and behold it often turns out that maybe they really weren’t overvalued after all.

Finally, the concern about high momentum names being susceptible to reversals.  There is truth to this.  Momentum is a trend following strategy and all trends work great until they end.  However, the key is whether or not enough money can be made while the trends are in place to make up for the amount of money that will be lost during changes in leadership.

To the data.  The Ken French Data Library is a fantastic resource for testing the merits of different strategies as it includes performance for a variety of investment approaches (momentum, value, size, dividend yield…).

One of the ways that the Ken French Data Library segments their universe of U.S. mid and large cap stocks is by size and momentum.  The results below show performance of three different portfolios.  All three are from a strategy that invests in stocks in the top half of market capitalization from their investment universe.  The “High” momentum portfolio is an equally-weighted portfolio of the stocks from the universe with the best momentum over the previous 12 months, the “Middle” momentum portfolio is an equally-weighted portfolio of stocks from the universe with moderate momentum (30-70th percentile) over the previous 12 months, and the “Bottom” momentum is an equally-weighted portfolio of stocks from the universe with the weakest momentum over the previous 12 months.  All three portfolios were rebalanced monthly.


Returns are inclusive of dividends, but do not include any fees or transaction costs. *12/31/1926 – 9/30/2015

Over this nearly 89-year period of time, the High momentum portfolio had an annualized return of 15.08%, the Middle momentum portfolio had an annualized return of 10.46%, and the Bottom momentum portfolio had an annualized return of 4.56%.

Furthermore, the High momentum portfolio outperformed the Middle momentum portfolio in 86% of rolling 5-periods and outperformed the Bottom momentum portfolio in 94% of rolling 5-year periods over this period of time.

Bottom line: Buy the winners (and continue to hold them as long as they remain strong).  It doesn’t work all the time, but it works a high percentage of the time.  When it comes to choosing long-term investment strategies that can be the cornerstone for an asset allocation, momentum makes a compelling argument to be in the mix.

One final thought, I can’t tell you how often I see people make reference to the compelling returns of momentum over time and then say something like, “but whatever you do, never use it as a stand alone factor!”  Still baffled by that one.  Seems that it works just fine as a single factor.  To be clear, I am not arguing that momentum should be the only factor in an entire allocation.  We have frequently made the argument, along with others, that momentum and value strategies tend to be good complements.  However, I see no reason why a single-factor momentum strategy can’t make up a meaningful portion of a client’s overall asset allocation.

The relative strength strategy is NOT a guarantee.  There may be times where all investments and strategies are unfavorable and depreciate in value.

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Relative Strength and Dividend Investing

October 1, 2015

The portfolio manager of a large, active dividend fund was recently interviewed by Morningstar.  (“What Active Management Can Bring To Dividend Investing”  The portfolio manager argues that simply looking for stocks with high dividend yields is insufficient because so many of those very high yielding stocks go through dividend cuts.  She says that using fundamental analysis and looking at things like cash flow generation can help a dividend investor avoid some (not all) of the stocks that eventually have dividend cuts.  That is certainly sound advice, and avoiding stocks that undergo dividend cuts is really the key to a successful dividend strategy.

Another way to help find stocks that might have a dividend cut is to use relative strength.  There is often a lot of selling pressure well before a dividend cut as more and more people figure out a company is not going to be able to maintain its current payout.  Like good fundamental analysis, using relative strength to screen out weak dividend stocks doesn’t mean you avoid every stock that has a dividend cut.  But it does help you avoid enough of them to add value over the long-term.

The following two models draw stocks from the same universe.  The universe is the top 1000 domestic stocks by market capitalization (a mid and large cap universe similar to the Russell 1000).  Each model is rebalanced at the end of each calendar quarter with 50 stocks.  The Dividend model simply selects the top 50 yielding stocks from the universe at the rebalance date and then weights those securities by their yield (i.e., higher yielding stocks get a larger percentage in the model).  The Dividend+Momentum model takes the top 50 yielding stocks from the universe that are also on a Buy Signal and in a Column of X’s on their Point and Figure RS chart.  The only difference between the two models is the Dividend+Momentum model adds the PnF RS filter to the yield screen.


The chart above shows how using an RS filter can enhance a dividend yield strategy.  Over the entire test period from 12/31/1989 through 9/30/2015 the added RS screen adds a tremendous amount of value.  Some of that outperformance comes from avoiding the stocks that have very high current yields that are unsustainable.  Also keep in mind that relative strength is not predictive so it isn’t necessary to try to “get out in front” of every dividend cut.  The market tends to recognize these situations well before the cut actually happens.

The article also addresses the financial crisis when a lot of dividend strategies were heavy in financials that eventually cut their payouts.  Just about every fully invested strategy had difficulty during that period, but the RS screen on the dividend portfolio did help to cut the drawdown versus a yield only portfolio:


The Dividend+Momentum definitely had a rough 2008, but was much better than not using the screen.  There are a number of periods during the test where the relative strength screen really improved performance.  This year is no exception.  We have seen this across the board with some other factors we track.  Things like Value+Momentum are doing much better so far this year than just Value alone.  The exception is Low Volatility where the relative strength screens aren’t adding as much value, but they are still outperforming their counterparts that don’t use the relative strength overlay.

The downside of using a relative strength screen with dividends comes in big mean reversion years.  This should be expected in any type of strategy that adds a momentum overlay.  Years like 2001 and 2009 are much better on a raw dividend yield basis.  However, if you are willing to deal with those periods of relative underperformance then adding a momentum screen to a yield strategy has the potential to add a tremendous amount of excess return over time.

The portfolio manager in the article is 100% correct about needing to find companies with high yields that are sustainable.  There are number of different ways you can accomplish this.  Most methods involve using some sort of fundamental data to ascertain if the company’s payout is sustainable or not. Another effective way to do this is to use a relative strength overlay to fund stocks that are outperforming the market with high yields. Using a RS overlay might cut down on the current yield of the portfolio, but testing shows that the gains from capital appreciation can make up for this. Making sure your portfolio of high yielding stocks remains on a buy signal and in a column of X’s versus the broad market is another way to filter out stocks that might have unsustainable yields.

The performance above is based on total returns, inclusive of dividends, but does not include all transaction costs.  Past performance is not indicative of future results.  Potential for profits is accompanied by possibility of loss.  The relative strength strategy is NOT a guarantee.  There may be times where all investments and strategies are unfavorable and depreciate in value.

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Most Stocks Do Poorly

September 10, 2015

Interesting stats from The Irrelevant Investor:

A study from J.P. Morgan examines all Russell 3000 stocks from 1980-2014. What they found was that 40% of all stocks suffered a catastrophic loss, which they define as “a decline of 70% or more in the price of a stock from its peak, after which there was little recovery such that the eventual loss from the peak is 60% or more.” J.P. Morgan also found that two-thirds of all stocks underperformed the Russell 3000, while 40% of stocks experienced negative absolute returns.

Active management, which seeks to add value over a cap-weighted index, has its work cut out for it.  Some investors will simply look at the 7.57% annualized return of the Russell 3000 index from 1990-2014 (I didn’t have access to Russell 3,000 data going back to 1980) and conclude that they are just fine investing in a fund that seeks to track the returns of this capitalization-weighted index.  I would argue that such an approach would be more prudent than simply buying 25 individual stocks in 1980 and holding them through 2014.  At least with a fund that seeks to track the Russell 3,000 Index you get a more diversified approach that will give the most weight to the largest cap stocks.  However, a cap-weighted index does suffer from the vulnerability of having significant exposure to large cap stocks that turn south and embark on extended periods of underperformance (think Japan in 1989, or tech-heavy Nasdaq in 2000).

Where a relative strength strategy excels is in focusing on those stocks within a broad investment universe that are demonstrating the best price momentum.  The best price performance may or may not be coming from the stocks with the largest capitalization in the Russell 3,000.  Furthermore, what you don’t own is every bit as important as what you do own.  Presumably, many of those stocks from the J.P. Morgan study that experienced negative absolute returns or trailed the Russell 3,000 Index had weak price momentum for long enough periods not to spend too much time in a Momentum-driven portfolio strategy.

For those investors looking to dig deeper into the merits of a momentum approach to investing, I would recommend reading Relative Strength and Portfolio Management by John Lewis or some of the other research we have posted on our site.

Source of returns for the Russell 3000: FactSet.  Total returns inclusive of dividends, but does not include transaction costs or management fees.  The relative strength strategy is NOT a guarantee. There may be times where all investments and strategies are unfavorable and depreciate in value.

HT: Abnormal Returns

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Momentum Environments

May 28, 2015

How consistent are Momentum returns?  This is among the most frequently asked questions about Momentum (and about any investment strategy for that matter).  One way to answer this question is to look at the following table from a white paper published by RBC Capital Markets.  According to their research, Momentum outperformed the S&P 500 in every decade since the 1930’s.  Yes, the margin of outperformance was larger in some decades than others, but Momentum outperformed in each.  You will notice from the table that this was not true of Value or Growth, which both experienced decades of underperformance.

Ken French

However, that is certainly not to say that Momentum outperforms every year.  I do find it entertaining that many critics of active management use the failure of active strategies to outperform every year as an argument against active management.  It is reasonable to expect the sun to rise each morning.  It is reasonable to expect your mother to love you.  But, it is incredibly irrational to believe that an active strategy should outperform every year!

The environments when Momentum tends to perform best is when trends are most stable.  In environments with major changes in leadership, Momentum tends to underperform.  One way to visualize these environments is by looking at our Relative Strength Spread.  This RS Spread takes a universe of U.S. mid and large cap stocks (S&P 900) and ranks them by their relative strength.  We divide the top quartile of the ranks by the bottom quartile to see when the leaders are outperforming the laggards.  A chart of the RS Spread over the last 3 years is shown below:


A couple observations:

  • The RS leaders underperformed the latter part of 2012 and some of the first part of 2013, but the majority of 2013 was spent with a steady rising RS Spread
  • The RS leaders had a pullback in the spring of 2014, but then stabilized and saw sharp improvement towards the end of 2014
  • The last couple months have been fairly choppy for the RS Spread, but in recent weeks the RS Spread has moved higher and is on the cusp of moving back above its 50 day moving average

Here is that same chart over the past 25 years:

RS Spread_lt

Some observations:

  • RS leaders have outperformed the RS laggards over time, but the RS Spread has certainly not always been smooth
  • The 1990’s had a relatively stable rising RS Spread with a huge move higher in the late 1990’s during the Tech boom
  • The two biggest laggard rallies occurred toward the end of major bear markets and in the first leg of new major bull markets (late 2002-early 2004 and in much of 2009)
  • The RS Spread has been relatively flat from late 2009 through late 2014

It is important to note that the RS Spread tells you nothing about absolute returns.  The absolute returns of a high relative strength strategy could be positive, flat, or negative in a rising, flat, or falling spread.  All it is telling you is how the RS leaders are performing compared to the RS laggards.  Given the relatively flat RS Spread for much of the past 5 years, I would not be surprised at all to see a strong rising RS Spread over the next 5 years, which could bode very well for RS strategies.

We happen to believe that Momentum is the most robust factor available and that it deserves a meaningful place in investor’s portfolios.  I believe that investors would be well served to have reasonable expectations about the type of returns and frequency of outperformance likely to be achieved by Momentum strategies over time.  If they do, they are more likely to correctly position it in a portfolio and to reap the rewards that we believe will accrue to long-term investors in Momentum strategies.

A relative strength or Momentum strategy is NOT a guarantee.  There may be times where all investments and strategies are unfavorable and depreciate in value.

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Sector Rotation with PnF Matricies

May 7, 2015

Watering the flowers and pulling the weeds.  Letting your winners run and cutting your losers short.  There are different expressions for sector rotation, but it is among the most profitable investment strategies we have found, when done in a disciplined way.  Sector rotation is based on the idea that there tends to be differences—sometimes large differences—in performance between various sectors of the market.  For example, so far this year Healthcare has been among the best performing sectors and Utilities has been among the worst.  One application of relative strength is to rank the broad macro sectors (Healthcare, Utilities, Energy, Consumer Cylclicals…), buy the top ranked sectors and avoid the weakest.  However, there are different ways to classify stocks in a sector.  Sub-industry groups (Distiller & Vintners, Technology Hardware, Airlines, Household Appliances…) can be evaluated to get a more granular look at strength and weakness in the market.  The next logical question is which sector classification should be used.  The answer will depend upon your objectives, because there are trade offs.  Consider the following excerpt from John Lewis’ 2014 white paper Sector Rotation with Point and Figure Matrices.

Macro sectors are blunt tools for sector rotation. Investors can purchase targeted funds providing more granular exposure within each macro sector.  We ran similar tests on the GICS Sub Industry indexes to illustrate the power of getting more granular with a sector rotation strategy. Figure 4 shows the summary results of testing rotation strategies using the 130 sub industry groups. (Please see Tables 6-10 for more detailed performance.) As with the macro sectors, we ran point and figure matrices at multiple box sizes. Each month the universe of sub industry groups is broken into quintiles based upon their scores in the point and figure matrix. Groups in the 80-100 quintile have the highest scores, and are the groups with the best momentum characteristics.

John’s white paper included a test of two approaches to sector rotation.  The first strategy involved trading the 10 broad macro sectors and the second strategy involved trading the 130 sub-industry groups.  Both tests were done over the period Dec 1995 – Jun 2014.  In the table below, we see the cumulative return for a sector rotation strategy that involved buying 1-5 of the top ranked sectors, using various box sizes in a PnF relative strength matrix.  The red-shaded boxes signify the worst performing model and the green-shaded boxes signify the best performing model.


Key takeaways from macro sector model:

  • Using a very sensitive relative strength box size (1%) typically led to the worst returns, while using a box size of 3-4% tended to work much better.
  • The fewer sectors held the better the performance was over time.  However, this will also likely be accompanied with greater volatility
  • A sector rotation strategy that trades the broad macro sectors has shown the ability to outperform the S&P 500 over time

In the table below, we see the cumulative return for a sector rotation strategy that involved buying quintiles of the sub-industry groups, using various box sizes in a PnF relative strength matrix.  For example, a sector rotation strategy that buys the top quintile (80-100 rank) of the sub-industry groups will hold approximately 26 (130 divided by 5) sub-industry groups.  As with the previous model, evaluations and necessary rebalances were done on a monthly basis.


Key takeaways from sub-industry group model:

  • Again, using a very sensitive relative strength box size (1%) typically led to the worst returns, while using a box size of about 6% tended to work much better.
  • The model focused on buying the top quintile of the sub-industry groups performed significantly better than the other groups.
  • A sector rotation strategy that trades the sub-industry groups has shown the ability to outperform the S&P 500 over time
  • A comparison of the cumulative returns generated from the macro sector model vs. the sub-industry group model makes it clear that there is significantly more return potential from the sub-industry group model

For a more complete description of the testing completed for this study, please read Sector Rotation with Point and Figure Matrices.  We believe that a careful study of this white paper will lead an investor to understand best practices as it relates to implementing sector rotation strategies using a Point and Figure relative strength matrix.

The relative strength strategy is NOT a guarantee.  There may be times where all investments and strategies are unfavorable and depreciate in value.

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The Momentum Effect

February 17, 2015

From Credit Suisse:

The two sets of bars on the left of Figure 8 relate to rotation based on prior-year returns for the USA (dark blue) and the UK (gray).  Each set of bars shows the annualized returns from investing in the previous year’s worst performers (losers), through to investing in the best quintile (winners).  If industries periodically become over- or undervalued, and then revert to fair value, we might expect reversals, with past losers beating past winners.  Figure 8 shows the reverse is true.  There is substantial industry momentum, with winners tending to continue to win, and losers having a propensity to continue their losses…

Figure 8

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Point and Figure RS Signal Implementation

September 2, 2014

Over the course of the summer we published three different whitepapers looking at point and figure relative strength signals on a universe of domestic equities.  In the first two papers, we demonstrated the power of using PnF RS signals and columns to find high momentum stocks, and then we looked at the optimal box size for calculating relative strength.  If you were on vacation and happened to miss one of the first two papers they can be found here and here.

The third paper examines the performance profiles you can reasonably expect by following a process designed around point and figure relative strength.  You can download a pdf version of the paper here.  Most momentum research focuses on performance based on purchasing large baskets of stocks, which is impractical for non-institutional investors.  Once we know that the entire basket of securities outperforms over time the next logical question is, “What happens if I just invest in a subset of the most highly ranked momentum securities?”  To answer this question, we created portfolios of randomly drawn securities and ran the process through time.  Each portfolio held 50 stocks at all times, which we believe is a realistic number for retail investors.  Each month we sold any security in the portfolio that was not one of the top relative strength ranks.  For every security that was sold, we purchased a new security at random from the high relative strength group that wasn’t already held in the portfolio.  We ran this process 100 times to create 100 different portfolio return streams that were all different.  The one thing all 100 portfolios had in common was they were always 100% invested in 50 stocks from the high relative strength group.  But the exact 50 stocks could be totally different from portfolio to portfolio.

The graph below taken from the paper shows the range of outcomes from our trials.  From year to year you never know if your portfolio is going to outperform, but over the length of the entire test period all 100 trials outperformed the broad market benchmark.

 (Click To Enlarge)

We believe this speaks to the robust nature of the momentum factor, and also demonstrates the breadth of the returns available in the highest ranked names.  It wasn’t just a small handful of names that drove the returns.  As long as you stick to the process of selling the underperforming securities and replacing them with stocks having better momentum ranks there is a high probability of outperformance over time.  Over short time horizons the outperformance can appear random, and two people following the same process can wind up with very different returns.  But over long time horizons the process works very well.

Past performance is not indicative of future results.  Potential for profits is accompanied by possibility of loss.  A momentum strategy is NOT a guarantee.  There may be times where all investments and strategies are unfavorable and depreciate in value. 

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Diversification by Style Box or by Risk Factor?

August 11, 2014

Andrew Ang, in his new book Asset Management: A Systematic Approach to Factor Investing, identifies a key obstacle for many wealthy investors–specifically business owners—who liquidate and then look to invest those assets in the financial markets:

It can be counterintuitive for rich individuals to realize that preserving wealth involves holding well-diversified portfolios that have exposure to different factor risk premiums.  They created their wealth by doing just the opposite: holding highly concentrated positions in a single business.

We’ve probably all heard someone make the case against diversification by saying something along the lines of, “My plan has been to put all my eggs in one basket and to watch that basket very closely!”  However, at some point most people have a desire to diversify their risks.

Two of the most rigorously tested risk factors are momentum and low volatility.  Compelling research suggests that both factors have demonstrated the ability to outperform over time and these two factors have the added benefit of having a relatively low correlation to one another.  For example, consider the correlation of the PowerShares DWA Momentum ETF (PDP) and the PowerShares S&P 500 Low Volatility ETF (SPLV) since 2011, the inception for SPLV.


Source: Yahoo! Finance and iShares.  The correlations above are based on monthly total returns, inclusive of dividends, but not inclusive of transaction costs.  Past performance is not indicative of future results.  Potential for profits is accompanied by possibility of loss.  

While much of the industry is still focused on seeking diversification between style boxes, I believe investors would be better served to start focusing on diversification between risk factors, like momentum and low volatility.   As you can see, there has been much lower correlation between PDP and SPLV than there has been between Growth and Value over this time.

Dorsey Wright & Associates is the index provider for the PowerShares DWA Momentum ETF (PDP).

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Point and Figure Relative Strength Box Sizes

July 25, 2014

In June, we released Point and Figure Relative Strength Signals, by John Lewis, CMT.  This white paper provided important insights into using PnF relative strength signals.  The study included research covering the period 1990-2013.

Securities on a buy signal and in a column of X’s have the best intermediate and long term relative strength characteristics so that is the basket of securities we would expect to perform the best over time. That is certainly the case over time. Maintaining a portfolio of stocks on relative strength point and figure buy signals and in columns of X’s dramatically outperformed the other three point and figure relative strength states.

John has now written a follow-up white paper that analyzes a different aspect of PnF relative strength signals: Box Sizes.  Click here to access Point and Figure Relative Strength Box Sizes.  This paper addresses the frequently asked question, “What box size should I use?” and will help answer the question of why 6.50% box size is the default box size on the Dorsey Wright research database.  This white paper also studies the period of 1990-2013.  A summary table of the results is shown below:

box sizes

The data in Table 1 helps us determine what the equivalent of an intermediate term horizon is in terms of point and figure box sizes. Much like the time-based methods, the returns suffer when the box size is too small or too large. In the case of the former, the system picks up too much of the short term trading noise. In the case of the latter, too much has to happen in order for the point and figure chart to register a change. The sweet spot is in the 6.5% to 7.5% box size range. Using a 6.5% box size means that a security has to underperform the broad market by 19.5% in order to change columns and be shifted out of the group that qualifies as having the best relative strength. The large percentage reversal required may surprise many people, but relative price moves in the 20% to 25% range exhibit the best long term performance. The magnitude of these moves indicates how important it is to stick with a strong stock during the dynamic part of its price appreciation cycle. We have noticed over the years that stocks with strong momentum characteristics are often volatile and are prone to sharp pullbacks before continuing to new highs. Trying to “get out in front” of the trend change by using a smaller box size will certainly be a better method when the trend change happens, but the data indicates this is hard to predict .

Stay tuned for part three of this series of white papers which will likely be released next month.

A relative strength strategy is NOT a guarantee.  There may be times where all investments and strategies are unfavorable and depreciate in value.  Past performance is no guarantee of future returns.

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Unrealistic Paradigms

July 21, 2014

The NYT unintentionally gives a great example of how NOT to analyze active equity strategies:

A new study by S.&P. Dow Jones Indices has some fresh and startling answers. The study, “Does Past Performance Matter? The Persistence Scorecard,” provides new arguments for investing in passively managed index funds — those that merely try to match market returns, not beat them.

Yet it won’t end the debate over active versus passive investing, because it also shows that a small number of active investors do manage to turn in remarkably good streaks for fairly long periods.

The study examined mutual fund performance in recent years. It found that very few funds have been consistently outstanding performers, and it corroborated the adage that past performance doesn’t guarantee future returns.

The S.&P. Dow Jones team looked at 2,862 mutual funds that had been operating for at least 12 months as of March 2010. Those funds were all broad, actively managed domestic stock funds. (The study excluded narrowly focused sector funds and leveraged funds that, essentially, used borrowed money to magnify their returns.)

The team selected the 25 percent of funds with the best performance over the 12 months through March 2010. Then the analysts asked how many of those funds — those in the top quarter for the original 12-month period — actually remained in the top quarter for the four succeeding 12-month periods through March 2014.

The answer was a vanishingly small number: Just 0.07 percent of the initial 2,862 funds managed to achieve top-quartile performance for those five successive years. If you do the math, that works out to just two funds. Put another way, 99.93 percent, or 2,860 of the 2,862 funds, failed the test.

Yes, that is right.  Unless a fund was in the top quartile of performance for each of the four years it was considered a failure.  The premise of the article is that investors should employ index funds unless they can find active strategies that outperform every year.  Talk about setting yourself up for failure!  I am aware of a number of investment factors that have generated outperformance over time (momentum, value, low volatility), but I am aware of nothing that outperforms every year.

The returns of those managers who are able to generate outperformance over time is rather lumpy.  Consider the performance profile of the best performing managers of the 1990’s as an example:

Cambridge Associates, a money management consulting firm, did a study of the top-performing managers for the decade of the 1990s. In 2000, they could look back and see which managers had returns in the top quartile for the entire decade. Presumably, these top quartile managers are precisely the ones that clients would like to identify and hire. Cambridge found that 98% of those top managers had periods of underperformance extending three years or more. 98% is not a misprint!  Even more striking, 68% of the top managers ended up in the bottom quartile for some three-year period and a full 40% of them visited the bottom decile during that ten years. Clearly, there are good and bad periods for every strategy.

Investing is challenging enough without setting yourself up for failure by placing unrealistic expectations on active managers.  I have nothing against index funds.  We use them in a number of our strategies and I think many investors can benefit from using them as part of their allocation.  However, they are not a panacea.

This example is presented for illustrative purposes only and does not represent a past recommendation.  A relative strength strategy is NOT a guarantee.  There may be times where all investments and strategies are unfavorable and depreciate in value.

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The Virtues of Pragmatism

July 10, 2014

Aren’t you glad you are a trend follower?  Leaving aside the potential performance advantages of trend following  for a moment, it is just less drama.  Case in point, as a trend follower you can avoid getting caught up in the endless debate about whether or not the market is overvalued.  Consider the following analysis from Barry Ritholtz:

It has become commonly accepted that stocks are very expensive, overbought and perhaps even in a bubble.

JPMorgan Chase & Co.’s latest quarterly chart book (you can download it here) takes issue with those conventions.


As you can see from the chart above, U.S. equity prices closely match their long-term average price-to-earnings ratio of 15.5. That’s precisely at fair value if you are comparing it to the Standard & Poor’s 500 Index earnings-per-share average of analyst estimates for the next 12 months.

That is one of the most common ways to value companies, but there are plenty of other approaches that show stocks either over or undervalued.

It is commonly stated by those immersed is the valuation debate that valuations may not matter in the short-run, but they absolutely matter in the long-run.  That may be true, but when it comes to your experience as an advisor with your clients, what are the practical implications of getting out the of the market 3 years (as an example) before the bull market ends?  That’s right, you get fired.

The principle of keeping it simple, has served Dorsey Wright very well for almost three decades now.  What is a trend follower’s interpretation of the following chart of the S&P 500?  A positive trend with no signs of deterioration at this point.

S&P 500

Source: Dorsey Wright, as of 7/10/14

This is no way negates the need for prudent financial planning and asset allocation.  Nor does this make us perma-bulls.  It does, however, make us pragmatic.  As to whether or not trend following “works” I would recommend reading the following white papers by John Lewis:

This example is presented for illustrative purposes only and does not represent a past recommendation.  A relative strength strategy is NOT a guarantee.  There may be times where all investments and strategies are unfavorable and depreciate in value.

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PnF Relative Strength Signals White Paper

June 15, 2014

Earlier this week we released a whitepaper on Point and Figure Relative Strength signals. If you haven’t had a chance to read the paper you can access a copy of it here. The current paper covers the basics of how different Point and Figure Relative Strength patterns perform over time. In the coming months, we hope to release a few follow up papers that will look at some other aspects of a momentum strategy.

Relative Strength (also known as momentum) has been an incredibly robust factor for quite some time. Technicians have used momentum as a primary tool for over 100 years, but it has been in the last 20 years or so that research into the factor has really taken off. In 1993, “Returns to Buying Winners and Selling Losers” was published and the exploration of the momentum factor really began by the academics. There are hundreds of papers in the public domain that have found the momentum factor works across asset classes and within asset classes. As long as there is some volatility and some dispersion in the universe you can generally make a relative strength strategy work over time.

Most of the current research centers around time-based calculations of momentum. For example, you sort securities by their trailing 12 month price return and then put the top decile into the portfolio. Point and Figure, on the other hand, ignores time and really looks at volatility. The more volatile something is versus its benchmark the more columns you will have moving across the page. What all of these methods have in common is ranking on an intermediate term time horizon. If you use too short of a time window or too small of a point and figure box size you wind up trading on the noise rather than the trend. If you use too long of a window or too large of a box size you get into an area where mean reversion rather than trend continuation is the rule.

Every momentum study I have seen comes to the same conclusions. Buying the stuff with the best momentum works very well over time. You can short the stocks with weak momentum, but that presents two big problems. First, not everyone can sell positions short. And second, the laggard rallies off of bear market bottoms are a killer on the short side. You can see that in the performance table in the whitepaper in 2003 and 2009. The laggards tend to do very well off the bottom and the leaders tend to underperform. But otherwise, momentum is a very robust and consistent factor.

So why does momentum work, and why does it get such a bad rap in the press? I think the latter is easy to explain. Everyone understands the concept of getting something at a discount. If I want a roll of paper towels why would I buy one for $1.00 when I could get the same roll on sale for $0.50? Everyone can identify with that, and deals with those decisions on a daily basis. But not all stocks are the same! Buying IBM is not similar to buying AAPL. Each stock has its own identity. It is similar to betting on the NBA Finals right now. After the Spurs embarrassed the Heat the last two games who would you bet on to win the finals? Probably the Spurs because they have now demonstrated the ability to outperform the Heat. A good momentum stock does the same thing. It demonstrates the ability to outperform. Why not buy something that is doing well rather than betting on the Heat and hoping Mario Chalmers remembers how to shoot and Ray Lewis sends Dwayne Wade some deer antler spray for his creaky knee? Vegas figured this out a long time ago! If they didn’t set odds on the series everyone would bet on the Spurs right now because they have a high probability of winning. They aren’t a sure thing to win, but the odds are definitely in their favor. They have to entice people to bet on the Heat to try to even the money on both sides of the bet. The Heat can certainly still win, but it is going to take a major change in what we have seen so far in the series. When you are buying stocks there are no odds! The stocks on a buy signal and in a column of x’s have a high probability of outperforming over time. Why not just buy those rather than bottom fishing? You don’t get 2:1 odds if you buy the beaten down stock. You don’t get worse odds if you buy the winner. It’s all even odds. It is a great gift just sitting there waiting to be opened. Merry Christmas.

The conclusion we hope everyone eventually reaches is simple: disciplined implementation of the strategy is the ultimate key to success. Step back and think about what is the the BX group, for example. There are stocks with good fundamentals, bad fundamentals, some on pullbacks, and some on spikes. I’m sure that BX group contained tons of stocks that were bought right at the top and then sold very shortly after they were purchased. There were stocks that pulled back, fell out of the top group, and then turned around and shot to new highs. It is important to realize that all those potential problems didn’t matter in the long run. Somehow that best group kept right on chugging. The real issue is the opportunity cost of not constantly packing the portfolio in to the best momentum names. Hoping a stock turns around because you just bought it and your clients might be upset, not buying a strong relative strength name because it is on a spike, and not re-buying a strong stock after you had to sell it are not included in the BX group equity curve. The opportunity cost of leaving things that aren’t strong in the portfolio is what kills performance over time. Weak stocks are for value buyers. If you are going to buy high relative strength stocks you have to keep strong names in the portfolio.

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Rolling 10-Year Momentum Returns

February 26, 2014

To get a sense for just how effective momentum investing has been over time, consider the rolling 10-year returns for the following momentum index compared to the S&P 500.  The data starts in January 1927 so the first 10-year period ends in January 1937.

momentum 02.26.14

Source: Ken French Data Library, Global Financial Data (1/1/1927 – 1/31/2014); Returns include dividends but do not include any transaction costs; The momentum index is based the Ken French momentum series (Equal-weighted index of the top half market cap, top third momentum of a universe of U.S. stocks).  This momentum index rebalanced monthly based on trailing 12 month returns of the securities.  

The chart below measures the difference between the 10-year returns for the momentum index minus the 10-year returns for the S&P 500:

momentum2 02.26.14

Note that the momentum index outperformed the S&P 500 in every rolling 10-year period during this study.  Yes, some 10-year periods were better than others for momentum from a relative performance perspective.  Also, the difference in performance between momentum and the S&P 500 for the 10-year period ending 1/31/2014 was 2.97%.  This is on the lower end of the range over the test period.  It would not surprise me at all to see this margin of outperformance revert to the mean in the years ahead (the average difference in performance between momentum and the S&P 500 for rolling 10-year periods was 5.63% over this test period).

Past performance is no guarantee of future returns.

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It’s All At The Upper End

February 3, 2014

Almost all of the performance from a relative strength or momentum model comes from the upper end of the ranks.  We run different models all the time to test different theories or to see how existing decision rules work on different groups of securities.  Sometimes we are surprised by the results, sometimes we aren’t.  But the more we run these tests, the more some clear patterns emerge.

One of these patterns we see constantly is all of the outperformance in a strategy coming from the very top of the ranks.  People are often surprised at how quickly any performance advantage disappears as you move down the ranking scale.  That is one of the things that makes implementing a relative strength strategy so difficult.  You have to be absolutely relentless in pushing the portfolio toward the strength because there is often zero outperformance in aggregate from the stuff that isn’t at the top of the ranks.  If you are the type of person that would rather “wait for a bounce” or “wait until I’m back to breakeven,” then you might as well just equal-weight the universe and call it a day.

Below is a chart from a sector rotation model I was looking at earlier this week.  This model uses the S&P 500 GICS sub-sectors and the ranks were done using a point & figure matrix (ie, running each sub-sector against every other sub-sector) and the portfolio was rebalanced monthly.  You can see the top quintile (ranks 80-100) performs quite well.  After that, good luck.  The “Univ” line is a monthly equal-weighted portfolio of all the GICS sub-sectors.  The next quintile (ranks 60-80) barely beats the universe return and probably adds no value after you are done with trading costs, taxes, etc…  Keep in mind that these sectors are still well within the top half of the ranks and they still add minimal value.  The other three quintiles are underperformers.  They are all clustered together well below the universe return.

 (Click on image to enlarge)

The overall performance numbers aren’t as good, but you get the exact same pattern of results if you use a 12-Month Trailing Return to rank the sub-sectors instead of a point & figure matrix:

 (click on image to enlarge)

Same deal if you use a 6-Month Trailing Return:

(click on image to enlarge)

This is a constant theme we see.  The very best sectors, stocks, markets, and so on drive almost all of the outperformance.  If you miss a few of the best ones it is very difficult to outperform.  If you are unwilling to constantly cut the losers and buy the winners because of some emotional hangup, it is extremely difficult to outperform.  The basket of securities in a momentum strategy that delivers the outperformance is often smaller than you think, so it is crucial to keep the portfolio focused on the top-ranked securities.

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Improving Sector Rotation With Momentum Indexes

January 21, 2014

Sector has been a popular investment strategy for many years.  The proliferation of sector based exchange traded funds has made it quick and easy to implement sector bets, but has also added a level of complexity to the process.  There are now many different flavors of ETF’s for each macro sector ranging from simple capitalization weightings to semi-active quantitative models to construct the sector index.  The vast array of choices in each sector allows investors to potentially add additional performance over time versus a simple capitalization based model.

Dorsey, Wright has a suite of sector indexes based on our Technical Leaders Momentum factor.  These indexes are designed to give exposure to the securities with the best momentum characteristics in each of the 9 broad macro sectors (Telecomm is split between Technology and Utilities depending on the industry group).  Long time readers of our blog should be aware of all of the research that demonstrates how effective the momentum factor has been over time providing returns above a broad market benchmark.  Using indexes constructed with the momentum factor have the potential to add incremental returns above a simple capitalization weighted sector rotation strategy just like they do on the individual stock side.

The sector SPDRs are the most popular sector suite of exchange traded products.  When investors make sector bets using this suite of products they are making a distinct sector bet and also making a bet on large capitalization stocks since the sector SPDRs are capitalization weighted.  There are times when large cap stocks outperform, but there are also times when the strength might be in small cap, value, momentum, or some other factor.  By not considering other weighting methodologies investors are potentially leaving money on the table.

We constructed several very simple sector rotation models to determine how returns might be enhanced by implementing a sector rotation strategy with indexes based on momentum.  The base models were created with either 3 or 5 holdings from the sector SPDR universe.  Each month a trailing 3 or 6 month return was calculated (based on the model specification) and the top n holdings were included in equal weights in the portfolio.  Each month the portfolio was rebalanced with the top 3 or 5 sector SPDRs based on the trailing return.  This is an extremely simple way to implement a momentum based sector rotation strategy, but one that proves to be surprisingly effective.

The second group of portfolios expanded the universe of securities we considered to implement the strategy.  All of the momentum rankings were still based on the trailing returns of the sector SPDRs, but we made one small change in what was purchased.  If, for example, the model selected Healthcare as one of the holdings we would buy either the sector SPDR or our Healthcare Momentum Index.  The way we determined which version of the sector to buy was simple: whichever of the two had the best trailing return (the window was the same as the ranking window) was included in the portfolio for the month.  In a market where momentum stocks were performing poorly the model would gravitate to the cap weighted SPDRs, but when momentum was performing well the model would tend to buy momentum based sectors.  Making that one small change allowed us to determine how important implementing the sector bet actually was.

 (Click Image To Enlarge)

The table above shows the results of the tests.  Trials were run using either 3 or 6 month look back windows to rank the sectors and also with either 3 or 5 holdings.  In each case, allowing the model to buy a sector composed of high momentum securities was materially better than its cap weighted counterpart.  Standard deviation also increased, but the returns justified the increased volatility as the risk adjusted return increased in each case.

This is one simple case illustrating how implementing your sector bests with different sector construction philosophies can be additive to investment returns.  The momentum factor is one of the premier investment anomalies out there, and using a basket of high momentum stocks in a specific sector has shown to increase returns in the testing we have done.

The performance numbers are not inclusive of any commissions or trading costs .  The Momentum Indexes are hypothetical prior to 3/28/2013 and do not reflect any fees or expenses.  Past performance is no guarantee of future returns.  Potential for profit is accompanied by potential for loss.  The models described above are for illustrative purposes only and should not be taken as a recommendation to buy or sell any security or strategy mentioned above.  Click here for additional disclosures.

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DWA Technical Leaders Webinar: Q1 Updates

January 20, 2014

On Wednesday, January 16th, Tom Dorsey, Founder and President of DWA, Tammy DeRosier, Chief Operating Officer, and John Lewis, Senior Vice President and Portfolio Manager, conducted a webinar around the most recent quarterly rebalances across the DWA Technical Leaders Indexes, as well as practical implementation ideas for using the four Momentum ETFs that track these indexes.

Follow this link for a replay of this webinar.


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DWA Technical Leaders Index Trade Profiles

January 6, 2014

The Dorsey, Wright Technical Leaders Index is composed of a basket of 100 mid and large cap securities that have strong relative strength (momentum) characteristics.  Each quarter we reconstitute the index by selling stocks that have underperformed and by adding new securities that score better in our ranking system.  We began calculating the index in real-time at the end of 2006.  Over the last seven years there have been quite a few deletions and additions as the index has adapted to some very dynamic market conditions.

Any relative strength or momentum-based investment strategy is a trend following strategy.  Trend following has worked for many years in financial markets (although not every year).  These systems are characterized by a several common attributes: 1) Losing trades are cut quickly and winners are allowed to run, 2) there are generally a lot of small losing trades, and 3) all of the money is made by the large outliers on the upside.  When we look at the underlying trades inside of the index over the years we find exactly that pattern of results.  There is a lot going on behind the scenes at each rebalance that is designed to eliminate losing positions quickly and maintain large allocations to the true winners that drive the returns.

We pulled constituent level data for the DWATL Index going back to the 12/31/2006 rebalance.  For each security we calculated the return relative to the S&P 500 and how many consecutive quarters it was held in the index.  (Note: stocks can be added, removed, and re-added to the index so any individual stock might have several entries in our data.)  The table below shows summary statistics for all the trades inside of the index over the last seven years:


The data shows our underlying strategy is doing exactly what a trend following system is designed to accomplish.  Stocks that aren’t held very long (1 to 2 quarters), on average, are underperforming trades.  But when we are able to find a security that can be held for several quarters, those trades are outperformers on average.  The whole goal of a relative strength process is to ruthlessly cut out losing positions and to replace them with positions that have better ranks.  Any investor makes tons of mistakes, but the system we use to reconstitute the DWATL Index is very good at identifying our mistakes and taking care of them.  At the same time, the process is also good at identifying winning positions and allowing them to remain in the index.

Here is the same data from the table shown graphically:


You can easily see the upward tilt to the data showing how relative performance on a trade-level basis improves with the time held in the index.  For the last seven years, each additional consecutive quarter we have been able to keep a security in the Index has led to an average relative performance improvement of about 920 basis points.  That should give you a pretty good idea about what drives the returns: the big multi-year winners.

We often speak to the overall performance of the Index, but we sometimes forget what is going on behind the scenes to generate that return.  The process that is used to constitute the index has all of the characteristics of a trend following system.  Underperforming positions are quickly removed and the big winning trades are allowed to remain in the index as long as they continue to outperform.  It’s a lot like fishing: you just keep throwing the small ones back until you catch a large one.  Sometimes it takes a couple of tries to get a keeper, but if you got a big fish on the first try all the time it would be called “catching” not “fishing.”  I believe part of what has made this index so successful over the years is there is zero human bias that enters the reconstitution process.  When a security needs to go, it goes.  If it starts to perform well again, it comes back.  It has no good or bad memories.  There are just numbers.

The performance numbers are pure price return, not inclusive of fees, dividends, or other expenses.  Past performance is no guarantee of future returns.  Potential for profit is accompanied by potential for loss.  A list of all holdings for the trailing 12 months is available upon request.

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60/40 Portfolio Subjected To Historical Data

December 30, 2013

Conventional wisdom says you don’t need anything more complicated than a 60/40 portfolio.  From the WSJ:

Investment advisers and managers usually recommend some variant of 60% stocks and 40% bonds (with fewer stocks and more bonds as you get older). The portfolio should be rebalanced at least once a year—selling some of what has done well to buy more of what has done poorly, restoring the target proportions.

The theory is that when stocks do badly bonds will do well, and vice versa. But the theory is flawed.

Historically, this portfolio has only succeeded when stocks, or bonds, or both, have been reasonably valued or cheap. In the past, if you had invested in this portfolio when stocks and bonds were both overvalued, it proved a very poor deal.

Using data on stock and bond returns from New York University’s Stern School of Business and inflation data from the Labor Department, I looked at how such a portfolio performed in the past when measured in real, inflation-adjusted dollars.

It lost a third of its value from 1928 to 1932, and it lost value over two longer periods as well, from 1936 to 1947 and from 1968 to 1982—even before deducting taxes and costs. In reality, most investors would have done very badly indeed.

Another theory that doesn’t hold up when subjected to real data.

So what are your alternatives?  How about expanding the investment universe to include domestic equities, international equities, inverse equities, currencies, commodities, real estate, and fixed income.  John Lewis conducted a rigorous test of this type of Tactical Asset Allocation strategy in this 2012 white paper.  Of particular interest in light of this WSJ article, note the performance of the Tactical Asset Allocation strategy compared to a 60/40 portfolio over time.

From John Lewis’ white paper:

factor summary

Table 2 shows a summary of returns using different lookback periods for various relative strength ranking factors.  Once again, the robust nature of relative strength is shown by the ability of multiple random trials to outperform using a variety of factors.  Some of the intermediate-term factors work better than others, but they all exhibit a significant ability to outperform over time.  At very short lookback periods, such as 1 month, the performance is not as good as at longer periods.  Relative strength models are not designed to catch every small wiggle, and investors need to allow positions to ebb and flow over time.  It is also clear from Table 2 that as you begin to lengthen your lookback period, returns begin to degrade.  While a long-term buy and hold approach to a relative strength strategy is necessary, the investments within the strategy are best rotated on an intermediate-term time horizon.

We have employed this type of tactical approach to portfolio management in The Arrow DWA Tactical Fund (DWTFX) and our Global Macro separately managed account.  DWTFX is +25.70% YTD through 12/27/13 and has outperformed 95 percent of its peers in 2013.


Source: Morningstar

Investors may benefit from looking beyond just domestic equities and domestic fixed income when deciding what strategies they want to employ to get them through the next couple of decades.

Past performance is no guarantee of future returns.  

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More Supporting Data For Momentum

December 30, 2013

The WSJ reports the performance of Sam Eisenstadt’s, former head of research at Value Line, stock-ranking system from 1965-2012.

Though the system is proprietary, its two primary factors are known as “price momentum” and “earnings momentum.” A stock is ranked higher to the extent its performance over the trailing year has been good and its earnings growth has accelerated. Despite the name “Value Line,” the stocks it favors fall closer to the “growth” end of the spectrum.

The system has been phenomenally successful over the past five decades. From 1965 through 2012, according to data on Value Line’s website, Group 1 stocks on average have gained an annualized 12.9%, before dividends. That’s nearly seven percentage points per year better than the S&P 500’s 6.1% annualized return over the same period, and more than 22 percentage points ahead of the minus 9.8% return for Group 5.

Once again, the data confirms the effectiveness of price momentum.

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Podcast: A Profile of our Growth Portfolio

November 25, 2013

A Profile of our Growth Portfolio

Mike Moody and Andy Hyer

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