Q4 Manager Insights

January 6, 2017

The stock market had a solid end to the year and finished up the year strong. U.S. equities, as represented by the S&P 500 Total Return, were up 3.8% for the quarter and 12.0% for the year. Many people had predicted a poor year for U.S. equities so these strong returns caught a lot of investors off guard. The returns for other asset classes were mixed during the fourth quarter, but on a year-to-date basis most of the broad asset class benchmarks finished in positive territory. One standout was small cap U.S. equities. The Russell 2000 Total Return Index was up 8.8% in the last three months of the year and 21.3% for the year. Bonds lagged equities, but did end the year with positive returns. Another big story was commodities, which had seen dreadful returns for the past couple of years. The rally in energy propelled the S&P GSCI Commodity Index to a gain of 11.4% in 2016.

The biggest story during the fourth quarter had to be Trump winning the election. Very few people predicted that outcome, and even fewer predicted what the market’s reaction would be with a Trump victory! Once again, we learned how difficult it is to mix politics with investing. We have always focused on price and left things like political forecasting to the people that need material for their television appearance. Initially, the market had a huge selloff when it became clear Trump was going to win. But before the dust even settled the market turned right around and shot higher as confused investors looked on. A Trump presidency will no doubt be filled with a lot of uncertainty. However, there are a few themes that have emerged since the election that have affected our portfolios. Financials (specifically Banks) have rallied on the hopes of looser government regulation. Whether or not that is good for Americans as a whole is not really for us to decide, but it should make it easier and cheaper for banks to do business going forward and the market recognized that very quickly. We saw our exposure to Financials increase after the election as a result of the sector’s relative strength. Trump’s plans for improving infrastructure also helped Industrials rally in to the end of the year. Again, we have seen that strength flow through to our momentum models, and we have large allocations to that sector in most of our strategies.

Lost in the shuffle of the fourth quarter was the Federal Reserve raising rates for the first time in a year. The move was largely expected, but that didn’t prevent bonds from having a rough quarter. The positive return from bonds for 2016 was largely earned in the first part of the year. The Barclays US Aggregate Index was down –3.0% for the quarter so the fear of rising rates among investors is real. Our Tactical Fixed Income strategy navigated the choppy waters very nicely in 2016. We were able to post nice gains at the beginning of the year by being in long maturity bonds as well as areas that do well in a strong bond market (High Yield, Emerging Markets, Corporates). As the probability of a rate hike grew and bonds began to falter we were able to switch the portfolio out of the “risk-on” bonds and into short term U.S. Treasuries to preserve those gains.

The two major events discussed above caused quite a bit of change in our models. That was really the theme all year. While the market posted impressive gains as a whole, there were a lot of crosscurrents under the surface. All year long, old trends died, and new ones emerged. At the beginning of the year, investors were favoring Low Volatility stocks and equities with high dividend yields. As the year wore on that leadership began to fall apart and investors began favoring more “risk on” investments like small caps that are more volatile and don’t provide as much current income. Gold was also a theme that came and went during the year. There were many themes like that over the course of 2016 and that led to us making a lot of changes in the portfolios throughout the year. Trading activity was much higher than normal as our models continued to adapt to emerging themes and get rid of old ones. It was definitely a transition year, but it looks like we are well positioned heading in to the new year. The crosscurrents under the surface will eventually subside. When they do, we should be well positioned to capture the trends in the new leadership.

This information is from sources believed to be reliable, but no guarantee is made to its accuracy.  This should not be considered a solicitation to buy or sell any security.  Unless otherwise stated, performance numbers are not inclusive of dividends or fees.  Investors cannot invest directly in an Index.  Past performance is not indicative of future results.  Potential for profits is accompanied by possibility of loss

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Everyone Loves A Story

November 22, 2016

There is always a reason not to invest in stocks.  Over the years there have been countless reasons that have kept investors out of the market.  The graph below was posted on Twitter back in August by @danielcrosby.  I pulled that chart up again last week after the election and reminded myself how many reasons over the years there have been to sit on the sidelines.  The sad truth is everything on the chart had well-reasoned arguments for why investors should have avoided the stock market.  But the market was actually up 100 times more than inflation over the time period in the chart.

smart

It is so difficult to avoid getting caught up in the madness because the issues surrounding these problems are real.  The story is always more persuasive than the truth.  Try playing a little game the next time you are at a cocktail and the stock market comes up.  There is usually someone around that has a story for a certain stock or how the economy will impact global returns.  Everyone standing around listening is captivated because they love the story.  Eventually someone will ask you how you go about investing.  My usual response is something like, “Well, I just buy a bunch of stocks that are going up and hold them until they stop going up.”  It is amazing how quickly everyone needs to go to the bar for a refill after hearing my “story.”

But that is one of the best ways to make money in the market over the long term!  Find an edge and exploit that edge as best you can.  Keep the process simple so that it is repeatable.  The more moving parts you have the more things you have that can break.  The more you focus on stories rather than a repeatable, proven process the more likely you will be to fall into the trap that plagues most investors.

Telling a story is great for sales.  You aren’t going to bring in many new accounts with the cocktail party speech I suggested above.  So go ahead a put a little sizzle on the steak in order to sell your process.  But when the rubber meets the road and it is time get down to the business of managing accounts don’t fool yourself.  There is elegance in simplicity.  Follow your process and don’t get caught up trying to make everyone think you are a really smart person.  There are always reasons to keep you out of the markets, but history shows that basing your investment decisions on the news story du jour isn’t the way to generate good returns over time.

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Point and Figure RS With Micro Caps

November 2, 2016

We have written quite a bit over the years about using point and figure relative strength for stock selection.  One of the whitepapers that discusses the basics of using RS can be found here.  In a lot of the research (including the whitepaper in the link) we use a universe of large capitalization stocks.  There really isn’t a good reason for the use of that universe.  Generally speaking, it is easier to deal with large caps because the information tends to be cleaner, and people are usually more interested in hearing about companies they know and recognize.

Point and figure relative strength works just as well or better on universes of small capitalization stocks.  The small cap universe is incredibly dynamic, and each year there are plenty of big winners and plenty of companies that wind up going out of business.  The dispersion of returns is very high, which is very good for a momentum strategy.  The micro capitalization universe has stocks in it that are even smaller than the small cap universe.  Micro caps are truly the wild, wild west.  The other characteristic with micro caps that make it a good universe for relative strength analysis is there is very little analyst coverage on these companies because they are so small.  That makes it easier to find undiscovered gems than it is in a larger capitalization universes.

To define a micro cap universe, we took the all of the stocks trading on US exchanges at the end of each calendar year and included everything ranked every company by market capitalization.  Stocks ranked from number 2000 through 3500 were included in the universe.  That universe essentially includes the bottom half (in terms of market cap) of the Russell 2000 and then another 500 stocks that don’t even qualify for inclusion in the Russell 2000 Index.  At each month we ranked every stock in the universe by relative strength versus a micro cap benchmark.  If you are unfamiliar with how a point and figure relative strength ranking works you can find an explanation in the paper linked to above.  Stocks were placed into one of four baskets at each month end based on point and figure relative strength chart configuration (signals and columns).  Each month the baskets were refreshed and all of the stocks in each basket were equally weighted.  Equally weighting each stock on a monthly basis would prove to be difficult in actual portfolio management, but we are just trying to get an idea about the power of relative strength in this universe.

microeqmicroret

(Click To Enlarge)

The Universe return listed above is the monthly equal weighted return of all the stocks in the universe.  We have included this because most broad market benchmarks are capitalization weighted and do not get the benefit of getting reweighted each month at no cost.  The returns of the universe are the best apples-to-apples comparison of how the universe was constructed.  The Benchmark return is a blended return of two different indexes: Russell 2000 Total Return and Russell Microcap Total Return.  The data on the Russell Microcap index doesn’t go back to the beginning of our test.  Before the microcap index was available we used the Russell 2000, and then linked that index performance to the microcap index when the data became available.

The stocks on a point and figure buy signal and in a column of X’s are the strongest stocks.  That basket of stocks performs remarkably well with very good volatility characteristics.  One thing that appears to be the case in the microcap universe is that relative strength helps find quality companies.  There are so many microcap companies that are, for lack of a better term, a hot mess.  Quality is extremely important when dealing with such small firms.  Most large cap companies are large because they have multiple products, experienced management teams, and defined processes and controls.  Sure, large cap companies make mistakes and their stock prices can go down quite a bit, but it is nothing like what goes on in the microcap space.  Momentum is very important when looking at very small stocks, and it also helps filter out a lot of the companies with poor quality characteristics.

Momentum works in a number of different markets and across markets.  In a universe of very small companies it is no different.  Using point and figure relative strength as a filter to focus on the strongest stocks in the universe is a great way to increase your odds of success.

Performance data is the result of hypothetical back-testing.  Investors cannot invest directly in an index.  Indexes have no fees.  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 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. Past performance is not indicative of future results. Potential for profits is accompanied by possibility of loss.  Neither the information nor any opinion expressed shall constitute an offer to sell or a solicitation or an offer to buy any securities, commodities or exchange traded products.  This document does not purport to be complete description of the securities, markets or developments to which reference is made.  Performance is inclusive of dividends, but does not include any transaction costs.

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Manager Insights: Third Quarter Review

October 10, 2016

The stock market spent the majority of the summer months moving sideways in a tight trading range.  The S&P 500 finished the quarter up almost 4%, and is sitting on a gain of 7.8% so far this year.  International equity markets were a bright spot, and outperformed domestic markets.  Developed markets finished up 6.5% and Emerging markets finished with a gain of 9.2%.  Bonds also finished in positive territory with a 0.5% gain.  Commodities were a weak spot in the third quarter.  After strong gains in the first six months of the year, the S&P GSCI Commodity Index gave back 4.2% over the summer and now sits at a gain of 5.3% for the year.

We continue to see rotation below the surface in a number of different asset classes.  This is nothing new, but we think the rotations we are seeing now have the potential to be very beneficial to our strategies.  In the U.S. equity markets there has been a momentum shift out of areas such as high dividend and low volatility stocks.  The relentless reach for yield drove many investors into stocks instead of bonds, and drove valuations to historically high levels.  The same valuation issues cropped up in low volatility stocks, which have been quite the hot ticket for the last year or so.  These are not the areas that usually lead a robust bull market.  Low Volatility, especially, tends to lead during down markets.  As a result, there was a lot of hand wringing about how solid the market actually was with that kind of leadership.  We felt the leadership we were seeing was more a result of investor’s preference for yield (and the lack of good fixed income options) rather than an indictment on the overall market.

The new leadership that appears to be emerging is what is traditionally considered positive for a strong bull market.  Small capitalization stocks have had spotty performance for a while, but they really picked up steam in the third quarter.  The Russell 2000 Total Return index finished with a gain of 9% moving it well ahead of the S&P 500 for the year.  Technology stocks also dramatically outperformed what could be considered the old leadership (Utilities, Consumer Staples, and Low Volatility) over the summer.  The relative improvement in these higher volatility areas shows investors are gaining more confidence in the market.  Confidence is an incredibly important piece of the puzzle for momentum strategies so we are looking at this new development very favorably.

The appetite for higher volatility investments is also increasing internationally.  As previously mentioned, Emerging markets had a fantastic third quarter.  Latin America has been the biggest driver of that performance so far this year.  For the past couple of years, international markets have not fared as well as our domestic markets.  That appears to be changing, and we are seeing increasing allocations to Emerging markets in those account styles that allocate internationally and globally.  The overall composition of those portfolios has changed dramatically over the course of the year.

As we head in to the final three months of the year it is impossible not to think about the upcoming election.  Frankly, it is nothing short of a circus sideshow at this point.  We fully understand the uncertainty people feel because neither candidate seems like a good choice.  That, however, is politics, and we are investing.  We encourage you not to get caught up in the headlines.  We do expect some volatility around election time, but we don’t think either candidate’s victory means doom or exuberance for the stock market.  It is incredibly difficult to forecast how politics will affect the market, and most so called experts get it wrong.  Keep your politics out of your investing plan and you will be much better off for it in the long run.  Never forget that there is always some reason not to invest, but the reality is that investing in stocks is a tremendous way to build wealth over time.

The final three months of the year should be interesting to say the least.  There are pieces falling in to place that lead us to believe our relative strength strategies can do quite well if these trends are sustainable.  If you have any questions about any of our strategies please give us a call at any time.

This information is from sources believed to be reliable, but no guarantee is made to its accuracy.  This should not be considered a solicitation to buy or sell any security.  Unless otherwise stated, performance numbers are not inclusive of dividends or fees.  Investors cannot invest directly in an Index.  Past performance is not indicative of future results.  Potential for profits is accompanied by possibility of loss.

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It’s All At The Upper End

September 7, 2016

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 recently.  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.  They are all clustered together well below the top quintile.

matrix ranks

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.

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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Combining Different Momentum Factors

July 18, 2016

Momentum can be calculated in a number of different ways.  As long as you are measuring the strength of price appreciation over an intermediate time horizon most logical calculation methods will work to one degree or another.  The standard, academic definition of momentum usually means taking the price appreciation of a security over a predefined time period and comparing it to all of the other securities in the universe.  It is common to use a 12 month window to calculate the price appreciation, but 6 and 9 months are also used.  (Note: most academic studies “skip” the most recent month to account for short-term mean reversion, but we will not address that here).  You can also calculate momentum using moving averages, slopes of regression lines, and point and figure relative strength charts.  There is no one right way to calculate momentum that will guarantee you better performance in the future.  This is similar to value investing.  There is no one correct way to determine a company’s intrinsic value and analysts use a lot of different tools to arrive at their valuations.

No matter what calculation method you choose it will have strengths and weaknesses.  What we want to look at here is using a combination of momentum calculations that have different strengths and weaknesses to improve the overall ranking system.  Two such calculation methods are the moving time window approach and point and figure relative strength.  The moving time window approach is very dependent on time.  If you are using a 12 month window, what happened 12 months plus one day ago makes no difference in the calculation.  In addition, anything that happens between those two points is also irrelevant.  All that matters is the distance from point A to point B.  Point and figure, on the other hand, removes time and only focuses on the volatility of ratio between a security’s price and an underlying benchmark.  That volatility can take place at any time in history and it will be reflected in the point and figure chart.  We have written several whitepapers on point and figure relative strength which you can access here if you want to learn more.

One challenge with any time window based approach is what to do with securities that have been extremely strong that begin to underperform.  Usually a system is set up to own securities from the top 10% or 20% or the ranks and sell them when the fall below a given threshold.  But it may take a tremendous amount of underperformance to actually fall out of the top of the ranks.  In the following example, NVIDIA Corp is up about 168% and at the top of the ranks.  In order to fall out of the top decile, NVIDIA would have to fall below the trailing performance of McCormick & Company, which is only up 35%.  Those numbers will be moving targets as the time window moves forward, but you get the idea – extreme performers have to fall quite a ways before they are actually sold.

Perf

(Click To Enlarge)

Combining point and figure relative strength signals with a time window approach can help solve this problem.  This issue doesn’t affect every security you buy.  It generally only affects the extreme performers, but it does happen often enough that you can substantially enhance returns by using a point and figure relative strength overlay.  Point and figure signals are divided up into “signals” for the long-term and “columns” for intermediate term signals.  We have found that the most bullish configuration is for a security to be on a buy signal and in a column of X’s versus the benchmark.  (You can find a whitepaper about that topic here).  That simply means a security is outperforming the benchmark on an intermediate term basis.

One of the things that makes a security’s point and figure relative strength chart less bullish is if the column reverses from X’s to O’s, which indicate the relative performance is declining over the intermediate term.  In order to get that reversal, the security must underperform the benchmark by 3 units of volatility.  This is known as the three box reversal, and has been around since the 1950’s.  The unit of volatility we are using is simply percentage performance of the performance versus the benchmark, which we set at 6.5% (click here to see research about box sizes).  So if a security underperforms the benchmark by 19.5% (6.5%*3) the column will flip from X’s to O’s and we than have a less bullish configuration.  This is also very similar to a trailing relative strength stop!

Adding a point and figure overlay to the example above would require NIVIDIA to underperform the market by about 20% to get sold from the portfolio.  It wouldn’t have to fall all the way out of the top of the ranks.  This can be a very big help when looking at securities with extreme performance.  The point and figure also does a couple of other things that make it better than a simple trailing stop.  First, it prevents the system from rebuying the security because it may still be the top performer after it hits the trailing stop.  Second, the point and figure configuration allows for an easy re-entry into the security if it reverses and continues to perform well.  If the security rises 19.5% (6.5% box size * 3 boxes) after the point and figure chart reverses to O’s, the chart will reverse back to X’s and the security will be eligible to be purchased again.

To measure the value of adding a point and figure overlay we ran Monte Carlo trials of a high momentum system.  The Monte Carlo trials are designed to eliminate the effect of picking a few lucky securities that might skew the test results.  We used an investment universe made up of the top 1000 stocks by market capitalization traded in the U.S.  The portfolios held 50 stocks at a time, and any new purchases were made out of the top decile of the ranks.  The ranks were based on the trailing 250 day total return performance.  We examined the portfolios each week and any security that fell out of the top quartile of the ranks was sold.  When a new security needed to be purchases we picked a stock out of the top decile at random that we didn’t already own.  There are always more securities in the top decile than we need to own because we had 50 holdings, but the top decile contained 100 securities.  By drawing securities at random we created 100 different equity curves over the period from 1989 through 2015.  The results of the 100 trials are shown below.  The mean is simply the average performance of all 100 trials during the year.  Some trials performed better than others, but since we were using the exact same process most of the performance difference from one model to the next can be attributed to luck.

RndPerf

(Click To Enlarge)

Over time, the 250 day trailing performance model does very well.  The average of all 100 trials over the entire test period annualizes at 14.66% (without transaction costs), and all 100 of the trials wound up outperforming the S&P 500.

The model used above was simply a trailing performance ranking.  It didn’t account for the extreme performance problem discussed above.  We ran the same Monte Carlo process using the 250 day performance ranks and added a point and figure relative strength overlay.  We required each security to be on a point and figure buy signal and in a column of X’s on its relative strength chart versus the S&P 500 Total Return Index.  The results of adding the point and figure overlay are shown below.

RndPerfPnF

(Click To Enlarge)

The Mean PnF line shows the average of all 100 trials with the point and figure relative strength overlay year by year.  Adding the point and figure overlay improves the average performance of the models 223 basis points per year from 14.66% to 16.89%.  That is a significant increase to an original system that was already generating quite a bit of outperformance.  By running 100 trials of randomly selected high momentum stocks, we can be very confident that the performance difference isn’t the result of a few lucky trades that one system picked up and the other didn’t.

The point and figure relative strength overlay acts similar to a trailing stop, and helps solve the problem of when extreme performers actually cease being high momentum securities.  Adding a point and figure relative strength overlay is an extremely effective way to boost the performance of a time based momentum system.

 

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 strategyduring 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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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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A Momentum Based Core Equity Strategy (Part 4)

June 14, 2016

To read Part 1 click here

To read Part 2 click here

To read Part 3 click here

The Core Equity strategy uses the three factor strategies discusses in Part 3.  In order to keep things as simple and non-optimized as possible I just included each model at a 1/3 weight of the total portfolio.  However, by using three different models you can adjust the weightings of each one to suit the end investor’s needs.  For example, if you needed less volatility you can just increase the weight of that model in the combined portfolio.

The Core Equity strategy is rebalanced monthly just like the individual factor models are.  Since a stock can be included in more than one of the factor models, the final model isn’t necessarily 300 stocks with equal weights.  Some stocks will have larger weights than others because they are in multiple models.  That is the only weighting difference though – there is no market capitalization or factor adjustment made to the stock weightings.

CE

In order to judge the fourth factor, size, I modified the final portfolio construction process to account for market cap.  Each stock’s market cap along with how many of the three factor models it was in was taken into account.  A mega cap stock in only one of the factor models might have a higher weight than a smaller cap stock in multiple models in this scenario.  Generally speaking, market capitalization weighting is sub-optimal for investment returns (not for capacity) and that is one big reason why Smart Beta has taken off.  We see the same thing here when we adjust for market cap.

CE2

The returns for the final Core Equity strategy add significant value over the broad market while keeping the volatility (standard deviation) close to that of the benchmark (below for a cap weight version of the Core Equity strategy).  More importantly, it helps smooth out the ride of the individual factor models.  In 1999, for example, Low Volatility was a large underperformer, but momentum was strong enough to carry the overall portfolio to strong returns.  Just two years later in 2001, the roles were reversed and it was Low Volatility and Value picking up the slack for the poor momentum returns.  You can see similar things happening in years like 2006, 2007, and 2011.  I think this point is vastly underrated because investors tend to abandon strategies at exactly the wrong times – after they have underperformed and are due for a rebound.  Combining all three factor strategies into one large Core Equity portfolio helps mask the underperformance of specific factor models and helps investors stay with underperforming strategies.

There are probably an infinite number of ways you can construct a core equity strategy using different factors.  Here, I have looked at just one that was designed to be extremely simple, non-optimized, and robust going forward.  I used some custom models to create the underlying factor models, but they shouldn’t be so dramatically different from existing ETF’s or mutual funds that something similar couldn’t be done on a smaller scale.  I’m sure there are better ways to construct the factor models and put them together in the final Core Equity strategy.  If you have any ideas about how that can be done I would love to hear them!

 

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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A Momentum Based Core Equity Strategy (Part 3)

June 13, 2016

To read Part 1 click here

To read Part 2 click here

I am using three different factor strategies (Momentum, Low Volatility, and Value) to form a Core Equity strategy.  A fourth factor, Size, will also be considered in the portfolio construction process (equal weighted instead of cap weighted).

All three of the factor strategies are constructed in a similar way.  The universe is the top 1000 market cap stocks traded in the U.S.  All of these stocks should have plenty of liquidity and should eliminate a lot of the issues that occur when you are dealing with small or micro-cap stocks.  All three strategies are rebalanced at the end of each month and have 100 stocks.  The strategies are run separately so in theory a stock could appear in all three strategies (I will account for this in the final model).  In addition, all three strategies use a Point and Figure momentum overlay that was discussed in part 2.  One of the goals was to make the three factor strategies as similar as possible in terms of portfolio construction to avoid as much optimization and curve fitting as possible.  All three of the factor strategies use very simple metrics and I believe they should be robust going forward.  There will certainly be times of underperformance (sometimes dramatic underperformance) from each of the strategies, but over time they have all shown to work very well.

The momentum strategy uses a simple, well-known momentum measure with a Point and Figure relative strength overlay.  Each month stocks are ranked by their trailing 250 day performance skipping the most recent 20 days (or one month).  This is a pretty standard definition for momentum.  The Point and Figure overlay actually does help returns over time, but not anywhere near what it does for the Value and Low Volatility models.  However, I wanted to include the Point and Figure overlay to keep the momentum model similar in portfolio construction to the other two.  Momentum provides really good returns, but is really volatile.  Momentum also has the distinction of being the least correlated with the other factors so they really help smooth out the volatility of a stand-alone momentum strategy.

Mom

The Value strategy uses a composite of four value ratios to rank the stocks in the universe: Price/Sales, Price/Book, Price/Free Cash Flow, and Price/Earnings.  Again, we use a Point and Figure relative strength overlay to improve the returns and filter out some of the value traps.  The universe is the same as the momentum model as are the rebalance dates and weightings.

ValMom

The Low Volatility strategy uses the standard deviation of daily returns over the trailing year to rank stocks in the universe.  The Point and Figure overlay is also used to keep everything consistent, and the other portfolio construction parameters remain the same.

LVMom

All three models perform very well versus the broad market (S&P 500 Total Return).  They do have their bumps along the way in terms of when they outperform and underperform, but often one strategy’s underperformance is offset by outperformance in another.

FactorRet

 

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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A Momentum Based Core Equity Strategy (Part 2)

June 10, 2016

To read Part 1 click here

The first post in this series laid out the background for a Core Equity strategy.  The goal is to use different factor strategies to create a more efficient portfolio than what you would get from traditional cap weighting.

The Value and Low Volatility strategies actually have a momentum component to them.  As I mentioned in the background post, we work with momentum all the time so it is easy for me to incorporate that with other factors to try and improve returns.  I realize this will lower some of the correlation benefits, but I think the potential return trade-off is worth it.

The graph below shows two versions of a Value model.  The Pure Value model selects the 100 cheapest stocks based on a composite indicator of Price/Sales, Price/Book, Price/Free Cash Flow, and Price/Earnings.  The Pure Value model below rebalances the portfolio quarterly with the 100 cheapest stocks out of a universe of the top 1000 market cap names.  The ValMom model uses the same value composite ranking system, but requires the stocks to be on a Point and Figure Buy Signal plus be in a Column of X’s.  For those of you not familiar with Point and Figure relative strength, this simply means the stock has been outperforming the broad market on an intermediate and long-term basis.

Val

You can see adding the momentum overlay to the value strategy is beneficial to returns.  Essentially, this helps filter a lot of the value traps.  More experienced Value investors have other methods to accomplish this goal, but adding the momentum overlay is something that works very well for what we do.

I did the same thing with the Low Volatility model.  This model picked 100 stocks with the lowest trailing one year daily standard deviation from a universe of 1000 top market cap names.  The portfolio was rebalanced quarterly.  Adding a momentum overlay helps ensure you have a portfolio of stocks that isn’t volatile, but has also demonstrated the ability to outperform the broad market over time.  Again, this will cut in to some of the correlation benefits of the factor strategies, but I think the return tradeoff is worth it.

LowVol

Adding the momentum overlay doesn’t improve returns as much as the Value example above, but it definitely does help.

The next post will detail the actual models I used for the factor strategies inside of the Core Equity model, but what has been discussed above is the basis for why I’m doing a couple of things differently than other models you might see and why the final model will have more of a momentum tilt.

 

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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A Momentum Based Core Equity Strategy (Part 1)

June 9, 2016

Smart Beta strategies have become increasingly popular over the last few years.  These factor based strategies rank groups of stocks or certain characteristics that have proven to provide long-term outperformance over broad market benchmarks.  The strategies tend to be extremely disciplined in their stock selection processes, which has been one of the biggest knocks on active management over the years.

One of the best ways to use the idea of Smart Beta or Factor Investing is to combine them to form a core equity portfolio.  Individually, the factor portfolios often have greater volatility than the overall market.  But many of the factors have excess returns that are negatively correlated so you can combine individual factors together to lower the volatility of the portfolio.  The lower volatility can come with a nice benefit: the factor portfolios have the potential to generate better returns than the overall market.

I was working on another project that involved putting some factor data together so I decided to combine the portfolios into a Core Equity portfolio to see how it looked.  I’ll have a few posts on the blog that run through the steps and the data I used to create the factor strategies as well as the combined strategy.  Since I have developed a lot of momentum strategies over the years you will notice that most of what is going in to this model has a momentum bias.  I’m sure there are some other ways to go about this, but I have worked with the momentum factor for so long it is really easy for me to incorporate it into other factor based strategies.  I thought it would be a useful exercise to run through this process on the blog because we generally get some interesting feedback and suggestions on how to improve the processes.

There are quite a few factors out there, but we will really focus on a manageable number of four: Momentum, Low Volatility, Value, and Size.  I took care of the Size factor in the portfolio construction process.  All of the strategies are equally weighted, which gives them a small cap tilt over time.  I will revisit the effect of the equal weighting tilt at the end when I look at the combined portfolio.  It is easy to estimate the size effect by running a cap weight and an equal weight version of the same portfolio.

The other three factors really form the backbone of the core equity strategy.  In the next post I will discuss how those strategies where constructed and the historical performance of each factor portfolio.

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Q1 Manager Insights

April 15, 2016

The year got off to a rocky start.  After the holidays ended and everyone returned to work, the stock market had a sharp selloff that left it in negative territory for January.  The market would eventually bottom out in mid-February and continue to recover through the end of the quarter.  Despite the early selloff, the S&P 500 actually finished up 1.3% for the quarter.  Small cap and International stocks didn’t fare as well as both of those categories finished in negative territory for the first three months of the year.  Fixed Income and interest rate sensitive securities were some of the best performing areas during the first quarter with broad bond market indexes finishing up about 3%.  Commodities also finished the quarter in negative territory, but did stop the relentless slide they had been on since last year.

Looking at the summary numbers for the first quarter might lead you to believe it was a ho-hum first three months of the year, but that was certainly not the case.  We saw a tremendous amount of rotation under the surface that had a big impact on all of our strategies.  In this piece, we normally like to update you on some big picture items that are affecting the markets and economy, but we felt it was more appropriate to go into greater detail about the specifics of the rotation we saw and how it affected our strategies.

The overarching theme for our investing style was that the laggards finally had their day in the sun.  Simply put, the stocks and asset classes that had been leading the market lower since last summer finally stopped going down and actually went up a lot from the lows.  This is known as a laggard rally, and is never a time when we perform well.  These laggard rallies come along every so often so we are used to them by now.  Everyone realizes the leaders can’t lead forever so we view these periods as an opportunity to refresh the portfolios and find new leadership.  More importantly, they don’t cause a change in our strategy, but they do cause trading activity to pick up as the old leadership is removed from the portfolios and our process tries to find the emerging leadership.  So, if you have noticed a lot more trading in your account recently, that is the reason why.

The changes we have made in the portfolios really changed the characteristics of some of the strategies.  One example of this was the weakening U.S. Dollar.  The Dollar had been strong for quite some time, and finally exhibited enough weakness that we needed to remove it from the portfolios.  We saw a weak dollar asset, Gold, added to many of the strategies.  The strong Dollar had caused quite a headwind for assets such as international equities and commodities, which generally do better in a weak dollar environment.  If the dollar continues to weaken, we expect to see more of these types of assets come into the strategies.  That would actually be a welcome change as it would allow our strategies to do what they do best: find bull markets anywhere around the globe (and in places many people are overlooking).

On the individual equity side, it was much the same as the asset class side.  The so-called FANGs (Facebook, Amazon, Netflix, and Google) were stellar performers last year, but had a difficult start to the year.  What really performed well were the things like energy and basic materials that had such dreadful performance last year.  In some of our other writing we touched on these issues during the quarter.  One example of this is when we look at the S&P 500 industry groups.  The worst relative strength groups outperformed the best performing group by more than 12% during the first quarter!  That was completely opposite from last year when just avoiding the worst groups was the key to outperformance.  Whether these groups can continue to perform is anyone’s guess, but often times they have a large rally off the bottom and then settle in as average performers while they work out their issues.

We are 100% sure (which you almost never hear in this business!) that some of the changes we made to the portfolios won’t work out and we will have to continue to search for leadership.  That is totally normal, and we expect that to be the case over the coming months.  If you have any questions, please don’t hesitate to call us.

Performance numbers provided are the performance of indexes that are not available for direct investment and do not include dividends or transaction costs. Past performance is not indicative of future results and there is no assurance that any forecasts mentioned in this report will be attained.  Stocks offer growth potential but are subject to market fluctuations. Dividends are not guaranteed; companies can reduce or eliminate their dividend at any time. There are special risks associated with an investment in real estate, including credit risk, interest rate fluctuations and the impact of varied economic conditions.  Technical analysis is just one form of analysis. You may also want to consider quantitative and fundamental analysis before making any investment decisions.  The information contained herein has been prepared without regard to any particular investor’s investment objectives, financial situation, and needs.  Accordingly, investors should not act on any recommendation (express or implied) or information in this material without obtaining specific advice from their financial advisors and should not rely on information herein as the primary basis for their investment decisions.  Information contained herein is based on data obtained from recognized statistical services, issuer reports or communications, or other sources believed to be reliable (“information providers”).  However, such information has not been verified by Dorsey, Wright & Associates, LLC (DWA) or the information provider and DWA and the information providers make no representations or warranties or take any responsibility as to the accuracy or completeness of any recommendation or information contained herein.  DWA and the information provider accept no liability to the recipient whatsoever whether in contract, in tort, for negligence, or otherwise for any direct, indirect, consequential, or special loss of any kind arising out of the use of this document or its contents or of the recipient relying on any such recommendation or information (except insofar as any statutory liability cannot be excluded).  Any statements nonfactual in nature constitute only current opinions, which are subject to change without notice.  Neither the information nor any opinion expressed shall constitute an offer to sell or a solicitation or an offer to buy any securities, commodities or exchange traded products.  This document does not purport to be complete description of the securities or commodities, markets or developments to which reference is made. Potential for profits is accompanied by possibility of loss.    You should consider this strategy’s investment objectives, risks, charges and expenses before investing.  The examples and information presented do not take into consideration commissions, tax implications, or other transaction costs.  The material has been prepared or is distributed solely for information purposes and is not a solicitation or an offer to buy any security or instrument or to participate in any trading strategy.

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What’s All The Fuss About

February 18, 2016

If you own last year’s laggards you are probably wondering what all the fuss over the market is about.  It has been tough sledding for the leaders so far this year as they have underperformed the laggards by quite a bit.  In one of the models we track, the laggards moved in to positive territory with yesterday’s price action!

We track a model of the S&P 500 Sub-Industry Groups that is broken into quintiles.  All of the holdings in the S&P 500 are assigned to one of the 130 GICS sub industry groups.  We then rank those sub groups using relative strength and assign to one of five quintiles.  Each month we repeat the process and equally weight all the sub groups in each quintile.  We wrote a white paper about the process a couple of years ago that can be accessed here.

So far this year the worst quintile is positive and outperforming the best quintile by over 6%.  That is quite a change from last year when the worst quintile got smashed and the other four quintiles all performed about the same.

Capture

Although this kind of performance happens from time to time, it certainly isn’t the norm.  If we look at the top quintile versus the bottom quintile over time you can see the dramatic outperformance of the best over the worst groups.Sub Industry Performance

Any strategy that buys the leaders probably isn’t doing too well this year.  This happens from time to time.  The long term results certainly favor owning the best groups and avoiding the worst groups.

The performance above is based on total return, inclusive of dividends, but does not include transaction costs.  Performance updated through 2/17/16.  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. 

Some performance information presented is the result of back-tested performance.  Back-tested performance is hypothetical and is provided for informational purposes to illustrate the effects of the strategy during a specific period. The hypothetical returns have been developed and tested by DWA, but have not been verified by any third party and are unaudited. Back-testing performance differs from actual performance because it is achieved through retroactive application of a model investment methodology designed with the benefit of hindsight. Model performance data (both backtested and live) 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.

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Q3 2015 Manager Insights

October 8, 2015

The third quarter was a difficult one for the stock market as U.S. stocks fell by -6.4% (measured by the S&P 500 Total Return Index).  The decline in equities actually began in May, so the August selloff caused the S&P 500 to fall by more than 10% from its high, which is considered to be a “correction.”  It had been nearly three years since  the market had last entered correction territory, which made it the fifth-longest correction-free streak on record.  Other markets fared even worse than U.S. stocks.  International Developed Markets stocks were down over -10%, and Emerging Markets equities fell nearly -18% during the third quarter.  Commodities were also terrible as Energy prices tumbled.  The one bright spot was Fixed Income, which managed to eke out a small gain of 1.2% (measured by the Barclay’s Aggregate Index).

The main catalyst for the selloff in the third quarter was China.  The Shanghai Composite Index was down more than -25% over the last three months.  The drop in Chinese equities was so severe that the government intervened during the summer and halted trading in some companies, as well as purchasing massive amounts of stock.  The Chinese economy has been slowing, and in August the government devalued the Yuan.  The official reason for the devaluation was to give market forces a bigger role in setting the exchange rate.  Market participants viewed the devaluation as a way for the Chinese government to prop up the ailing industrial sector.  If the government was going to such extreme measures to prop up industry then many people thought they might be in worse shape than we think.

The Federal Reserve was also in focus during the third quarter.  Everyone has been waiting for that first rate hike for what seems like forever.  It seemed like the consensus was that there would be a rate hike during the September meeting.  The Fed, however, decided to leave rates unchanged for the time being.  The issues in China were certainly part of this decision to postpone the rate increase.  Normally, investors are happy with the Fed when they don’t raise rates, but that wasn’t the case this time.  We had another selloff after the lack of a rate increase because people are worried that the global economy might be slowing too much to handle a rate increase.  If that is the case, it will be difficult to use monetary policy to kick start the economy because we are already starting from historically low interest rates.  We also think the disappointment over a lack of a rate increase is because people are simply tired of waiting for it to happen.  The Fed has telegraphed this move so far in advance it is hard to believe anyone will be caught off guard by it.  At this point, it seems like the Fed risks being the parent who constantly threatens punishment, but never follows through with the discipline.  Eventually, the kids stop listening and chaos ensues.  Looking at the big picture, a 0.25% rate increase really isn’t a big deal, and rates will still be historically low.  It seems like raising rates in the near future would signal that the Fed feels the economy is strong enough to handle it and would be welcomed by most investors.

The news hasn’t been all bad though!  High Relative Strength stocks continued to maintain their spread over the broad market on a year to date basis.  Momentum stocks generally performed in-line with the broad market over the summer, which leaves them ahead of the benchmarks for the year.  The biggest area of excess performance this year has come from avoiding the laggard stocks.  Energy and Basic Materials have performed terribly this year, and avoiding those areas has been crucial.  Owning the highest RS stocks has helped, but not as much as avoiding the losers.  That situation isn’t common, but we do see it happen from time to time.

Heading in to the fourth quarter we are optimistic that we are near the bottom of the correction.  Many of our indicators are in oversold territory where historically, we have seen significant rallies develop.  We do anticipate more volatility in the near term, but we believe the environment is setting up nicely for a rally late in the year.

Information is from sources believed to be reliable, but no guarantee is made to its accuracy.  This should not be considered a solicitation to buy or sell any security.  Past performance should not be considered indicative of future results. Potential for profits is accompanied by possibility of loss.

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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”  http://www.morningstar.com/cover/videocenter.aspx?id=716392).  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.

div

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:

div_ann

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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Q2 Manager Insights

July 8, 2015

Stocks managed small gains in the second quarter of 2015.  The S&P 500 was up slightly, leaving it up about 1.2% so far this year.  Bond yields also rose during the second quarter as investors braced for upcoming rate hikes from the Federal Reserve.  Despite the underperformance of international equities during the second quarter, they remain ahead U.S. equities on a year–to-date basis.

High relative strength equities had much choppier returns during the quarter than the broad market.  After their stellar performance during the first three months of the year we expected there might be some giveback this quarter.  Right out of the gate, high momentum equities began underperforming the broad market, and reached a relative low during the last part of April and beginning of May.  But the second half of the quarter was very good for our strategies.  We made up a lot of the performance lag during the last six weeks of the quarter.  High momentum stocks generally finished slightly behind their benchmarks for the quarter, but that still leaves them well ahead of the broad market for the year.  We were pleased to see the rebound in the types of stocks we buy given the exceptionally strong performance to begin the year.

The circus in Greece dominated the headlines at the end of the second quarter.  There is really no question whether Greece will default on their debts to their creditors – they are going to.  The big question seems to be whether Greece will accept austerity measures that will allow them to borrow even more money, and if not, whether they will abandon the Euro as their currency and return to the Drachma.  To put things in perspective, Greece has spent a good portion of its modern history in some sort of financial crisis so this is really nothing new.  What is new is that Greece doesn’t have its own currency any more so that can not be used as a tool to inflate their way out of debt.  The political and economic ramifications of any scenario in Greece are, quite frankly, impossible to determine.  There is no shortage of opinions being expressed in the media about what should happen, but nobody knows for sure.  We do know, however, that this will play out in the price action of various securities and asset classes around the globe.  It appears U.S. investors have been largely shrugging off the news so far.  If that changes, new trends will emerge and our process is designed to shift the portfolios to those emerging trends.

The economic data received during the second quarter confirmed that the economy is slowing down.  Gross Domestic Product decreased 0.2% during the first three months of the year.  There were some mixed data points that were released later in the quarter so it is really too early to tell if the slowdown in Q1 was the beginning of something bigger or just a small dip within a larger trend.  The economic data did seem to have an effect on Federal Reserve policy.  It appeared as if the Fed was looking to raise rates over the summer (as early as June), but the slowing economy seems to have delayed those plans.  The Fed minutes indicated they would like to begin rate increases some time this year, but the pace of those increases might not be as rapid as people thought.  The Fed has spent an enormous amount of time and energy (and money!) helping the economy limp along, and their statement confirmed they are not about to abandon that policy any time soon.

Overall, we were pleased that our strategies held their own during the quarter after such a strong start to the year.  We believe this leaves us well positioned to capitalize on the second half of the year.  There are definitely some news headlines that will get a lot of attention and add to the volatility over the summer.  However, it is important to remember there have been numerous “problems” that have cropped up during the entire bull run over the last 5 plus years, and nothing has been able to derail this bull market yet.

E-mail andy@dorseywright.com to request the brochure for our Systematic Relative Strength portfolios.

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Q1 Manager Insights

April 9, 2015

The stock market chopped its way to a small gain in the first three months of 2015. February was definitely the best month of the first quarter and made up for negative returns in the other two months. That pattern held true for international markets as well. Bonds also turned in a positive performance for the quarter, but, like equities, experienced some volatility along the way.

One of the biggest stories of the first quarter continued to be the decline in oil prices. As the world continues to have an oversupply of oil, it is a double-edged sword as far as the markets are concerned. Obviously, weak oil prices are not good for the Energy sector which has been one of the worst performing areas lately, and we are seeing a major slowdown in capital spending for new and existing energy projects. On the other hand, lower energy prices are very good for consumers. Many of you have probably noticed much lower gasoline prices. I just drove by a gas station with gas under $3.00 per gallon, which is unheard of in our area of Southern California. Lower energy prices give consumers more spending power, and they seem to be taking advantage of it. Our strategies have recognized this and have allocated more toward areas benefitted by increased consumer spending while underweighting energy companies. It will be interesting to see how this dynamic plays out, but for now it appears the consumer is the biggest beneficiary of the oil price decline.

The big news overseas was the European Central Bank announcement of a bond buying program that could top $1 trillion. That sent the euro into a tailspin, but also helped buoy the European equity markets. In contrast to last year when international markets dramatically underperformed U.S. markets, international markets performed much better during the first three months of the year. Most of the performance came from Developed Markets (reflecting the strength in Europe). Emerging Markets performed better than U.S. markets, but didn’t perform as well as Developed Markets.

Investors are also becoming more concerned with when the Federal Reserve is finally going to raise rates. That possibility was once way off in the distance, but now it appears it may come later this year. Even if the Fed does raise rates later this year, interest rates will still be low by historical standards so the move will be mostly symbolic. It will also be symbolic of a move toward a more “normal” market with much less government intervention. This would be a welcome sign for our strategies. Our decision-making process is primarily focused on market price movements. Less government intervention should mean fewer price shocks and less correlation among stocks and sectors than during the risk on, risk off environment of the last few years.

Our relative strength strategies performed well across the board in the first quarter. We track a Relative Strength Spread, which measures the performance of a basket of strong momentum stocks versus a basket of weak momentum stocks. That measure exploded higher during the first quarter. The spread has been moving sideways for a couple of years, so seeing it begin to move upward again is a very good sign. The strong momentum performance was fairly broad- based, and wasn’t the result of a few lucky stocks. We track a number of strategies that mix momentum with value, dividends, or some other factor. In each case, the strategy with the momentum overlay was superior to the other factor by itself this quarter. The momentum move was very broad based and appeared in numerous segments of the market.

As the bull market continues to mature we are expecting the leadership to narrow. Valuations are up and it is getting more difficult to find bargains. When investors can’t find enough value they often turn to companies that can continue to grow earnings despite a slowing economy. This is usually a very healthy period for momentum strategies. We think we are entering that phase of the cycle and are quite optimistic for what that will mean for our strategies for the rest of 2015.

Information is from sources believed to be reliable, but no guarantee is made to its accuracy.  This should not be considered a solicitation to buy or sell any security.  The relative strength (momentum) strategy is not a guarantee.   There may be times when all investments and strategies are unfavorable and depreciate in value.  Past performance should not be considered indicative of future results.  Potential for profits is accompanied by possibility of loss.

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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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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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What Are We Really Trying To Accomplish Here?

February 28, 2014

March 1st is the seventh anniversary of our Technical Leaders Index launching on the NYSE.  The last seven years have seen some crazy markets!  Through it all we have been really happy at how the index has adapted to the different market environments we have had.

We often get caught up in the day-to-day gyrations of the market and we forget to take a step back and look at what a strategy is designed to accomplish.  The Technical Leaders Index is designed to keep the index invested in high momentum stocks.  It is a process that is supposed to cut the underperforming stocks out and ride the winners as long as they continue to outperform.  That is how most successful momentum and trend-following strategies work.

With that in mind, I thought it would be interesting to show everyone what would be coming out of the index and what would be going in if we rebalanced it today.  Remember, the process is designed to cut out the stocks that aren’t performing well and to buy stocks that are performing better than what we are selling.  Here are the stocks we would be selling (I have taken the names off the charts for compliance reasons, but the actual names of the stocks don’t really matter anyways):

(Click To Enlarge)

And here is what we would be buying:

(Click To Enlarge)

Pretty remarkable difference, right?  The performance over the last few months is quite different for the two groups of stocks.  Over time that is what the Technical Leaders process does.  It constantly replaces weak stocks with stronger ones.

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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:

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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 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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Momentum in Rising Rate Environments

November 19, 2013

The latest PowerShares Connection report is out. There is a nice writeup about the PowerShares DWA Small Cap Momentum ETF and what happens to high momentum securities during rising rate environments. You can view the report here.

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The Coach Who Never Punts

November 14, 2013

Have you ever been to a football game and never seen a punt?  Yeah, me neither.  You would probably think that coach was crazy.  I would have thought so too, but the numbers say otherwise.

It seems like most of the comparisons between advanced statistical metrics in sports and investing have revolved around baseball.  This is the first example I have seen of a football coach really thinking outside of the box to give his team a statistical advantage every game.  Sure, football coaches have used statistics to game plan and find tendencies, but what this coach is doing goes way beyond that.

How does this relate to investing?  This coach has found an edge and relentless exploits the edge no matter what the cost.  He knows that statistically he is better off never giving the ball to the other team.  He never punts the ball to them.  When he kicks off, it is always an onside kick.  If the other team wants the ball they have to earn it.  He readily admits they only have a 50% fourth down conversion rate so it isn’t like this is some sort of offensive juggernaut that can never be stopped.  This coach is wrong a lot.  He no doubt looks like a fool quite often.  But he has done the math and knows his methods give him a clear statistical advantage to win games over time.  It might not work on any given play, series, quarter, or half.  Winning investment strategies don’t work every day, week, quarter, or even every year.  But over time they do, and the only thing preventing you from realizing those gains buckling under the pressure and failing to execute the strategy. The edges are small, but they add up over time.

 

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