Podcast: PnF RS Key Takeaways

September 15, 2014

Podcast: PnF RS Key Takeaways

Andy Hyer & Chris Moyer discuss key takeaways from the following white papers by John Lewis:

PnF Relative Strength Signals

PnF RS Box Sizes

Implementing PnF RS Signals

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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.

Random zps69d808c9 Point and Figure RS Signal Implementation

 (Click To Enlarge)

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

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

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Diversification by Style Box or by Risk Factor?

August 11, 2014

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

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

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

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

correlations1 Diversification by Style Box or by Risk Factor?

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

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

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

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Point and Figure Relative Strength Box Sizes

July 25, 2014

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

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

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

box sizes Point and Figure Relative Strength Box Sizes

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

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

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

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Unrealistic Paradigms

July 21, 2014

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

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

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

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

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

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

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

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

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

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

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

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

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The Virtues of Pragmatism

July 10, 2014

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

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

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

ii19S2zDTQng The Virtues of Pragmatism

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

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

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

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

SP 500 The Virtues of Pragmatism

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

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

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

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PnF Relative Strength Signals White Paper

June 15, 2014

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

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

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

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

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

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

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Rolling 10-Year Momentum Returns

February 26, 2014

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

momentum 02.26.14 Rolling 10 Year Momentum Returns

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

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

momentum2 02.26.14 Rolling 10 Year Momentum Returns

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

Past performance is no guarantee of future returns.

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It’s All At The Upper End

February 3, 2014

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

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

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

GICSMatrix zpse4a88b8f Its All At The Upper End

 (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:

GICS12Mth zpsb3fb152f Its All At The Upper End

 (click on image to enlarge)

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

GICS6Mth zps8af7edf9 Its All At The Upper End

(click on image to enlarge)

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

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Improving Sector Rotation With Momentum Indexes

January 21, 2014

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

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

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

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

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

Capture zps07daf1e3 Improving Sector Rotation With Momentum Indexes

 (Click Image To Enlarge)

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

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

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

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DWA Technical Leaders Webinar: Q1 Updates

January 20, 2014

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

Follow this link for a replay of this webinar.

bw012114  DWA Technical Leaders Webinar: Q1 Updates

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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:

TLTable zps9d3df2ae DWA Technical Leaders Index Trade Profiles

 

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:

TLChart zps7c20d6fe DWA Technical Leaders Index Trade Profiles

 

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

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

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

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60/40 Portfolio Subjected To Historical Data

December 30, 2013

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

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

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

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

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

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

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

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

From John Lewis’ white paper:

factor summary1 60/40 Portfolio Subjected To Historical Data

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

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

dwtfx1 60/40 Portfolio Subjected To Historical Data

Source: Morningstar

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

Past performance is no guarantee of future returns.  

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More Supporting Data For Momentum

December 30, 2013

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

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

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

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

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Podcast: A Profile of our Growth Portfolio

November 25, 2013

A Profile of our Growth Portfolio

Mike Moody and Andy Hyer

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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.

PSConnection zps6dc00387 Momentum in Rising Rate Environments

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Momentum and Dividends in Rising Rate Environments

October 14, 2013

In a low-interest rate environment, investors have naturally turned their attention to stocks paying high dividends as a way to generate income.  Momentum, as a return factor, has not been in the spotlight.  However, as interest rates have moved higher from their lows of last summer (On October 10, 2013 the 10-year US Treasury yield was 2.71% compared to 1.43% on July 25, 2012.), you might wonder how high dividend paying stocks tend to perform in rising rate environments over time.  A current trend chart of the 10-year U.S. Treasury Yield Index, shows that yields are trending higher.

10YR Yield1 Momentum and Dividends in Rising Rate Environments

Source: Dorsey Wright

A longer-term chart of the 10-year US Treasury Yield Index is shown below:

10 Year Treasury Rates Momentum and Dividends in Rising Rate Environments

Jim O’Shaughnessy’s What Works On Wall Street says this about high-yielders:

The high-yielders from Large Stocks do best in market environments in which value is outperforming growth, winning 74 percent of the time.  They also do well in markets in which bonds are outperforming stocks, winning 65 percent of the time in those environments.

O’Shaughnessy’s book lays out the performance of portfolios formed by a number of return factors since the 1920s.  His book includes the performance of portfolios formed by market capitalization, price-to-earnings ratios, EBITDA, price-to-cash flow ratios, price-to-sales ratios, price-to book ratios, dividend yields, relative strength (momentum), and many other factors.

In the rising interest rate environment of the 1960s and 1970s, O’Shaughessy shows the performance for the portfolio of the highest yielders as follows:

comparison Momentum and Dividends in Rising Rate Environments

Source: What Works On Wall Street

Not bad—the dividend-focused portfolio was still able to generate modest outperformance.  However, a portfolio formed by price momentum was clearly able to generate much higher returns in a rising rate environment.  While this may not be the best environment for portfolios of high dividend payers to really stand out, investors may find that momentum can excel in rising-rate periods.   

Past performance is no guarantee of future returns.

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Long-Only Momentum

October 4, 2013

Gary Antonacci has a very nice article at Optimal Momentum regarding long-only momentum.  Most academic studies look at long-short momentum, while most practitioners (like us) use long-only momentum (also known as relative strength).  Partly this is because it is somewhat impractical to short across hundreds of managed accounts, and partly because clients don’t usually want to have short positions.  The article has another good reason, quoting from an Israel & Moskowitz paper:

Using data over the last 86 years in the U.S. stock market (from 1926 to 2011) and over the last four decades in international stockmarkets and other asset classes (from 1972 to 2011), we find that the importance of shorting is inconsequential for all strategies when looking at raw returns. For an investor who cares only about raw returns, the return premia to size, value, and momentum are dominated by the contribution from long positions.

In other words, most of your return comes from the long positions anyway.

The Israel & Moskowitz paper looks at raw long-only returns from capitalization, value, and momentum.  Perhaps even more importantly, at least for the Modern Portfolio Theory crowd, it looks at CAPM alphas from these same segments on a long-only basis.  The CAPM alpha, in theory, is the amount of excess return available after adjusting for each factor.  Here’s the chart:

long onlymomentum zps14b7ad7e Long Only Momentum

Source: Optimal Momentum

(click on image to enlarge)

From the Antonacci article, here’s what you are looking at and the results:

I&M charts and tables show the top 30% of long-only momentum US stocks from 1927 through 2011 based on the past 12-month return skipping the most recent month. They also show the top 30% of value stocks using the standard book-to-market equity ratio, BE/ME, and the smallest 30% of US stocks based on market capitalization.

Long-only momentum produces an annual information ratio almost three times larger than value or size. Long-only versions of size, value, and momentum produce positive alphas, but those of size and value are statistically weak and only exist in the second half of the data. Momentum delivers significant abnormal performance relative to the market and does so consistently across all the data.

Looking at market alphas across decile spreads in the table above, there are no significant abnormal returns for size or value decile spreads over the entire 1926 to 2011 time period. Alphas for momentum decile portfolio spread returns, on the other hand, are statistically and economically large.

Mind-boggling right?  On a long-only basis, momentum smokes both value and capitalization!

Israel & Moskowitz’s article is also quoted in the post, and here is what they say about their results:

Looking at these finer time slices, there is no significant size premium in any sub period after adjusting for the market. The value premium is positive in every sub period but is only statistically significant at the 5% level in one of the four 20-year periods, from 1970 to 1989. The momentum premium, however, is positive and statistically significant in every sub period, producing reliable alphas that range from 8.9 to 10.3% per year over the four sub periods.

Looking across different sized firms, we find that the momentum premium is present and stable across all size groups—there is little evidence that momentum is substantially stronger among small cap stocks over the entire 86-year U.S. sample period. The value premium, on the other hand, is largely concentrated only among small stocks and is insignificant among the largest two quintiles of stocks (largest 40% of NYSE stocks). Our smallest size groupings of stocks contain mostly micro-cap stocks that may be difficult to trade and implement in a real-world portfolio. The smallest two groupings of stocks contain firms that are much smaller than firms in the Russell 2000 universe.

What is this saying?  Well, the value premium doesn’t appear to exist in the biggest NYSE stocks (the stuff your firm’s research covers).  You can find value in micro-caps, but the effect is still not very significant relative to momentum in long-only portfolios.  And momentum works across all cap levels, not just in the small cap area.

All of this is quite important if you are running long-only portfolios for clients, which is what most of the industry does.  Relative strength (momentum) is a practical tool because it appears to generate excess return over many time periods and across all capitalizations.

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Momentum Coming of Age

September 27, 2013

We’ve come a long way from the days of  ”Wall Street is a random walk, and past price movements tell you nothing about the future,” as advocated by Efficient Market Hypothesis (EMH) proponents Burton Malkiel and Eugene Fama in the 1960s and 1970s.  While practioners have been using relative strength strategies since at least the 1930s (Richard Wyckoff, H.M. Gartley, Robert Levy, George Chestnut, and many others), most academics stayed fairly loyal to the EMH until the early 1990s. Wesley Gray, PhD, of Turnkey Analyst, summarizes how academic studies on momentum in the early 1990s started to turn the tide in a academic community:

In the early 90s academics (e.g., Jagadeesh and Titman (1993) began to focus on the concept of “momentum,” which refers to the fact that, contrary to the EMH, past returns can predict future returns, via a trend effect. That is, if a stock has performed well in the recent past, it will continue to perform well in the future. EMH proponents were perplexed, but argued that momentum returns were likely related to additional risks borne: riskier smaller and cheaper companies drove the effect. Many researchers have responded with studies that find the effect persists even when controlling for company size and value factors. And the effect appears to hold across multiple asset classes, such as commodities, currencies and even bonds. (e.g., Check out Chris Geczy’s “World’s Longest Backtest”).

momentum Momentum Coming of Age

An EMH advocate reviews the momentum data

It seems that much of the research on momentum today is moving beyond the initial question of “Is it possible that momentum really does work?” to trying to better understand why it works.

Again, from Wesley Gray:

In short, it appears the evidence for momentum is only growing stronger (Gary Antonacci has some great research on the subject: http://optimalmomentum.blogspot.com/). Today researchers are going even farther by applying behavioral finance concepts in order to understand psychological factors that drive the momentum effect.

In “Demystifying Managed Futures,” by Brian Hurst, Yao Hua Ooi, and Lasse Heje Pedersen, the authors argue that the returns for even the largest and most successful Managed Futures Funds and CTAs can be attributed to momentum strategies.  They also discuss a model for the lifecycle of a trend, and then draw on behavioral psychology to hypothesize the cognitive mechanisms that drive the underlying momentum effect. Below is a graph of a typical trend:

trend Momentum Coming of Age

Note that there are several distinct components to the trend: 1) initial under-reaction, when market price is below fundamental value, 2) over-reaction, as the market price exceeds fundamental value, and 3) the end of the trend, when the price converges with fundamental value. There are several behavioral biases that may systematically contribute to these components.

Under-reaction phase:

Adjustment and Anchoring.  This occurs when we consider a value for a quantity before estimating that quantity. Consider the following 2 questions posed by Kahneman: Was Gandhi more or less than 144 years old when he died? How old was Gandhi when he died? Your guess was affected by the suggestion of his advanced age, which led you to anchor on it and then insufficiently adjust from that starting point, similar to how people under-react to news about a security. (also, Gandhi died at 79)

The disposition effect.  This is the tendency of investors to sell their winners too early and hold onto losers too long. Selling early creates selling pressure on a long in the under-reaction phase, and reduces selling pressure on a short in the under-reaction phase, thus delaying the price discovery process in both cases.

Over-reaction phase:

Feedback trading and the herd effect.  Traders follow positive feedback strategies. For instance, George Soros has described his concept of “reflexivity,” which involves buying in anticipation of further buying by uninformed investors in a self-reinforcing process.  Additionally, herding can be a defense mechanism occurring when an animal reduces its risk of being eaten by a predator by staying with the crowd. As Charles MacKay put it in 1841 in his book, Extraordinary Popular Delusions and Madness of Crowds, “Men, it has been well said, think in herds; it will be seen that they go mad in herds, while they only recover their senses slowly, and one by one.”

Gray concludes:

The growing academic body of work supporting the existence of the momentum effect, along with a sensible psychological framework that explains it, are a potent combination. Indeed, momentum may have come of age as an investment tool, as more and more investors incorporate it into their portfolios.

Dorsey Wright has been refining work on relative strength/momentum for decades and the recent milestone of the PowerShares DWA Momentum ETFs (PDP, PIZ, PIE, and DWAS) passing $2 billion in assets is further confirmation that “momentum is coming of age.”  Furthermore, users of the Dorsey Wright research have a great number of relative strength-based tools (RS Matrix, Favored Sector, Dynamic Asset Level Investing, Technical Attributes…) at their fingertips to be able to customize relative strength-based strategies for their clients.  It’s been a long time coming, and acceptance of momentum still has a long way to go, but it is encouraging to see this factor begin to get the recognition that I believe it is due.

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The Case for Systematic Decision-Making

September 25, 2013

From Wes Gray comes an excellent video about expert decision making versus model-based decision making.  Well worth the 17 minutes.

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From the Archives: Mebane Faber’s New White Paper on Relative Strength

September 17, 2013

Mebane Faber recently released a nice white paper, Relative Strength Strategies for Investing, in which he tested relative strength models consisting of US equity sectors from 1926-2009.  He also tested relative strength models consisting of global assets like foreign stocks, domestic stocks, bonds, real estate, and commodities from 1973-2009. The relative strength measures that he used for the studies are publicly-known methods based on trailing returns. Some noteworthy conclusions from the paper:

  • Relative strength models outperformed buy-and-hold in roughly 70% of all years
  • Approximately 300-600 basis points of outperformance per year was achieved
  • His relative strength models outperformed in each of the 8 decades studied

I always enjoy reading white papers on relative strength.  It is important to mention that the methods of calculating relative strength that were used in Faber’s white paper are publicly-known and have been pointed to for decades by various academics and practitioners. Yet, they continue to work!  Those that argue that relative strength strategies will eventually become so popular that they will cease to work have some explaining to do.

—-this article originally appeared 4/20/2010.  Of course, the white paper is no longer new at this point, but it is a reminder of the durability of relative strength as a return factor.  Every investing method goes through periods of favor and disfavor.  Investors are, unfortunately, likely to abandon even profitable methods at the worst possible time.  This paper is a good reminder that return factors are durable, but patience may be required to harvest those returns.  Most often, the investor that sticks to it will be rewarded.

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Relative Strength Environments

August 9, 2013

What market environments are best for relative strength strategies?  Our partners at Arrow Funds recently completed a nice research piece that addresses that question.  The essential factors are correlations and dispersions (both of which are looking better for relative strength by the way).  Click below to read the report.

RS Environments Relative Strength Environments

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Factor Performance and Factor Failure

July 30, 2013

Advisor Perspectives recently carried an article by Michael Nairne of Tacita Capital about factor investing.  The article discussed a number of aspects of factor investing, including factor performance and periods of factor underperformance (factor failure).  The remarkable thing about relative strength (termed momentum in his article) is the nice combination of strong performance and relatively short periods of underperformance that it affords the investor seeking alpha.

Mr. Nairne discusses a variety of factors that have been shown to generate excess returns over time.  He includes a chart showing their performance versus the broad market.

factorperformance zps037b7505 Factor Performance and Factor Failure

Source: Advisor Perspectives/Tacita Capital  (click on image to enlarge)

Yep, the one at the top is momentum.

All factors, even very successful ones, underperform from time to time.  In fact, the author points out that these periods of underperformance might even contribute to their factor returns.

No one can guarantee that the return premia originating from these dimensions of the market will persist in the future. But, the enduring nature of the underlying causes – cognitive biases hardwired into the human psyche, the impact of social influences and incremental risk – suggests that higher expected returns should be available from these factor-based strategies.

There is another reason to believe that these strategies offer the prospect of future return premia for patient, long-term investors. These premia are very volatile and can disappear or go negative for many years. The chart on the following page highlights the percentage of 36-month rolling periods where  the factor-based portfolios – high quality, momentum, small cap, small cap value and value – underperformed the broad market.

To many investors, three years of under-performance is almost an eternity. Yet, these factor portfolios underperformed the broad market anywhere from almost 15% to over 50% of the 36-month periods from 1982 to 2012. If one were to include the higher transaction costs of the factor-based portfolios due to their higher turnover, the incidence of underperformance would be more  frequent. One of the reasons that these premia will likely persist is that many  investors are simply not patient enough to stay invested to earn them.

The bold is mine, but I think Mr. Nairne has a good point.  Many investors seem to believe in magic and want their portfolio to significantly outperform—all the time.

That’s just not going to happen with any factor.  Not surprisingly, though, momentum has tended to have shorter stretches of underperformance than many other factors, a consideration that might have been partially responsible for its good performance over time.  Mr. Nairne’s excellent graphic on periods of factor failure is reproduced below.

factorfailure zps55a7cd1c Factor Performance and Factor Failure

Source: Advisor Perspectives/Tacita Capital (click on image to enlarge)

Once again, whether you choose to try to harvest returns from relative strength or from one of the other factors, patience is an underrated component of actually receiving those returns.  The market can be a discouraging place, but in order to reap good factor performance you have to stay with it during the inevitable periods of factor failure.

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Relative Strength Spread

July 23, 2013

The chart below is the spread between the relative strength leaders and relative strength laggards (universe of mid and large cap stocks).  When the chart is rising, relative strength leaders are performing better than relative strength laggards.    As of 7/22/2013:

Capture5 Relative Strength Spread

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212 Years of Price Momentum

July 23, 2013

Christopher Geczy, University of Pennsylvania, and Mikhail Samonov, Octoquant.com, recently published the world’s longest backtest on momentum (1801 – 2012).  This is a truly fascinating white paper.  So much of the testing on momentum has been done on the CRSP database of U.S. Securities which goes back to 1926.  Now, we can get a much longer-term perspective on the performance of momentum investing.

Abstract:

We assemble a dataset of U.S. security prices between 1801 and 1926, and create an out-of-sample test of the price momentum strategy, discovered in the post-1927 data. The pre-1927 momentum profits remain positive and statistically significant. Additional time series data strengthens the evidence that momentum is dynamically exposed to market beta, conditional on the sign and duration of the tailing market state. In the beginning of each market state, momentum’s beta is opposite from the new market direction, generating a negative contribution to momentum profits around market turning points. A dynamically hedged momentum strategy significantly outperforms the un-hedged strategy.

Yep, momentum worked then too!  As pointed out in the white paper, those looking for a return factor that outperforms every single year will not find it with price momentum (or any other factor), but momentum has a long track record of generating excess returns.

So much for the theory that ideas in investing tend to streak, get overinvested, then die.  Some, like momentum (or value), go in an out of favor, but they have generated very robust returns over long periods of time.

HT: Abnormal Returns and Turnkey Analyst

Past performance is no guarantee of future returns.

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