An Allocation, Not A Trade

February 16, 2016

Corey Hoffstein makes a compelling argument for why active strategies should be treated as an allocation, not a trade:

Many studies have shown that there are numerous characteristics that can deliver superior risk-adjusted returns over time.  The most popular include value, size, momentum, trend-following, quality, low-volatility and high yield.

Most active managers tend to align their portfolios one, or several, of these factors.

None of these characteristics, however, out-performs in all markets.  In the short-run, their relative out-performance to the market can vary considerably.

A value tilt, for example, has historically delivered an average 2-year return premium of 547 basis points (“bp”) over the broad US equity market.  In the short run, it has seen periods ranging between -4046bp and +5753bp.


Source: Kenneth French data library.  Analysis by Newfound Research.  

As we’ve said before, investors do not experience “average.”  They experience under-performing the market by -40% over two years before they experience out-performing the market by 57%, assuming they did not sell and go to a different manager.

Further on in his article, he looks at some popular factor tilt premiums over the last 20 years (1995-2015) and shows that they went through significant and prolonged drawdowns relative to the S&P 500.

Can we blame investors who gave up on a value tilt after under-performing the market by 31.90%?  That relative drawdown, from peak-to-recovery, took 8 years.


None of the factor tilts went unscathed and yet they all were able to generate excess returns over this 20-year period of time.  As proponents of momentum investing, it is worth noting that momentum had the highest annualized premium of any of those shown in the article.

Investing is hard.  I fully understand the argument put forth by John Bogle and others that investors should just buy a cap-weighted index and forget about trying to outperform the market.  For many investors, that is probably the right answer.  However, research suggests that excess returns are available and Hoffstein’s study provides some important clues about asset allocation best practices.  First, do your homework to know what factor tilts have historically generated outpeformance.  Second, do sufficient due diligence to come to a conclusion about whether or not those factor premiums are likely to continue.  Third, allocate across a number of factor tilts, preferably among several that have a relatively low correlation to one another.  Finally, don’t trade in an out of those active factor tilts unless you have a crystal ball.

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

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Simplicity is the Ultimate Sophistication

October 16, 2015

Great insight from Ben Carlson:

Running a very complex, unique style of portfolio management might impress the boosters, but it’s terrible from a continuity standpoint. You give yourself the potential to hit a home run, but the risk of striking out is magnified.

The more complex you make a portfolio in terms of different investment structures and strategies, the harder it becomes to maintain a consistent approach over time. It’s an operationally inefficient way to invest and requires serious manpower, time and resources to pull it off. Very few organizations can thread the needle in this way. And even if you have all of those things in place there’s no guarantee, except for the fact that you’ll be paying much higher fees. Increasing the number of fund types you implement only increases the operational risk, due diligence costs and monitoring problems.

It doesn’t get much more simple (or effective I would argue) than momentum.  As da Vinci said: “Simplicity is the ultimate sophistication.”

HT: Abnormal Returns

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When Theory Meets Reality

October 14, 2015

Ben Carlson’s concise evaluation of mean-variance optimization:

One of the students asked for my thoughts on the efficient frontier and mean-variance optimization. I told them that the general idea behind these theories has been very helpful to the portfolio management industry in a number of ways. Diversification and the idea that adding together investments that behave differently in a portfolio is an important concept.

But you can’t take these types of models literally. Correlations and market relationships are constantly changing. Nothing is stable and the past isn’t a perfect window into what’s going to happen in the future. The efficient frontier shows you the best risk-adjusted returns from a historical data set. It can’t tell you what the perfect asset allocation will be in the future.

Models and textbook theories can play a role in building your knowledge base, but they never tell the whole story. Many people make the mistake of taking them at face value without thinking through the real world implications. No model is perfect, so the majority of the time what really matters is the interpretation by the end user.

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Market Timing with CAPE

March 5, 2015

How worried should you be when you hear someone say that the market is overvalued?

Wesley Gray at Alpha Architect takes a deep dive into the topic of timing the market using CAPE, Shiller’s Cyclically Adjusted PE ratio in a recent post.  His results should give pause to even he most strident market timing fundamentalist.  He also looked at market timing using simple trend following (trend following performed better!).  His methods are described below (study covered the period 1/1/1947 – 1/31/2015):

To create our “valuation-timing” indicator, every month we identify the 99 percentile valuations using rolling 5-, 10-, and 20-year look-back periods. Our trading rule is simple: if the current market valuation is greater or equal to the 99 percentile measure, we invest in the risk-free rate (short-term treasury bills), otherwise, we stay invested.

We compare the valuation-timing indicator to a monthly-assessed simple moving-average (MA) trading rule, and a buy-and-hold strategy. The buy-and-hold strategy is straightforward, and the MA indicator is simple: if the current market price is lower than the 12 month moving average, we invest in the risk-free rate (short-term treasury bills), otherwise, we stay invested.

Our conclusion is counterintuitive, but not entirely surprising:

Strategy Legend:
  • SP500 = S&P 500 Total Return Index
  • LTR = The Merrill Lynch 10-year U.S. Treasury Futures Total Return Index
  • Rolling 5 year 99perc CAPE= Timing signal uses the 99th percentile valuation metric using rolling 5 year look-back periods.
  • Rolling 10 year 99perc CAPE = Timing signal uses the 99th percentile valuation metric using rolling 10 year look-back periods.
  • Rolling 20 year 99perc CAPE= Timing signal uses the 99th percentile valuation metric using rolling 20 year look-back periods.
  • (1,12) MA= If last month’s price is above the past 12 month average, invest in the S&P 500; otherwise, buy U.S. Treasury Bills (RF).

alpha architect

The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request.


There is no evidence to support the use of “valuation-timing,” which performs similarly to buy-and-hold strategies (after costs it would we much worse). There is nothing magical about the 99th percentile. Trend-following, at least historically, seems to more effective.

Perhaps there are more convoluted, complex, and data-optimized ways in which we can leverage overall market valuations to help us time markets. We haven’t found any, but that doesn’t mean they don’t exist. Please share.

Pretty shocking results.  If you can’t even successfully identify overvalued markets when a market is in the 99th valuation percentile, then why even pay any attention to valuation measures at all?  If someone wants to be bearish, there is always some seemingly plausible reason and CAPE valuation measures are an often-cited reason.  However, Gray’s study is a solid takedown of the idea that CAPE can be effectively used as a way to get out of the market at the right time.  As pointed out in his study, a simple trend following method worked better.  Perhaps, an even more promising approach is a relative strength-driven Tactical Asset Allocation strategy as is detailed in this white paper by our own John Lewis.

I agree with a recent tweet by Cullen Roche:

FYI, no one knows how “expensive” the market really is or how “expensive” it really should be.

A reality that investors would do well to embrace.

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

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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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Can You See the Future?

August 13, 2014

A key excerpt from Andrew Ang’s book Asset Management: A Systematic Approach to Factor Investing:

Investors must use past data to estimate inputs for optimization problems.  But many investors simply take historical averages on short, rolling samples.  This is the worst thing you can do.

In drawing all of the mean-variance frontiers for the G5, or various subsets of countries, I used historical data.  I plead guilty.  I did, however, use a fairly long sample, from January 1970 to December 2011.  Nevertheless, even this approximately forty-year sample is relatively short.  You should view the figures in this chapter as what has transpired over the last forty years and not as pictures of what will happen in the future.  As the investment companies like to say in small print, past performance is no guarantee of future returns.  The inputs required for mean-variance investing—expected returns, volatilities, and correlations—are statements about what we think will happen in the future.

Using short data samples to produce estimates for mean-variance inputs is very dangerous.  It leads to pro-cyclicality.  When past returns have been high, current prices are high.  But current prices are high because future returns tend to be low.  Thus, using a past data sample to estimate a mean produces a high estimate right when future returns are likely to be low.  These problems are compounded when more recent data are weighted more heavily, which occurs in techniques like exponential smoothing.

Mean-variance optimization (a darling of financial theory) works just fine when you can accurately predict returns, volatilities, and correlations.  But, if you can’t (or should I say when you can’t), mean-variance optimization is useless.  As Ang points out, simply taking historical averages “is the worst thing you can do.”  Alas, coming up with the optimal asset allocation that will allow us to create wonderful returns with a small amount of volatility is comforting to wish for, but real life is much messier than the theory.

Trend following may, admittedly,  be somewhat simple (although as John can attest, the computer programming required isn’t for simpletons).  But, the only question that ultimately matters is does it work.  Click here and here for some material to help answer that question.

Past performance is no guarantee of future returns.  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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Adaptive Asset Allocation

May 14, 2014

Vitaliy Katsenelson in Institutional Investor doesn’t mince words when it comes to Modern Portfolio Theory (MPT):

Teachers will teach what is teachable; they’ll default to solving a mathematical equations (while stuffing it with arbitrary numbers for the most part), because that is what they know how to do.  They can learn MPT by reading their predecessors’ textbooks, and therefore that is what they’ll teach, too.  The beauty of MPT, at least from a teaching perspective, is that it turns investing into a math problem, with elegant equations that always spit out precise, albeit random numbers.

But please don’t tell anyone I said this, because as an investor I’d love for MPT to be taught starting in kindergarten.  It would make my job easier: I’d be competing against imbeciles who still believe the world is flat.  However, as a well-wishing person dispensing advice, I’d say, spend as little time as you can studying MPT.

Among the more dubious assumption in MPT are that correlations between assets are fixed and constant forever and that the volatility of an asset is known in advance and is also constant.  Yea, about that…  See below for a chart that is a couple years old, but the point should be pretty clear—correlations change!


Source: Rex Macey, Investments & Wealth Monitor

The five-year correlation between domestic large stocks (Russell 1000) and the MSCI EAFE index varied but never exceeded 0.6 from the start of the dataset until the late 1990s.  Consultants used this data to argue for international diversification.  Who would have expected based on historical data that the correlation would rise to the 0.9 level matching the correlation of large U.S. stocks with small U.S. stocks?  I suspect those relying on international diversification were quite disappointed.

It has been estimated that there is some $7 trillion invested in accordance with the tenets of MPT, so this is far from being just an academic exercise.

So, what’s the alternative?  How about Tactical Asset Allocation for one.  Rather than relying on an approach to asset allocation that makes assumptions about how the future should look, why not embrace a tactical approach to asset allocation that is designed to adapt?  Correlations can change, variances can change, and returns can change and tactical asset allocation still has the potential to produce favorable returns over time.

Click here to read an FAQ on our Global Macro strategy, which provides a truly tactical alternative to MPT.

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The Japan Example

April 21, 2014

Burton Malkiel suggests that “Smart Beta” may be just a fad and that capitalization-weighted indexes “will give the investor the most prudent trade-off between risk and return available.”  I wonder how he would respond to the “Japan example.”  Japan, at its 1989 peak, made up 60% of the MSCI EAFE index, as shown in the chart below.



The chart above is only updated through 1998.  However, the weighting of Japan in the MSCI EAFE is still around 20 percent.  Japan’s stock market performance since 1989 has been ugly:

Nikkei Index

Source: Yahoo! Finance.  Returns include dividends, but do not include any transaction costs.  Investors cannot invest directly in an index.  Jan. 1984 – Mar 2014.

Japan is still 62% below its 1989 peak!  Some might take issue with the argument that investing in a capitalization-weighted index like the MSCI EAFE has given you the “most prudent trade-off between risk and return available.”  Weighting an index by capitalization has its strengths and its weaknesses.  “Smart Beta” indexes that build an index around different investment factors also have their strengths and their weaknesses.  Our area of expertise–building momentum indexes–is supported by a large body of work and we believe will offer superior returns to capitalization-weighted indexes over time.  Ultimately, I don’t think it is helpful to investors to try to position anything other than a capitalization-weighted index in a negative light.  Investors have benefited greatly from innovation over time, and “Smart Beta” is just another step in giving investors what we believe to be better tools to help them achieve their financial goals.

Dorsey Wright provides an alternative way to get exposure to developed international markets with the PowerShares DWA Developed Markets ETF (PIZ).

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. Potential for profits is accompanied by possibility of loss. Dorsey Wright & Associates is the index provider for the suite of Momentum ETFs with PowerShares.  See for more information.

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Quote of the Week

April 11, 2014

Mark Abraham, of Abraham Trading:

While a fundamental analyst may be able to properly evaluate the economics underlying a stock, I do not believe they can predict how the masses will process this same information. Ultimately, it is the dollar-weighted collective opinion of all market participants that determines whether a stock goes up or down. This consensus is revealed by analyzing price.

HT: Michael Covel

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The Wisdom of Crowds

April 9, 2014

NPR, reporting on “The Good Judgement Project” writes a fascinating story about the wisdom of crowds.  Maybe there is something to this momentum factor after all!  

For the past three years, Rich and 3,000 other average people have been quietly making probability estimates about everything from Venezuelan gas subsidies to North Korean politics as part of the Good Judgment Project, an experiment put together by three well-known psychologists and some people inside the intelligence community.

According to one report, the predictions made by the Good Judgment Project are often better even than intelligence analysts with access to classified information, and many of the people involved in the project have been astonished by its success at making accurate predictions…

…How can that be?

“Everyone has been surprised by these outcomes,” said Philip Tetlock, one of the three psychologists who came up with the idea for the Good Judgment Project. The other two are Barbara Mellers and Don Moore.

For most of his professional career, Tetlock studied the problems associated with expert decision making. His book Expert Political Judgment is considered a classic, and almost everyone in the business of thinking about judgment speaks of it with unqualified awe.

All of his studies brought Tetlock to at least two important conclusions.

First, if you want people to get better at making predictions, you need to keep score of how accurate their predictions turn out to be, so they have concrete feedback.

But also, if you take a large crowd of different people with access to different information and pool their predictions, you will be in much better shape than if you rely on a single very smart person, or even a small group of very smart people.

“The wisdom of crowds is a very important part of this project, and it’s an important driver of accuracy,” Tetlock said.

The wisdom of crowds is a concept first discovered by the British statistician Francis Galton in 1906.

Galton was at a fair where about 800 people had tried to guess the weight of a dead ox in a competition. After the prize was awarded, Galton collected all the guesses so he could figure out how far off the mark the average guess was.

It turned out that most of the guesses were really bad — way too high or way too low. But when Galton averaged them together, he was shocked:

The dead ox weighed 1,198 pounds. The crowd’s average: 1,197.

My emphasis added.  As pointed out by Philip Tetlock, “everyone has been surprised by these outcomes.”  Just like everyone (or many) are surprised by the results of momentum investing.  Yet, momentum works for for the very same reasons that “The Good Judgement Project” is apparently succeeding—the wisdom of the crowds.  It requires a certain amount of humility to turn investment decision-making over to the wisdom of the crowds and humility is not an attribute in great supply on Wall Street.  The vast majority of investment managers in this industry spend large portions of their time convincing others (and themselves) that they are right and “the market” is wrong, but that the market will eventually come around to their way of thinking.  Momentum takes a different approach.  If a given security, or group of securities, are relatively stronger than their peers momentum investors follow those trends—often times without a clear understanding of the fundamental reasons for the superior momentum characteristics.  If the market changes, momentum investors change and reorient their portfolios to adapt to the new leadership.  Momentum investors focus on the process of keeping their portfolios in-tune with market trends rather than praying that the market will eventually prove that any one “expert” opinion will prove correct.

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Michael Covel Interviews Tom Dorsey

March 31, 2014

Click here to listen to the story of how Tom came to embrace Point & Figure Charting.


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Quote of the Week

March 27, 2014

From Meb Faber:

 Most of the alpha out there (or smart beta or whatever it is being called these days) is either hard to find or hard to DO.  And by do, I mean it goes against everything your behavioral instincts tell you to do.  Buying a stock at all time highs is hard to do, and one reason momentum and trend work.  Buying a value investment is hard for many reasons.

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The Valuation Game

March 15, 2014

Morningstar analyst, Samuel Lee, describes one of the major challenges facing those investors who look to base their decisions on valuations.  Lee says, that “the problem is that earnings quality–operating, and as-reported, has declined over time.”  He cites Warren Buffet on the topic from one of his shareholder letters:

It was once relatively easy to tell the good guys in accounting from the bad: The late 1960s, for example, brought on an orgy of what one charlatan dubbed ‘bold, imaginative accounting’ (the practice of which, incidentally, made him loved for a time by Wall Street because he never missed expectations). But most investors of that period knew who was playing games. And, to their credit, virtually all of America’s most-admired companies then shunned deception.”In recent years, probity has eroded. Many major corporations still play things straight, but a significant and growing number of otherwise high-grade managers–CEOs you would be happy to have as spouses for your children or as trustees under your will–have come to the view that it’s okay to manipulate earnings to satisfy what they believe are Wall Street’s desires. Indeed, many CEOs think this kind of manipulation is not only okay, but actually their duty.

Lee’s article brings up the question of whether P/E ratios should be compared to their 130-year average or whether it would be better to compare today’s P/E ratios to their 30 or 50-year averages in order to determine whether the broad U.S. equity market is overvalued or undervalued.

Such difficulties with valuation only strengthen the case for trend following.  It’s not that earnings don’t matter–they certainly do.  It’s just not clear in what time frame and to what degree they will impact the stock price.  Investors are free to use any criteria they choose (or none at all) to determine whether they will buy or sell a given stock, ETF, or mutual fund.  Trend followers, like us, spend much less time worrying about concepts like overvalued or undervalued and much more time focusing on executing a strategy that seeks to build a portfolio of securities that have favorable relative strength characteristics.  Over time, we generally end up with a portfolio of securities that many analysts would view as fundamentally strong (at least in retrospect), it’s just that we rely on “the wisdom of the crowds” instead of Wall Street earnings reports.

HT: Abnormal Returns

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Survivorship Bias

February 28, 2014

Everyone in the financial services industry has seen awesome-looking backtests for various return factors or trading methods, but most people don’t even know what survivorship bias is.  When I see one of those amazing backtests and I ask how they removed the survivorship bias, the usual answer is “Huh?”

A recent post by Cesar Alvarez at Alvarez Quant Trading shows just how enormous survivorship bias can be for a trend following system.  Most people with amazing backtests, when pushed, will concede there might be “some” effect from survivorship.  None of them ever think it will be this large!

Here, Mr. Alvarez describes the bias and shows the results:

Pre-inclusion bias is using today’s index constituents as your trading universe and assuming these stocks were always in the index during your testing period. For example if one were testing back to 2004, GOOG did not enter the S&P500 index until early 2006 at a price of $390. But your testing could potentially trade GOOG during the huge rise from $100 to $300.


  • It is the first trading day of the month
  • Stock is member of the S&P500 (on trading date vs as of today)
  • S&P500 closes above its 200 day moving average (with and without this rule)
  • Rank stocks by their six month returns
  • Buy the 10 best performing stocks at the close

Source: Alvarez Quant Trading

(click on image to enlarge to full size)

Mind-boggling, isn’t it?  The fantastic system that showed 30%+ returns now shows returns of less than 8%!!  (The test period, by the way, was 2004-2013.)

Unfortunately, this is the way much backtesting is done.  It’s much more trouble to acquire a database that has all of the delisted securities and all of the historical index constituents.  That’s expensive and time-consuming, but it’s the only way to get accurate results.  (Needless to say, that’s how our testing is done.  You can link to one of our white papers that additionally includes Monte Carlo testing to make the results even more robust.)  By the way, the pre-inclusion bias also shows very clearly how the index providers actually manage these indexes!

Mr. Alvarez concludes:

People often write about systems they have developed using the current Nasdaq 100 or S&P 500 stocks and have tested back for 5 to 10 years. Looking at this table shows that one should completely ignore those results.

When looking at backtested results, it often pays to be skeptical and to ask some questions about survivorship bias.

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It’s All At The Upper End

February 3, 2014

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

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

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

 (Click on image to enlarge)

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

 (click on image to enlarge)

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

(click on image to enlarge)

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

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The 1%

January 6, 2014

“The 1%” phrase has been used a lot to decry income inequality, but I’m using it here in an entirely different context.  I’m thinking about the 1% in relation to a recent article by Motley Fool’s Morgan Housel.  Here’s an excerpt from his article:

Building wealth over a lifetime doesn’t require a lifetime of superior skill. It requires pretty mediocre skills — basic arithmetic and a grasp of investing fundamentals — practiced consistently throughout your entire lifetime, especially during times of mania and panic.  Most of what matters as a long-term investor is how you behave during the 1% of the time everyone else is losing their cool.

That puts a little different spin on it.  Maybe your behavior during 1% of the time is how you get to be part of the 1%.  (The bold in Mr. Housel’s quotation above is mine.)

In his article, Housel demonstrates how consistency—in this case, dollar-cost averaging—beats a couple of risk avoiders who try to miss recessions.  We’ve harped on having some kind of systematic investment process here, so consistency is certainly a big part of success.

But also consider what might happen if you can capitalize on those periods of panic and add to your holdings.  Imagine that kind of program practiced consistently over a lifetime!  Warren Buffett’s article in the New York Times, “Buy American.  I Am.” from October 2008 comes to mind.  Here is a brief excerpt of Mr. Buffett’s thinking during the financial crisis:

THE financial world is a mess, both in the United States and abroad. Its problems, moreover, have been leaking into the general economy, and the leaks are now turning into a gusher. In the near term, unemployment will rise, business activity will falter and headlines will continue to be scary.

So … I’ve been buying American stocks.

If prices keep looking attractive, my non-Berkshire net worth will soon be 100 percent in United States equities.

A simple rule dictates my buying: Be fearful when others are greedy, and be greedy when others are fearful. And most certainly, fear is now widespread, gripping even seasoned investors. To be sure, investors are right to be wary of highly leveraged entities or businesses in weak competitive positions. But fears regarding the long-term prosperity of the nation’s many sound companies make no sense. These businesses will indeed suffer earnings hiccups, as they always have. But most major companies will be setting new profit records 5, 10 and 20 years from now.

Let me be clear on one point: I can’t predict the short-term movements of the stock market. I haven’t the faintest idea as to whether stocks will be higher or lower a month — or a year — from now. What is likely, however, is that the market will move higher, perhaps substantially so, well before either sentiment or the economy turns up. So if you wait for the robins, spring will be over.

Gee, I wonder how that worked out for him?  It’s no mystery why Warren Buffett has $60 billion—he is as skilled a psychological arbitrageur as there is and he has been at it for a very long time.

As Mr. Housel points out, even with mediocre investing skills, just consistency can go a long way toward building wealth—and the ability to be greedy when others are fearful has the potential to compound success.

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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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Investor Behavior

January 6, 2014

Marshall Jaffe, in a recent article for ThinkAdvisor, made an outstanding observation:

In a world where almost nothing can be predicted with any accuracy, investor behavior is one of the rare exceptions. You can take it to the bank that investors will continue to be driven by impatience, social conformity, conventional wisdom, fear, greed and a confusion of volatility with risk. By standing apart and being driven solely by the facts, the value investor can take advantage of the opportunities caused by those behaviors—and be in the optimal position to create and preserve wealth.

His article was focused on value investing, but I think it is equally applicable to relative strength investing.  In fact, maybe even more so, as value investors often differ about what they consider a good value, while relative strength is just a mathematical calculation with little room for interpretation.

Mr. Jaffe’s main point—that investors are driven by all sorts of irrational and incorrect cognitive forces—is quite valid.  Dozens of studies point it out and there is a shocking lack of studies (i.e., none!) that show the average investor to be a patient, independent thinker devoid of fear and greed.

What’s the best way to take advantage of this observation about investor behavior?  I think salvation may lie in using a systematic investment process.  If you start with an investment methodology likely to outperform over time, like relative strength or value, and construct a rules-based systematic process to follow for entry and exit, you’ve got a decent chance to avoid some of the cognitive errors that will assail everyone else.

Of course, you will construct your rules during a period of calm and contemplation—but that’s never when rules are difficult to apply!  The real test is sticking to your rules during the periods of fear and greed that occur routinely in financial markets.  Devising the rules may be relatively simple, but following them in trying circumstances never is!  As with most things, the harder it is to do, the bigger the potential payoff usually is.

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The Simplicity of Relative Strength

December 16, 2013

One of the ongoing difficulties for investors is finding some kind of simple method for investing.  Relative strength is just such a simple method.  Even simple methods, however, have to be applied!

Tadas Viskanta from Abnormal Returns writes:

…having a plan, even a sub-optimal one, that you can stick to is preferable to having no plan at all. The ongoing challenge for advisors and investors alike is to find a plan that they will not abandon at the first sign of trouble.

That’s an important point.  If you can’t follow a method because it is too complex and if you bail in panic during the first downturn, you’re not going to succeed with any method.

Abnormal Returns revisited this theme recently, in connection with a discussion about systems versus optimization.  Mr. Viskanta pulled a quote from Scott Adams:

Optimizing is often the strategy of people who have specific goals and feel the need to do everything in their power to achieve them. Simplifying is generally the strategy of people who view the world in terms of systems. The best systems are simple, and for good reason. Complicated systems have more opportunities for failure. Human nature is such that we’re good at following simple systems and not so good at following complicated systems.

This has a great deal of applicability to the investing process.  Simple systems are generally more robust than complex systems, and relative strength is about as simple as you can get.  Relative strength is not an optimized system—like most simple systems, it will make plenty of mistakes but its simplicity makes it robust.  (I would note that Modern Portfolio Theory relies on mean variance optimization to construct the “ideal” portfolio.  Optimized systems are both complicated and fragile.)

In practice, complicated systems tend to blow up.  Robust systems are generally more resilient to failure, but will certainly struggle from time to time.

Human nature, I think, makes it difficult to follow any system, whether simple or complex, so discipline is also required.  Investors will improve their chances for success with a simple, robust methodology and the discipline to stick with it.

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A Reminder About Real Return

November 20, 2013

The main thing that should matter to a long-term investor is real return.  Real return is return after inflation is factored in.  When your real return is positive, you are actually increasing your purchasing power— and purchasing goods and services is the point of having a medium of exchange (money) in the first place.

A recent article in The New York Times serves as a useful reminder about real return.

The Dow Jones industrial average broke through 16,000 on Monday for the first time on record — well, at least in nominal terms. If you adjust for inflation, technically the highest level was on Jan. 14, 2000.

Adjusting for price changes, the Dow’s high today was still about 1.3 percent below its close on Jan. 14, 2000 (and about 1.6 percent below its intraday high from that date).

There’s a handy graphic as well, of the Dow Jones Industrial Average adjusted for inflation.

Source: New York Times/Bloomberg

(click on image to enlarge)

This chart, I think, is a good reminder that buy-and-hold (known in our office as “sit-and-take-it”) is not always a good idea.  In most market environments there are asset classes that are providing real return, but that asset class is not always the broad stock market.  There is value in tactical asset allocation, market segmentation, strategy diversification, and other ways to expose yourself to assets that are appreciating fast enough to augment your purchasing power.

I’ve read a number of pieces recently that contend that “risk-adjusted” returns are the most important investment outcome.  Really?  This would be awesome if I could buy a risk-adjusted basket of groceries at my local supermarket, but strangely, they seem to prefer the actual dollars.  Your client could have wonderful risk-adjusted returns rolling Treasury bills, but would then also get to have a lovely risk-adjusted retirement in a mud hut.  If those dollars are growing more slowly than inflation, you’re just moving in reverse.

Real returns are where it’s at.

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Dumb Talk About Smart Beta?

October 7, 2013

John Rekenthaler at Morningstar, who usually has some pretty smart stuff to say, took on the topic of smart beta in a recent article.  Specifically, he examined a variety of smart beta factors with an eye to determining which ones were real and might persist.  He also thought some factors might be fool’s gold.

Here’s what he had to say about value:

The value premium has long been known and continues to persist.

And here’s what he had to say about relative strength (momentum):

I have trouble seeing how momentum can succeed now that its existence is well documented.

The italics are mine.  I didn’t take logic in college, but it seems disingenuous to argue that one factor will continue to work after it is well-known, while becoming well-known will cause the other factor to fail!  (If you are biased in favor of value, just say so, but don’t use the same argument to reach two opposite conclusions.)

There are a variety of explanations about why momentum works, but just because academics can’t agree on which one is correct doesn’t mean it won’t continue to work.  It is certainly possible that any anomaly could be arbitraged away, but Robert Levy’s relative strength work has been known since the 1960s and our 2005 paper in Technical Analysis of Stocks & Commodities showed it continued to work just fine just the way he published it.  Academics under the spell of efficient markets trashed his work at the time too, but 40 years of subsequent returns shows the professors got it wrong.

However, I do have a background in psychology and I can hazard a guess as to why both the value and momentum factors will continue to persistthey are both uncomfortable to implement.  It is very uncomfortable to buy deep value.  There is a terrific fear that you are buying a value trap and that the impairment that created the value will continue or get worse.  It also goes against human nature to buy momentum stocks after they have already outperformed significantly.  There is a great fear that the stock will top and collapse right after you add it to your portfolio.  Investors and clients are quite resistant to buying stocks after they have already doubled, for example, because there is a possibility of looking really dumb.

Here’s the reason I think both factors are psychological in origin: it is absurdly easy to screen for either value or momentum.  Any idiot can implement either strategy with any free screener on the web.  Pick your value metric or your momentum lookback period and away you go.  In fact, this is pretty much exactly what James O’Shaughnessy did in What Works on Wall Street.  Both factors worked well—and continue to work despite plenty of publicity.  So the barrier is not that there is some secret formula, it’s just that investors are unwilling to implement either strategy in a systematic way–because of the psychological discomfort.

If I were to make an argument—the behavioral finance version—about which smart beta factor could potentially be arbitraged away over time, I would have to guess low volatility.  If you ask clients whether they would prefer to buy stocks that a) had already dropped 50%, b) had already gone up 50%, or c) had low volatility, I think most of them would go with “c!”  (Although I think it’s also possible that aversion to leverage will keep this factor going.)

Value and momentum also happen to work very well together.  Value is a mean reversion factor, while momentum is a trend continuation factor.  As AQR has shown, the excess returns of these two factors (unsurprisingly, once you understand how they are philosophical opposites) are uncorrelated.  Combining them may have the potential to smooth out an equity return stream a little bit.  Regardless, two good return factors are better than one!

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Stocks for the Long Run

September 20, 2013

Unlike certain authors, I am not promoting some agenda about where stocks will be at some future date!  Instead, I am just including a couple of excerpts from a paper by luminaries David Blanchett, Michael Finke, and Wade Pfau that suggests that stocks are the right investment for the long run—based on historical research.  Their findings are actually fairly broad and call market efficiency into question.

We find strong historical evidence to support the notion that a higher allocation to equities is optimal for investors with longer time horizons, and that the time diversification effect is relatively consistent across countries and that it persists for different levels of risk aversion.

When they examine optimal equity weightings in a portfolio by time horizon, the findings are rather striking.  Here’s a reproduction of one of their figures from the paper:

Source: SSRN/Blanchett, Finke, Pfau  (click to enlarge)

They describe the findings very simply:

Figure 1 also demonstrates how to interpret the results we include later in Tables 2 and 3. In Figure 1 we note an intercept (α) of 45.02% (which we will assume is 45% for simplicity purposes) and a slope (β) of .0299 (which for simplicity purposes we will assume is .03). Therefore the optimal historical allocation to equities for an investor with a 5 year holding period would be 60% stocks, which would be determined by: 45% + 5(3%) = 60%.

In other words, if your holding period is 15-20 years or longer, the optimal portfolio is 100% stocks!

Reality, of course, can be different from statistical probability, but their point is that it makes sense to own a greater percentage of stocks the longer your time horizon is.  The equity risk premium—the little extra boost in returns you tend to get from owning stocks—is both persistent and decently high, enough to make owning stocks a good long-term bet.

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From the Archives: Inherently Unstable Correlations

September 19, 2013

No, this is not a post on personality disorders.

Rather, it is a post on the inherently unstable nature of correlations between securities and between asset classes.  This is important because the success of many of the approaches to portfolio management make the erroneous assumption that correlations are fairly stable over time.  I was reminded just how false this belief is while reading The Leuthold Group‘s April Green Book in which they highlighted the rolling 10-year correlations in monthly percentage changes between the S&P 500 and the 10-year bond yield.  Does this look stable to you?  Chart is shown by permission from The Leuthold Group.

Correlation Inherently Unstable

(Click to Enlarge)

If you are trying to use this data, would you conclude that higher bond yields are good for the stock market or bad?  The answer is that the correlations are all over the map.  In 2006, William J. Coaker II published The Volatility of Correlations in the FPA Journal.  That paper details the changes in correlations between 15 different asset classes and the S&P 500 over a 34-year time horizon.  To give you a flavor for his conclusions, he pointed out that Real Estate’s rolling 5-year correlations to the S&P 500 ranged from 0.17 to 0.75, and for Natural Resources the range was -0.34 to 0.49.   History is conclusive – correlations are unstable.

This becomes a big problem for strategic asset allocation models that use historical data to calculate an average correlation between securities or asset classes over time.  Those models use that stationary correlation as one of the key inputs into determining how the model should currently be allocated.  That may well be of no help to you over the next five to ten years.  Unstable correlations are also a major problem for “financial engineers” who use their impressive physics and computer programming abilities to identify historical relationships between securities.   They may find patterns in the historical data that lead them to seek to exploit those same patterns in the future (i.e. LTCM in the 1990′s.) The problem is that the future is under no obligation to behave like the past.

Many of the quants are smart enough to recognize that unstable correlations are a major problem.  The solution, which I have heard from several well-known quants, is to constantly be willing to reexamine your assumptions and to change the model on an ongoing basis.  That logic may sound intelligent, but the reality is that many, if not most, of these quants will end up chasing their tail. Ultimately, they end up in the forecasting game.  These quants are rightly worried about when their current model is going to blow up.

Relative strength relies on a different premise.  The only historical pattern that must hold true for relative strength to be effective in the future is for long-term trends to exist. That is it.  Real estate (insert any other asset class) and commodities (insert any other asset class) can be positively or negatively correlated in the future and relative strength models can do just fine either way.  Relative strength models make zero assumptions about what the future should look like.  Again, the only assumption that we make is that there will be longer-term trends in the future to capitalize on.  Relative strength keeps the portfolio fresh with those securities that have been strong relative performers.  It makes no assumptions about the length of time that a given security will remain in the portfolio.  Sure, there will be choppy periods here and there where relative strength models do poorly, but there is no need (and it is counterproductive) to constantly tweak the model.

Ultimately, the difference between an adaptive relative strength model and most quant models is as different as a mule is from a horse.  Both have four legs, but they are very different animals.  One has a high probability of being an excellent performer in the future, while the other’s performance is a big unknown.

—-this article originally appeared 4/16/2010.  It’s important to understand the difference between a model that relies on historical correlations and a model that just adapts to current trends.

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Bucket Portfolio Stress Test

September 4, 2013

I’ve long been a fan of portfolio buckets or sleeves, for two reasons.  The first reason is that it facilitates good diversification, which I define as diversification by volatility, by asset class, and by strategy.  (We happen to like relative strength as one of these primary strategies, but there are several offsetting strategies that might make sense.)  A bucket portfolio makes this kind of diversification easy to implement.

The second benefit is largely psychological—but not to be underestimated.  Investors with bucket portfolios had better performance in real life during the financial crisis because they didn’t panic.  While the lack of panic is a psychological benefit, the performance benefit was very real.

Another champion of bucketed portfolios is Christine Benz at Morningstar.  She recently wrote a series of article in which she stress-tested bucketed portfolios, first through the 2007-2012 period (one big bear market) and then through the 2000-2012 period (two bear markets).  She describes her methodology for rebalancing and the results.

If you have any interest in portfolio construction for actual living, breathing human beings who are prone to all kinds of cognitive biases and emotional volatility, these articles are mandatory reading.  Better yet for fans of portfolio sleeves, the results kept clients afloat.  I’ve included the links below.  (Some may require a free Morningstar registration to read.)

Article:  A Bucket Portfolio Stress Test

Article:  We Put the Bucket System Through Additional Stress Tests

Article:  We Put the Bucket System Through a Longer Stress Test



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3 Keys to a Simple Investment Strategy

August 8, 2013

Simplicity is the ultimate sophistication.—-Leonardo di Vinci

This quotation doubles as the title of a Vanguard piece discussing the merits of a simple fund portfolio.  However, it occurred to me that their guidelines that make the simple fund portfolio work are the same for making any investment strategy work.  They are:

  • adopt the investment strategy
  • embrace it with confidence, and
  • endure the inevitable ups and downs in the markets

Perhaps this seems obvious, but we see many investors acting differently, more like this:

  • adopt the investment strategy that has been working lately
  • embrace it tentatively, as long as it has good returns
  • bail out during the inevitable ups and downs in the markets
  • adopt another investment strategy that has been working lately…

You can see the problem with this course of action.  The investment strategy is only embraced at the peak of popularity—usually when it’s primed for a pullback.  Even that would be a minor problem if the commitment to the investment strategy were strong.  But often, investors bail out somewhere near a low.  This is the primary cause of poor investor returns according to DALBAR.

Investing well need not be terribly complicated.  Vanguard’s three guidelines are good ones, whether you adopt relative strength as we have or some different investment strategy.  If the strategy is reasonable, commitment and patience are the big drivers of return over time.  As Vanguard points out:

Complexity is not necessarily sophisticated, it’s just complex.

Words to live by.


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