The Coach Who Never Punts

November 14, 2013

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

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

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


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Q1 RS Factor Review

April 4, 2012

Earlier this quarter we updated our white paper on using relative strength to invest in stocks.  If you haven’t read the paper you can find it here.  In this post I will be recapping the performance of various relative strength (momentum) factors using the same methodology used in the paper.

The S&P 500 had a great first quarter ending up about 12% (price only).  Relative strength strategies did OK.  The best performing factors during Q1 were actually the factors that performed the worst over a long time horizon (see the white paper for details).  Several of the best long-term winning factors had a tough time in Q1.

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The graph above shows the returns for all 100 trials for each of the time-based RS factors we track.  A trailing 18 month and 36 month window to compute RS worked very well.  These worked well because those models didn’t rotate into low volatility names at the end of last year, and then rotate back out of them during Q1.  In effect, the long time horizon allowed them to capitalize on the laggard bounce that was so prevalent during the first part of the quarter.  The very short-term windows also did well.  They were able to quickly rotate into the high beta names that were the leadership.  But, more importantly, that trend was sustainable so the short-term mean reversion effect didn’t hurt those factors in Q1.  The 6 month and 9 month factors performed very poorly.  The main reason is these intermediate term factors rotated into low beta and high dividend stocks at the end of last year.  Those were the laggards during Q1, and it took some time for those models to rotate into the new leadership.  Keep in mind, however, that these two factors are two of the best performing over long time horizons.

The laggard bounce was most pronounced in January and February.  By March things had settled back down and the intermediate term factors were performing well.  The better performance was the result of the market rewarding intermediate term momentum, and the models having a chance to shed the laggards and re-position themselves into the current leadership.

January Performance

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February Performance

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March Performance

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The turnaround for intermediate term momentum strategies wasn’t enough to totally reverse the underperformance during the first two months of the year.  But it is very good to see the intermediate term factors getting back into gear!  We noticed the same thing in our managed portfolios too.  Things definitely picked up in the last part of the quarter for high RS stocks.

All of the factors in this post are simple, time based relative strength (momentum) factors.  These are the factors that match what we published in the white paper.  We do track other RS factors though.  It is interesting to note, that the underperformance of the intermediate term factors was most pronounced in the simple, time based factors.  Intermediate term factors we track that use some sort of smoothing or multiple time periods performed much better than the 6 and 9 month factors.  The only explanation I have for that is that the 6 month ranking window was the perfect time to maximize your whipsaw into low volatility and back out again.  The smoothed and compound factors did a much better job this quarter at avoiding that whipsaw.

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Relative Strength And Portfolio Management

February 3, 2012

Years ago we developed a testing protocol to help us determine how robust a strategy really is.  We wanted to determine how much of the strategy’s tested returns were a result of luck and how much of the return was due to the underlying factor performance.  We have run all of our strategies through that process over the years, and we published some of those results back in 2010.  The data was just updated through the end of last year and the updated can be found here.

When testing a model it is always difficult to determine if the results you are achieving are repeatable or not.  If you are testing a high relative strength model, for example, are the results coming from one or two stocks that make the whole test look fantastic?  If that is the case I would have my doubts about how that strategy would perform in real-time.  But if the results are truly from an underlying factor performance (regardless of the individual securities in the portfolio) then you have something you can work with.

The way we determine if a model is lucky or not is to run multiple simulations based on a random draw of securities.  In a relative strength model we might break our universe into ten different buckets.  Out of the highest bucket we might draw 50 stocks at random.  We hold those stocks until they are no longer classified as high relative strength securities.  Once they fall below a specific rank we sell the security and buy another one at random.  If we run 100 trials we get 100 different portfolios over time.  What we are trying to determine is if the individual securities in the test really matter, or is just the concept of buying high relative strength securities over time what causes the outperformance.

As it turns out, what stocks go in to the portfolio aren’t as important as exploiting the factor.  A disciplined approach is that consistently drives the portfolio to strength is what drives the returns over time.

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The table shows the results from one of the factors tested in the paper.  You can see the range of outcomes each year as well as how each model did over the 16 year test period.  Sometimes the models outperform, sometimes the underperform, and some years you have mixed results.  But over 16 years, all of the models outperformed!  All we did was pick stocks at random out of a high relative strength basket.  There is nothing complicated about it.  The main thing is that the process is systematic and extremely disciplined.

More details about the testing process and results can be found in the paper (click here).

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It’s Not You, It’s Me…..

January 12, 2012

It’s not you, it’s me….  I think everyone has used that line at some point, but nobody does it better than George Costanza!

I have been putting data together to update our white papers.  It’s no secret that running a Global Tactical Asset Allocation (TAA) strategy was difficult last year.  But when I looked at the data it was very clear that the problem wasn’t the strategies.  The real problem was how the market behaved during 2011.  It’s not you, it’s me.  It’s not your trend following strategy, it’s what you’re trying to follow.  The market was essentially a psycho, stage 5 clinger last year!

The data I will reference in this post is an extension of the data we published last year in two white papers.  If you haven’t read them you can find them here.  Our research process for this dataset takes a diverse universe of ETF’s and creates 100 different equity curves for a number of different momentum factors.  The universe has a number of different asset classes represented including Equities (Domestic & Foreign), Bonds, Commodities, Currencies, and Real Estate.  The results provide a good idea about how a momentum-based, global TAA strategy would have performed.  By creating 100 different equity curves we are taking luck out of the equation and showing a realistic range of outcomes from buying high relative strength securities out of our universe.

Most of the momentum factors we follow underperformed last year.  The factors we are showing refer to the lookback period to do our rankings.  The 1MORET factor (1-month return) means we used 1 month of data to calculate our momentum ranks (all securities are held until they fall out of the top of the ranks, which might be as short as one week or as long as a couple of years).  The 12MORET factor uses the prior 12 months of price data to rank the securities.  The 3-month factor actually performed the best in 2011, but only 40 out of the 100 trials outperformed the S&P 500, so you needed some luck to outperform.  The 6-month factor was the next best, but only 1 trial outperformed so you needed to be really lucky.  All the other trials were very poor.  There was so much short-term volatility back and forth last year that the very short 1-month formulation period was deadly.  It paid not to be too quick on the trigger last year!

Full Year 2011

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But looking at 2011 in aggregate doesn’t really tell the whole story.  The beginning of the year was good for these strategies.  That person you were dating held it together pretty well for the first couple of dates!  Through the end of April, most of the strategies were outperforming the S&P 500 on average.  The 6-month factor was doing great as all 100 trials were outperforming.  Ironically, the factor doing the worst was the 3-month factor.

2011 Through April

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The problems for trend following strategies began in May.  There were a series of sharp trend reversals in a number of different assets: Bonds, Stocks, Precious Metals, Currencies (Yen & Swiss Franc).  No matter what factor you were using from May to the end of the year it was difficult.  It was tough to get traction anywhere.  The only factor that did even so-so was the 3-month factor, and that was the worst factor through April.  That’s just one of many examples of how crazy the 2011 market was!

2nd Half 2011

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So where do we go from here?  Well, the, “It’s not you, it’s me…” line always leads to a breakup.  That’s probably not a bad idea when dealing with something that doesn’t change.  Does that psycho, stage 5 clinger ever get any better?  Nope.  It only gets worse.

But markets change, and TAA based on momentum is very adaptive.  We will not be in a choppy, range bound environment forever.  Trends will emerge. (If they don’t, it will be the first time in history.)

Investors were euphoric about momentum-based TAA strategies in the first part of the year.  Looking at the data you can see why – they were working exceptionally well.  After the last few months, people are certainly not as excited.  In reality, now is the time to be really excited about relative strength strategies, not back in April.  Now is the time you want to be adding money.

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Relative Strength and Market Volatility

September 30, 2011

Markets have been extremely volatile over the last couple of months.  Volatile markets are very difficult to navigate.  It is very easy to make mistakes, and when a mistake is made it is magnified by the volatility.  From a relative strength standpoint, there are things you can do to help ease the pain of all of these large, unpredictable market moves.  But judging by all the client calls we have taken over the years–almost always when volatility was high–the steps needed to make a relative strength model perform well are most definitely not what most investors would think!

Before we look at relative strength specifically, let’s take a step back and look at different investment strategies on a very broad basis.  There are really two types of strategies: trend continuation and mean reversion.  A trend continuation strategy buys a security and assumes it will keep moving in the same direction.  A mean reversion (or value) strategy buys a security and assumes it will reverse course and come closer to a more “normal” state.  Both strategies work over time if implemented correctly, but volatility affects them in different ways.  Mean reversion strategies tend to thrive in high volatility markets, as those types of markets create larger mispricings for value investors to exploit.

When we construct systematic relative strength models, we have always preferred to use longer-term rather than shorter-term signals.   This decision was made entirely on the basis of data—by testing many models over a lot of different types of markets.  Judging by all the questions we get during periods of high volatility, I would guess that using a longer-term signal when the market is volatile strikes most investors as counter-intuitive.  In my years at Dorsey Wright, I can’t remember talking to a single client or advisor that told me when markets get really volatile they look to slow things down!

During volatile markets, generally we hear the opposite view–everyone wants to speed up their process.  Speeding up the process can take many forms.  It might mean using a smaller box size on a point and figure chart, or using a 3-month look back instead of a 12-month look back when formulating your rankings.  It might be as simple as rebalancing the portfolio more often, or tightening your stops.  Whatever the case, most investors are of the opinion that being more proactive in these types of markets makes performance better.

Their gut response, however, is contradicted by the data.  As I mentioned before, our testing has shown that slowing down the process actually works better in volatile markets.  And we aren’t the only ones who have found that to be the case!  GMO published a whitepaper in March 2010 that discussed momentum investing (the paper can be found here).  Figure 17 on page 11 specifically addresses what happens to relative strength models during different states of market volatility.

(Click Image To Enlarge.  Source: GMO Whitepaper, Sept. 2010)

The chart clearly shows how shortening your look back period decreases performance in volatile markets.  The 6-12 month time horizon has historically been the optimal time frame for formulating a momentum model.  But when the market gets very volatile, the best returns come from moving all the way out to 12 months, not shortening your window to make your model more sensitive.

Psychologically, it is extremely difficult to lengthen your time horizon in volatile markets.  Every instinct you have will tell you to respond more quickly in order to get out of what isn’t working and into something better.  But the data says you shouldn’t shorten your window, and conceptually this makes sense.  Volatile markets tend to be better for mean reversion strategies.  But for a relative strength strategy, volatile markets also create many whipsaws.  When thinking about how volatility interacts with relative strength, it makes sense to lengthen your time horizon.  Hopping on every short term trend is problematic if the trends are constantly reversing!  All the volatility creates noise, and the only way to cancel out the noise is to use more (not less) data.  You can’t react to all the short-term swings because the mean reversion is so violent in volatile markets.  It doesn’t make any sense to get on trends more rapidly when you are going through a period that is not optimal for a trend following strategy.

We use a data-driven process to construct models.  We have found that using a relatively longer time horizon, while uncomfortable, ultimately leads to better performance over time.  Outside studies show the same thing.  If the data showed that reacting more quickly to short-term swings in volatile markets was superior we would advocate doing exactly that!

As is often the case in the investing world, this seems to be another situation where doing the most uncomfortable thing actually leads to better performance over time.  Good investing is an uphill run against human nature.  Of course, it stands to reason that that’s the way things usually are.  If it were comfortable, everyone would do it and investors would find their excess return quickly arbitraged away.

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The Lucky Few

August 9, 2011

I just read Andy’s post about the High RS Diffusion Index.  My first reaction was, “Wow, that’s a really low number!”  My second reaction was, “Good God, what’s still above its 50 Day Moving Average from that universe?”

The list of securities is short:

  • Green Mountain Coffee Roasters (GMCR)
  • Southern Union Co (SUG)
  • Timberland Co (TBL)
Southern Union and Timberland have held up well because they are buyouts.  That leaves Green Mountain as the only true company from the universe above the 50 Day MA on its own merits.  I don’t think that really means much for GMCR.  I think it just shows how incredibly washed out this market is right now.  The “real” number rounds down to zero!
Disclosure: Dorsey Wright Money Management has positions in GMCR in a number of different account styles.

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The Love Affair Continues

July 13, 2011


I wish I could quit you, Quantitative Easing.

You complete me, Quantitative Easing.

You had me at hello……

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Small Cap & NASDAQ Technical Leaders Update

July 1, 2011

At the end of March we began tracking two new indexes based on our Technical Leaders methodology.  The two new indexes follow Small Capitalization stocks and stocks traded on the NASDAQ exchange.  We use our Technical Leaders methodology for three other indexes: Domestic Equities, Developed Markets Foreign Equities, and Emerging Markets Equities.  These three indexes are licensed by PowerShares and you can purchase ETF’s based on the (tickers: PDP, PIZ, and PIE respectively).

These two new indexes aren’t licensed by an ETF provider so you can’t directly invest in them.  We like the concept for both indexes because history shows that relative strength works very well with small cap stocks.  The NASDAQ Technical Leaders is also very intriguing because there are many companies in that universe with very dynamic business models, and those are the type of companies that relative strength is very good at identifying and capitalizing on.

The constituents for both indexes are below:

Small Cap:


The performance for the second quarter was so-so.  Both indexes had a huge first quarter (as did most RS strategies) so they remain well ahead of their benchmarks for the year.

If you have any questions about the indexes please post them in the comments section.

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The Widowmaker Rides Again

June 3, 2011

Netflix has been one of the real battleground stocks over the last year.  Momentum buyers love the stock.  The shorts think it is way overvalued.  Whitney Tilson even had to throw in the towel on his well-publicized short of NFLX.  NFLX has confounded so many people it was referred to recently as a Widowmaker!

Why is NFLX such a widowmaker for the shorts?  Probably because it has performed very well when the market is down!  If you are shorting any stock you should be relieved to look over at the quote screen and see a sea of red.  But over the past year, that’s not exactly what you get with NFLX.  Below is a chart of NFLX’s performance over the last 12 months when the S&P 500 is down for the day.  NFLX is up substantially over the past year when the S&P is down.  That has to be a killer if you’re short.  Hence, The Widowmaker.

Now look at the performance of NFLX over the past year when the S&P has an up day.  It hasn’t exactly shot the lights out for the longs on up days!

I suppose when you are looking for a good solid defensive name that will hold up in a down market you should put NFLX at the top of the list!  It does look like that relationship is changing over the last couple of months.  But no matter how you cut it, NFLX continues to confound everyone!  Same old story with this stock today.  The market is selling off hard on the anemic jobs number, but NFLX was up in the morning.

Disclosure:  Dorsey Wright Money Management has positions in NFLX.  Past performance is no guarantee of future results.  A list of all holdings for this portfolio over the past 12 months is available upon request.

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New Technical Leaders Indexes

March 31, 2011

The Technical Leaders Indexes are indexes created by Dorsey Wright Money Management and are constituted with high relative strength securities from a given universe.  We currently run three indexes:  Domestic mid to large cap equity, Foreign Developed Markets Equity, and Emerging Markets Equity.  These three indexes are licensed by PowerShares and can be purchased in an ETF format (Tickers: PDP, PIZ, PIE).

We are expanding the number of indexes we create.  We are adding two more indexes to the Technical Leaders family.  (Please note that these indexes are not licensed by any ETF sponsor so there is no vehicle to purchase them directly.)

One of the new Technical Leaders indexes will cover the Domestic Small Cap space.  All of our other indexes are constituted with 100 securities, but the Small Cap Technical Leaders Index will have 200.  This will still allow us to select the top decile from a small cap index like the Russell 2000, while keeping liquidity constraints in mind.  To see a list of the current constituents you can click here:

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The second index we are beginning to publish tracks 100 high RS securities traded on the NASDAQ exchange.  As you can imagine, the selection process will pull out a lot of emerging growth companies so we think this index will be very interesting to follow.  For a list of the current constituents you can click here:

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Over the next couple of days I’ll post some more information about what is in the indexes.  If you have any questions feel free to post them in the comments section and I’ll try to respond to them.

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Buying The Dips

March 22, 2011

Retail investors and hedge funds have taken opposing views on the most recent stock market correction.  Clusterstock has a short post on what Global Macro hedge funds did during the dip (click here for the original post).  The graph below (taken from the original Clusterstock post) was produced by BofA ML and shows net exposure to the S&P 500 Index for Global Macro Hedge Funds.

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You can see spike up in long exposure to equities over the last month.  Our own sentiment survey (the full results of the most recent survey can be seen here), and the most recent AAII survey both show retail investors have become more bearish over the last month.  These two surveys don’t measure actual exposure like the BofA survey does, but I think it is safe to assume that retail investors are not increasing equity exposure while they are becoming more bearish on stocks.

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In our survey we ask financial professionals whether their clients are becoming more fearful.  As equities rallied, their clients became less worried about a downturn.  But as the S&P 500 corrected over the last month their clients became more worried about getting caught in a downdraft.

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The AAII poll asks individual investors directly whether they are bullish or bearish.  This chart was taken from Bespoke and clearly shows individual investors became more bearish very quickly during the decline.

Only time will tell which group is correct.  However, I think it is a positive sign for equity markets that there are large pools of money ready to move into stocks during very small corrections.  It is also a positive sign that not everyone is buying the dips!  When everyone is excited to buy dips you are often closer to a top than a bottom.

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Updated White Paper Data

April 8, 2010

Back in January, we published a white paper that discussed using relative strength and portfolio management.  If you haven’t read the paper (or would like to read it again) it can be found here.  (Note: Please see the original paper for all of the necessary disclosures.)  The original paper also outlines the unique process we use to test various relative strength factors.  All of the data in that paper was updated through 2009.  Since we have just finished updating all of our data through the end of Q1, we can update the data in the paper.

The first quarter was very good for relative strength.  The data in the original paper showed that the best returns come from an intermediate term time horizon (about 3-12 months).  Last year that was very different.  We found very good returns for 2009 at very short-term time horizons.  A 1-Month RS factor was actually one of the better performers in 2009.  Over longer periods, a 1-Month RS factor has been a very poor performer so we definitely saw some anomalies during the huge laggard rally last year.  The first quarter of 2010 was much more normal for relative strength strategies.  The table below shows the performance for the first three months of 2010 for all of the models we tested in the original paper.

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The best returns came in the 6-12 month time horizon, which is what we would expect.  (For those of you who are confused about the “Factor,” it is not a holding period.  It is the lookback period for calculating relative strength.  So the 6-Mo Price Return, for example  simply takes the 6-month return for all stocks in the universe and ranks them from best to worst.)

The next table shows the cumulative annualized returns for all of the models updated through 3/31/10.

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The various models keep chugging along!  The intermediate term factors work very well.  Even when we are throwing darts at a basket of high relative strength stocks we find 100 out of 100 trials outperforming the benchmark over time.  As the original paper showed, relative strength models aren’t going to outperform each quarter or each year, but over time they do exceptionally well.

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New White Paper: DWAMM Testing Process

January 26, 2010

Anyone involved in developing and implementing systematic investment strategies should be obsessed with testing.  There is no end to the advice you can find about how to invest.  The problem for investors is that most of these investment ideas don’t actually stand up to rigorous testing.  They may sound good, and they may have worked for a short time period, but when you test them over different market conditions they don’t work as advertised.

We focus on relative strength as the main (or only) factor in our investment process.  There has been quite a bit of testing done over the years that shows how well RS/Momentum works over intermediate-term time horizons.  There are certainly other factors that stand up to rigorous testing over time, but RS is where we feel we have an exploitable edge over time.  Market technicians have used RS/Momentum for many years.  The development of computers in the 1960’s even allowed large-scale testing of the idea that strong stocks outperform over intermediate-term time horizons.  The academic community got into the act in the early 1990’s, and have continued to research the topic heavily because it was such a blow to long-held academic theories about stock market behavior.  With so many different people testing RS using different universes and different formulations for calculating RS, I think it’s safe to say that RS/Momentum strategies can add alpha over time.

What is frustrating about most of this testing is that is very difficult to implement in an actual portfolio.  Do you really want to go long 200 stocks and short 200 stocks and rebalance the whole portfolio every month?  Not likely.  So in the real world, portfolio managers take a factor that has been proven to work, and then begin haphazardly applying it to their style.  Is it a sound investment strategy to take a list of high RS stocks and then buy a subset that have “good managements?”  Will it work if you cherry pick some stocks with good value characteristics out of the high RS list?  What if you don’t rebalance on the same schedule as the RS testing?  These are just some of the problems if you don’t implement your strategy exactly as the research was done.

We designed a custom testing process here that determines how robust RS/Momentum is as a factor.  Instead of rebalancing each month, quarter, or year we run a continuous process that behaves like an actual portfolio manager would.  We also run a Monte Carlo process that buys high RS stocks at random instead of just taking the top ranked stock.  This helps us determine what kind of range in outcomes we can expect over time, and what can happen if you get lucky or unlucky in your stockpicking.

In a whitepaper available here we outline our unique testing process using several well-known RS factors.  We have a proprietary RS factor we use for our actual portfolio management, but we used well-known factors because people are always amazed that it doesn’t take a “silver bullet” to outperform over time using RS.

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Laggard Rally Progression

December 15, 2009

The laggard rally of 2009 has been the second largest in history.  Only the massive laggard rally off the market low in 1932 was larger (and by a substantial amount!).  Doug Ramsey of The Leuthold Group ( published a great study in their December 2009 Green Book that addressed laggard rallies in relation to market drawdowns.  His thesis is that the larger the market drawdown, the larger the laggard rally.

Doug was kind enough to help me sort through the source data they used for the research (6  Portfolios Formed On Size & Momentum on Ken French’s website) so I was able to recreate his results, which are shown below.

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The chart above measures the market drawdown on the horizontal axis, and the amount of the laggard outperformance over 12 months on the vertical axis.  You can see the very strong relationship between drawdown and laggard outperformance.  The laggard rally from the lows of the most recent drawdown are basically sitting on the regression line.  In terms of the fitted model, this is about what we should have expected.

The striking relationship between drawdown and laggard outperformance got me thinking.  We are always on the lookout for a reliable regime switching methodology that can reliably predict these periods of laggard outperformance.  We have tested a lot of ideas, but nothing ever seems to improve the performance of simply sticking with the winners over the long haul.  I broke the performance of the laggards out into multiple time periods to see how the laggard rally unfolds over time.  The charts below show what happens over the first three months from the market bottom.  One chart includes all of the data, and the second chart excludes the datapoint from the bottom in 1932 because it was such an outlier.

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You can see there is a very strong relationship between drawdown and laggard outperformance over the first three months after the market low.  Excluding the datapoint from 1932 weakens the fit somewhat, but the relationship is still there.  Now compare the performance over the first 3 months to the performance of the next 9 months (months 3-12).

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Once you get past the initial thrust off the bottom there is very little correlation between laggard outperformance and drawdown.  Sometimes the laggard rally ends, and the leaders begin to outperform again.  Other times the laggards continue to outperform.  It is very unreliable and you might as well just toss a coin.

In looking at the data, it is my opinion that the reason it is so difficult to consistently capitalize on the laggard rallies is that market timing becomes so important.  Most of the consistent money is made in the laggards in the initial thrust off the lows.  I think it goes without saying that in order to get in at the lows you either need to be a very good market timer or very lucky!  Buying laggards near what you think is the bottom can be a disaster.  Many people (yours truly included) thought the market bottomed in November 2008.  If you had bought some bank stocks at that point you had problems as they got crushed in the first part of 2009.  Bank Of America, one of the poster children for laggard stocks in the decline, went from $11.50 to $3.75 from the November lows until the ultimate lows in March 2009.  You would have ultimately been bailed out in that trade if you held on because the laggard rally has been so dramatic.  But I think everyone reading this needs to be honest with themselves: very few people would have actually held on to that stock for the entire round trip.

Catching a nice laggard rally requires tremendous timing.  It also requires incredible fortitude because you must buy companies that appear to have very dim prospects of survival (let alone outperformance).  If you can do these two things you must be extremely nimble because the effect doesn’t appear to be consistently reliable once the initial thrust is done.  We have just seen the second largest laggard rally in history (at least according to this data).  This laggard rally has been about double what we have seen from the rallies off the lows in 1938, 1990, and 2002.  Given the magnitude of outperformace by the laggards it is extremely tempting to attempt to enter into a regime switching methodology to capitalize on it.  I see two problems with this.  First, over time it has been more difficult to consistently capitalize on this phenomenon than the most recent rally would suggest.  Second, this laggard rally has been historically huge.  You probably won’t get the same results going forward for quite some time.  Of course there will be laggard rallies in the future, but history shows they have a higher probability of being much more muted than the current one.

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Black Swan Of Laggard Rallies

December 9, 2009

It has been a difficult year for Relative Strength strategies.  RS is a “success-based” factor, and what has worked this year has been companies that have had little success. From an RS standpoint, the laggards (those stocks that declined the most heading into the market low) have outperformed the leaders (those that held up the best in the market decline) by a historically wide margin.  According to research by The Leuthold Group in their December 2009 Green Book, this year has been the second best year for laggard outperformance in history.  The best year for the laggards to outperform the leaders was all the way back beginning at the market low in June 1932.  Their study uses Ken French’s data, and calculates the performance spread between the leaders and laggards in the first 12 months of a new bull market.

The historically high laggard outperformance presents interesting challenges for anyone who is testing and systematically applying an RS factor.  Take the following two models, for example:

For purposes of this post the actual model specifications don’t matter.  But for those of you who will e-mail me anyway, Model A is a simple 12-month trailing return model with 50 holdings.  Very straightforward and plain vanilla.  Model B is just a test I was playing around with that attempts to adapt more quickly to the market.  In effect, the RS factor is a 12-month trailing return unless there is a 20% swing in the S&P 500.  If there is a 20% swing, the lookback period changes from 12 months ago to the market high or low where the swing is measured from.

If you just look at the cumulative return you would say that Model B is much better.  But is it really?  There are some big differences in the performance of the two models in 2002 and 2003, but look at the difference in 2009.  Essentially, all of the outperformance from Model B comes in 2009.  In fact, if you ran these two models as of 12/31/2008 you would have said that Model A is better (+186% for Model A versus +154% for Model B).

You can’t simply look at the return streams of any model in a vacuum.  There are reasons why the returns are what they are, and those reasons need to be considered.  I think hitching your wagon to an RS strategy that makes a huge percentage of its relative (to other models) gains in a year like 2009 is asking for trouble.  According to Leuthold’s research, the next set of laggard rallies that were difficult for RS were off the market lows in 1990, 2002, and 1938.  All three of those instances were around 40% outperformance by the laggards over 12 months.  This current laggard rally is almost 2 times that (77%).

Could it be that we have just seen the proverbial Black Swan of laggard rallies?  It’s certainly possible.  While a laggard rally of this magnitude can certainly happen again (and probably will at some point), the data suggests that relying on this type of rally to generate returns using an RS model over long periods of time is not wise.  So like most things that get tested, I would say the idea of Model B was very interesting, but will wind up in the graveyard of good ideas.

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Walking Albert Pujols

October 8, 2009

During last night’s Dodgers/Cardinals playoff game, the subject of walking Albert Pujols was discussed by one of the announcers, Bob Brenly.  For those of you who are not familiar with the Great Albert Pujols, he is one of (if not the) best hitters in the game today.  He is also one of the best hitters of all time, and by the time he is done with baseball he will no doubt have made his presence felt all over the record books.  The Dodgers are well aware of this and chose to intentionally walk Pujols several times.  They are so fearful of Pujols’s bat they are willing to give him a free base to avoid something worse happening.

Bob Brenly was in agreement with the Dodgers’ decision to walk Pujols.  He also said just about every other manager would do the same thing.  Brenly used to be a major league manager so he has some insight into the decision-making process that goes on.  But one of the reasons Brenly gave for walking Pujols simply floored me.  Brenly said most managers wouldn’t want to pitch to Pujols because they don’t want to deal with the media if it turns out Pujols winds up beating you.  They’re scared to answer the media’s questions?  Nice…  If that’s how you manage, I hope you have your resume up to date!

The sad thing is that I don’t think Brenly is off-base with his comments.  You can find this sort of behavior all over.  Portfolio management is a great example.  How many “closet indexers” exist today?  We have written about the concept of Active Share before, and it is clear from the research that the number of truly actively managed portfolios has been dwindling over time.  The reason is simple: managers are afraid to deviate too much from their benchmark.  They’re afraid to take the risks they need to take in order to outperform their benchmark.  A manager who is truly active will go through stretches of poor relative performance.  That’s just part of the deal.  But research shows those managers are really the only ones who can provide alpha over time.  The “closet indexers” wind up underperforming by the amount of the fee over time.  Portfolio managers fall into the same trap as baseball managers.  They don’t want to deal with the short-term consequences of deviating from the crowd, even if it is the best thing to do over the long-term.

So is walking Pujols the right decision over time?  If you just look at his numbers versus Matt Holliday’s (the next batter) then you would probably say, “yes.”  This is what the announcers were discussing last night.  But I believe that is not the right question to be asking!  Pujols is a better hitter than Holliday, no question.  But is Pujols better than Holliday with Pujols on first base?  I’m not so sure the expected run differential is as great as people think.  But that is a question for the real stat-geeks!  The series is still far from being over so it will be interesting to see how this matchup plays out.

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August 7, 2009

The August edition of Ford Equity Research’s newsletter  (click here to learn more about Ford Equity Research) was in my inbox this morning.  I certainly don’t anticipate seeing their newsletter in my inbox like I anticipate seeing the SI Swimsuit edition in my mailbox, but that’s a different story.

Ford did have one interesting piece of information I wanted to pass along.  We have been noticing that relative strength strategies have been, for lack of a better term, disjointed recently.  What I mean  is some RS formulations are doing fine right now, but others are doing very poorly.  Longer term RS, which generally tests much more favorably than shorter term RS, has lagged the market by a wide margin.  But some of the shorter term models are doing much better.  Go figure.  It’s something I can’t remember seeing to this magnitude before.

A couple of months ago I looked at the returns of different RS factors over a YTD time horizon.  At that time the 1 Month RS factor was performing exceptionally well, and most of the other factors were lagging the market.  We were picking up the initial stages of the laggard rally.  It seems the 1 Month Factor, which has traditionally been a better mean-reversion factor than RS factor, seems to have reverted to form.  Now the 3 Month Factor is performing well, but the longer term stuff is still lagging.  Ford’s interpretation is in the graphic below:


In the “Best” category you find a bunch of Value factors and the 3 Month Momentum factor.  In the “Worst” category you have factors on either side of the 3 Month (1 Month, 12 Month) and some growth factors.  Very strange indeed.

It just goes to show how difficult it is right now to define a “strong” stock.  Using different RS factors can lead to dramatically different results.  We run several different types of accounts using different RS factors.  This week, for example, some of the styles outperformed the market by a good margin, and others were way behind.  Same philosophy, different measurement period with a dramatically different end product.  I wouldn’t expect this type of thing to continue, but a disjointed market like this can leave you scratching your head sometimes.

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RS In Depth

July 24, 2009

Most investors misunderstand—or maybe “over-simplify” is the right term– Relative Strength (RS).  I think a big part of the problem is the inherent desire to make RS sound more simple and basic than it really is.  Simple processes are easier for a novice to understand and accept as a viable investment strategy.  From the standpoint of the expert talking to a novice this is preferable because a novice doesn’t have the time or inclination to learn all of the complexities of the strategy.  In other words, a novice doesn’t need to know how to build a watch; they just need to know how to tell time.

The reality is that RS is much more complicated than people actually think.  When we talk to advisors they are often under the impression that a strong stock is a strong stock and there is only one way to measure strength.  It just so happens that the best measure of momentum, according to whoever you are talking to, is the method that person has been using lately!  RS is much more complicated than that.  There are numerous ways to measure the strength of a stock.

It is no different than a value strategy.  If you ask a room full of value managers what defines a “value stock,” you’re going to get a room full of answers.  Some people define value in terms of dividend yield, while others might use a price-to-cash flow model.  If you look at the portfolios of these two individuals, they are going to have different holdings even though they are both buying “cheap” stocks.  More importantly, the portfolios are going to perform differently during different parts of the market cycle.  The market rewards different factors to different degrees at different times.  Both factors might outperform over a long time horizon, but over any short time period the performance might be dramatically different.  There is nothing wrong with this!  If the dividend yield manager underperforms the cash flow manager during a given time period, it might not have anything to do with the skill of the two managers–it might simply be a matter of the market rewarding different value factors at different times.

Just as there are numerous value factors, relative strength can be calculated in many different ways.  Even something as straightforward as point & figure relative strength has numerous calculation methodologies.  You can use a 3.25% box size, 6.50% box size, the old Chartcraft standard boxes, a matrix (using any box size you want), or anything else you can dream up.  You can also favor an RS column change, and RS buy signal, or some combination of the two.  Dorsey Wright doesn’t even advocate one superior calculation method, as the research database makes all of these different calculation tools available to you.  The market will reward these different RS factors at different times.  Sometimes short term strength is rewarded more than long term strength; sometimes it’s the other way around.  It is no different than the value stock example discussed above. If one person uses a 12-month price return model to measure RS and another uses a 3-month price return model, their portfolios are going to hold different securities, have different turnover, and performance might be completely different during any given time period.  Both managers are using relative strength and buying strong stocks–they are just defining RS slightly differently.

For example, take a look at how several different RS calculation methodologies performed during the first 6 months of 2009.  As part of our research and ongoing process of continuous improvement, we track lots of RS factors in the Money Management office (besides our own proprietary measurement).  Here, we are just using some simple price-return-over-time models to illustrate our point.


The data in the table above shows that the way you define relative strength leads to very different return profiles over short time periods. All of these models outperform the broad market over long periods of time (we do have the data on that!) so they are all acceptable ways to determine strength.  However, over half a year the market has rewarded each factor very differently.

In addition to the variation in returns between RS factors there will also be variation in returns within an RS factor model.  Unless two managers buy the entire basket of high RS securities, their portfolios are going to look different.  This may seem obvious, but it is something many people don’t consider when evaluating an RS strategy.  While the chosen factor might be robust enough to deliver market-beating returns over time, any sub-set of the high RS basket might perform better or worse than any other sub-set over a given time period.  There can be massive variation between portfolios, even ones using the exact same RS factor.  Here, we use 100 different random trials to give you some sense of the possible variation.  The following table illustrates this point.


Using exactly the same factor, your returns could range from -21% to +8%, depending on which stocks in the group you ended up with.

We use a unique and robust testing protocol here in the Money Management office.  We try to make everything as “real world” as possible.  In a paper our portfolio staff wrote way back in 2005, we designed a random trade process that allowed us to randomly select high RS securities for a concentrated portfolio.  The theory is this: if you can select stocks at random from a predefined sub-set of a universe and still outperform a benchmark over time you have a remarkably robust process.  That random trade generation process was used to test the model in the table above.  You can see that with the same parameters and same investment universe there can be wildly different results over a short time period.  Over a longer testing period (1995 through mid-2009) all 100 random trials of the simple 6-month model outperformed the broad market.  But over a short period of time, it is quite possible to get stuck with a low-probability, lousy outcome.  (You can even get quarters where about half the trials outperform and half underperform.)

There is a tendency for investors, when they get stuck with a lousy outcome, to believe the process is broken.  It isn’t.  Most people want to believe they are in control of every situation so having to think in terms of probabilities often makes them uneasy.  However, it’s the reality of investing: not just in an RS strategy, but in every other strategy as well.

As you can see, there is variation in relative strength strategies just like there is variation in returns with every other investment strategy.  There isn’t one right or wrong way to calculate RS, although we do know that some ways are better than others!  (We happen to be fans of our proprietary method.)  Every calculation methodology has strengths and weaknesses.  Both the strengths and weaknesses will be exposed at some point during the market cycle.  There is no way to avoid this phenomenon.  There is no magic unicorn that’s going to appear and tell you the best way to identify a strong stock this week—and it will change next week anyway.  You’re better off to stop looking for the unicorn and spend your time testing for a robust method that will work over a long period of time, and then understanding everything you can about the tradeoffs you are making.  Our bias is to use exhaustive testing and data analysis to investigate and make decisions about the tradeoffs we need to live with.  Perhaps not everyone has the resources and programming ability to make that feasible, but you can spend time thinking about how your factor is constructed and where it might be vulnerable.  There are very few guarantees in finance, but I can make this one:  if you truly understand everything about the statistical parameters of your process, the next time someone tries to tell you your model is broken, you might have a knowing smile instead of a concerned frown.

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Big Day For The Laggards

July 23, 2009

So far this quarter we haven’t had a really big spread day between the laggards and leaders.  That is until today.  The low ranked RS stocks did much better than the highly ranked RS stocks today.  The broad market was up dramatically today, but all of the good performance came from the low ranked stocks.  Looking at the performance by RS decile you can see a steady progression in the performance as the RS ranks get worse.


You can see from the data that any decile that outperformed the universe average was at least in the bottom half of the ranks.  It is very rare to find an RS strategy that even holds stocks ranked in the bottom half, let alone buys them.  On a day like today, the more levered you are to the high RS stocks, the worse you performed.

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Efficient Markets

July 17, 2009

For markets to be efficient, investors must react rationally to new information as it enters the public domain.  One of the big problems with the Efficient Markets Hypothesis is that investors do some crazy things.  They are not always rational!

General Motors recently emerged from bankruptcy as two new entities.  There is the “new” General Motors Corp that will continue to sell cars, and there is also a Motors Liquidation Corp that owns all of the bad assets the new GM didn’t want anymore.  Matt Phillips wrote an interesting column for the Wall Street Journal (click here to read it) about what happened to GM stock on the day it emerged from bankruptcy.  People who still held the old stock were thrilled that GM came out of bankruptcy and bid the stock up 35%. But there was one small problem.  Those GM shares have nothing to do with the company that is still selling cars.  They represent ownership in Motors Liquidation Corp, which is still looking to sell all of GM’s bad assets.

The stock price reaction to GM emergence from bankruptcy was so irrational FINRA had to step in to protect all the investors that hadn’t bothered to consider what stock they owned.  They changed the name to Motors Liquidation Corp and the symbol to MTLQQ to avoid any confusion.  The stock promptly dropped 50%.

Investors do strange things.  Humans are not hard wired to be good investors.  As a result, there are market inefficiencies that a disciplined process can exploit over time.

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Asness on Momentum

July 10, 2009

Clifford Asness runs AQR Capital, a very large and successful hedge fund firm.  He has long been an advocate of using momentum (a similar factor to relative strength) as an investment factor.  He was on CNBC this morning and gave some insightful comments about using a momentum or relative strength strategy for investing.  The clip is well worth watching.

I think there are a couple of points he makes that we would totally agree with:

  • Don’t put all your eggs into an RS basket
  • Using a value strategy to “offset” an RS strategy is a good idea
  • RS works, and it has worked for years and years.  It has periods of underperformance, but it works in both bull and bear markets.
  • It is difficult to time an RS strategy (just like it is difficult to time the market)

Click here for a link to the CNBC video.

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July 8, 2009

I haven’t posted much in the way of performance of the RS quartiles and deciles because lately there hasn’t been much to write about.  The market has pulled back during the first 5 trading days of the quarter, and it seems as if everything has moved together.  In looking at the data you can see the top 2 deciles have outperformed the universe average, and the bottom decile has really taken it on the chin.  But other than that, all the other deciles are pretty close to the universe average.


The bottom decile has really been hurt by some of the low-priced laggards that are beginning to come back to earth.

This is actually a good thing given the laggard rally we had last quarter!  These periods of stabilization often occur before the spread between the high RS and low RS names begins to widen.  We certainly haven’t seen the relentless outperformance by the laggards that characterized the second quarter so far this quarter.

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Inside Today’s Performance

June 23, 2009

It was a unremarkable day for stocks today.  Not much action at all.  In terms of RS performance, both the highest and lowest deciles didn’t fare as well as the universe average.  The best performance came from the lower ranked stocks, but not from the lowest ranked stocks (if that makes any sense).


A lot of the poor performance in the top decile RS stocks came from the restaurant group.  I checked a couple of the names for a news item, but didn’t find anything that would usually cause such a large amount of underperformance from an entire group.  CAKE, BOBE, DRI, EAT, and CMG were all down substantially more than the market today.  When you look at the performance of the top 3 deciles for today you can see that in other areas the performance of high RS stocks was actually stronger than it looks.


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Today’s RS Performance

June 22, 2009

It was a terrible day for the stock market.  The broad market indexes all dropped 3% or more on concerns that the global economy might not be doing as well as people had thought.  It’s amazing how quickly sentiment can change!

When stocks were going well, the areas that were leading were the Banks, Energy, and Commodity groups.  These are all areas that would benefit from a growing global economy.  These are also the areas that have been hit the hardest during the current pullback.  High RS stocks have held up much better than the laggards over the last week or so.  Today’s performance showed a huge spread between the High RS stocks and Low RS stocks:


The top 6 deciles all outperformed the equal weighted universe.  The real damage was done in the bottom decile – 277 bps worse than the average.  When you break the universe out by decile and industry group you get a clear picture of where the real damage came from:

(click to enlarge image)

The Materials, Energy, and Financials stocks from the bottom decile got hammered today.  Energy stocks from our bottom decile were down over 10% on average.

Technology stocks as a group had marginal outperformance today.  QQQQ and SPY were down about the same amount, but in out equal weighted universe Technology outperformed by about 20-25 bps.  This outperformance is certainly not earth shattering, but what I find interesting is that Tech did well during the rally and seems to be holding its gains better than some of the other leading groups.  Even the bottom deciles in Technology performed OK today.

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“The Uninvestables”

June 19, 2009

The Quantitative Investing Team at Turner Investment Partners has a great report (click here for the full report) about the characteristics of the current rally off the bottom.  We have written about this laggard rally quite a bit in the last month.  It has been such a historic laggard rally that we are seeing a lot of research coming out that puts things into perspective.

Turner’s team focuses on small cap stocks, which is different than our universe.  We tend to focus on the mid to large cap universe, but it seems the small and micro caps have seen the exact same phenomenon.  They have a couple of fundamental factors they examine, but also include price momentum.  Their conclusion:

“In our analysis, this rally was an atypical, perverse phenomenon, a statistical fat tail, an investing anomaly.  We doubt that any time soon we will encounter a stock market rally that so lavishly rewards The Uninvestables….”

In financial markets it seems the “statistical fat tail” occurs much more often than people think, so I’m not sure if we will see this type of rally again soon or not.  The proverbial 100 year storm seems to come every 5 years or so.  But I do agree that with their analysis that this rally has been statistically out of the norm, and one that has been difficult to navigate if you invest in strong RS or momentum securities.

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