Rolling 10-Year Momentum Returns

February 26, 2014

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

momentum 02.26.14 Rolling 10 Year Momentum Returns

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

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

momentum2 02.26.14 Rolling 10 Year Momentum Returns

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

Past performance is no guarantee of future returns.

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It’s All At The Upper End

February 3, 2014

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

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

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

GICSMatrix zpse4a88b8f Its All At The Upper End

 (Click on image to enlarge)

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

GICS12Mth zpsb3fb152f Its All At The Upper End

 (click on image to enlarge)

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

GICS6Mth zps8af7edf9 Its All At The Upper End

(click on image to enlarge)

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

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Improving Sector Rotation With Momentum Indexes

January 21, 2014

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

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

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

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

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

Capture zps07daf1e3 Improving Sector Rotation With Momentum Indexes

 (Click Image To Enlarge)

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

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

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

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DWA Technical Leaders Webinar: Q1 Updates

January 20, 2014

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

Follow this link for a replay of this webinar.

bw012114  DWA Technical Leaders Webinar: Q1 Updates

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DWA Technical Leaders Index Trade Profiles

January 6, 2014

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

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

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

TLTable zps9d3df2ae DWA Technical Leaders Index Trade Profiles

 

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

Here is the same data from the table shown graphically:

TLChart zps7c20d6fe DWA Technical Leaders Index Trade Profiles

 

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

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

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

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60/40 Portfolio Subjected To Historical Data

December 30, 2013

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

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

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

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

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

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

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

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

From John Lewis’ white paper:

factor summary1 60/40 Portfolio Subjected To Historical Data

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

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

dwtfx1 60/40 Portfolio Subjected To Historical Data

Source: Morningstar

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

Past performance is no guarantee of future returns.  

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More Supporting Data For Momentum

December 30, 2013

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

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

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

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

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Podcast: A Profile of our Growth Portfolio

November 25, 2013

A Profile of our Growth Portfolio

Mike Moody and Andy Hyer

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Momentum in Rising Rate Environments

November 19, 2013

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

PSConnection zps6dc00387 Momentum in Rising Rate Environments

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Momentum and Dividends in Rising Rate Environments

October 14, 2013

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

10YR Yield1 Momentum and Dividends in Rising Rate Environments

Source: Dorsey Wright

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

10 Year Treasury Rates Momentum and Dividends in Rising Rate Environments

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

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

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

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

comparison Momentum and Dividends in Rising Rate Environments

Source: What Works On Wall Street

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

Past performance is no guarantee of future returns.

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Long-Only Momentum

October 4, 2013

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

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

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

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

long onlymomentum zps14b7ad7e Long Only Momentum

Source: Optimal Momentum

(click on image to enlarge)

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

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

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

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

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

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

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

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

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

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

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Momentum Coming of Age

September 27, 2013

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

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

momentum Momentum Coming of Age

An EMH advocate reviews the momentum data

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

Again, from Wesley Gray:

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

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

trend Momentum Coming of Age

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

Under-reaction phase:

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

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

Over-reaction phase:

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

Gray concludes:

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

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

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The Case for Systematic Decision-Making

September 25, 2013

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

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

September 17, 2013

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

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

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

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

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Relative Strength Environments

August 9, 2013

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

RS Environments Relative Strength Environments

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Factor Performance and Factor Failure

July 30, 2013

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

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

factorperformance zps037b7505 Factor Performance and Factor Failure

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

Yep, the one at the top is momentum.

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

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

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

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

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

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

factorfailure zps55a7cd1c Factor Performance and Factor Failure

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

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

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Relative Strength Spread

July 23, 2013

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

Capture5 Relative Strength Spread

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212 Years of Price Momentum

July 23, 2013

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

Abstract:

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

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

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

HT: Abnormal Returns and Turnkey Analyst

Past performance is no guarantee of future returns.

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How to Pick a Winner

July 23, 2013

Scientific American takes a look at the best way to select a winner:

Given the prevalence of betting and the money at stake, it is worth considering how we pick sides. What is the best method for predicting a winner? One might expect that, for the average person, an accurate forecast depends on the careful analysis of specific, detailed information. For example, focusing on the nitty-gritty knowledge about competing teams (e.g., batting averages, recent player injuries, coaching staff) should allow one to predict the winner of a game more effectively than relying on global impressions (e.g., overall performance of the teams in recent years). But it doesn’t.

In fact, recent research by Song-Oh Yoon and colleagues at the Korea University Business School suggests that when you zero in on the details of a team or event (e.g., RBIs, unforced errors, home runs), you may weigh one of those details too heavily. For example, you might consider the number of games won by a team in a recent streak, and lose sight of the total games won this season. As a result, your judgment of the likely winner of the game is skewed, and you are less accurate in predicting the outcome of the game than someone who takes a big picture approach. In other words, it is easy to lose sight of the forest for the trees.

So often people that consider employing relative strength strategies, which measure overall relative price performance of securities rather than delving into the weeds with various accounting level details, feel like they must not be doing an adequate job of analyzing the merits of a given security.  As pointed out in  this research, the best results came from focusing on less data, not more.

Whether trying to select a winner in sports or in the stock market, it is important to remember that “detailed analysis fog the future.”

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Alternative Beta

July 22, 2013

…has been discovered by the Wall Street Journal.  Recently, they wrote an article about better ways to index—alternative beta—and referenced a study by Cass Business School.  (We wrote about this study here in April.)

Here’s the WSJ’s take on the Cass Business School study:

The Cass Business School researchers examined how 13 alternative index methodologies would have performed for the 1,000 largest U.S. stocks from 1968 to 2011.

All 13 of the alternative indexes produced higher returns than a theoretical market-cap index the researchers created. While the market-cap index generated a 9.4% annualized return over the full period, the other indexes delivered between 9.8% and 11.4%. The market-cap-weighted index was the weakest performer in every decade except the 1990s.

The most interesting part of the article, to me, was the discussion of the growing acceptance of alternative beta.  This is truly exciting.

Indeed, a bevy of funds tracking alternative indexes have been launched in recent years. And their popularity is soaring: 43% of inflows into U.S.-listed equity exchange-traded products in the first five months of 2013 went to products that aren’t weighted by market capitalization, up from 20% for all of last year, according to asset manager BlackRock Inc.

And then there was one mystifying thing: although one of the best-performing alternative beta measures is relative strength (“momentum” to academics), relative strength was not mentioned in the WSJ article at all!

Instead there was significant championing of fundamental indexes.  Fundamental indexes are obviously a valid form of alternative beta, but I am always amazed how relative strength flies under the radar.  (See The #1 Investment Return Factor No One Wants to Talk About.)  Indeed, as you can see from the graphic below, the returns of two representative ETFs, PRF and PDP are virtually indistinguishable.  One can only hope that relative strength will eventually gets its due.

PDPvPRF zps323d99f1 Alternative Beta

The performance numbers above are pure price returns, based on the applicable index not  inclusive of dividends, fees, commissions, or other expenses. Past performance not indicative of future results.  Potential for profits accompanied by possibility of loss.  See www.powershares.com for more information.  

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Academic Perspective on Momentum-Based Investing Webinar

July 2, 2013

Come listen to an academic perspectives as to why momentum-based investing makes sense, and how you may be able to take advantage of it, from one of the pioneers in momentum and relative strength investing, Dorsey Wright & Associates.  This webinar features Tom Dorsey and Andy Hyer.

Click here for a replay of the webinar.

powershares Academic Perspective on Momentum Based Investing Webinar

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DWTFX Tops Peers

June 14, 2013

Earlier this year, we featured an excellent resource published by our partners at Arrow Funds called Relative Strength Turns. You can access a PDF of the brochure by clicking here. This research discusses the type of behavior you can expect from a relative strength driven strategy in various market cycles and it makes the case for why relative strength strategies may experience favorable returns in the years ahead.

Interestingly, we have seen this corroborated by the performance of the Arrow DWA Tactical Fund, which employs a largely unconstrained application of relative strength to multiple asset classes. With YTD performance of 10.65% through 6/13/13, it is outperforming 99% of its peers in the Morningstar World Allocation category.

DWTFX DWTFX Tops Peers

Past performance is no guarantee of future returns.

This strategy is available in the Arrow DWA Tactical Fund (DWTFX) and also as a separately managed account as our Global Macro strategy, which is available on a number of major platforms, including the Wells Fargo Masters and DMA platforms.

Please click here and here for disclosures.

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From the Archives: If You Miss the 10 Best Days

June 7, 2013

We’ve all seen numerous studies that purport to show how passive investing is the way to go because you don’t want to be out of the market for the 10 best days.  No one ever mentions that the “best days” most often occur during the declines!

It turns out that the majority of the best days and the worst days occur near one another, during the declines.  Why?  Because the market is more volatile during declines.  It is true that the market goes down 2-3x as fast as it goes up.  (World Beta has a nice post on this topic of volatility clustering, which is where this handy-dandy table comes from.)

 From the Archives: If You Miss the 10 Best Days

from World Beta

You can see how volatility increases and the number of days with daily moves greater than 2.5% really spikes when the market is in a downward trend.  It would seem to be a very straightforward proposition to improve your returns simply by avoiding the market when it is in a downtrend.

However, not every strategy can be improved by going to cash.  Think about the math: if your investing methodology makes enough extra money on the good days to offset the bad days, or if it can make money during a significant number of the declines, you might be better off just gritting your teeth during the declines and banking the higher returns.  Although the table above suggests it should help, a simple strategy of exiting the market (i.e., going to cash) when it is below its 200-day moving average may not always live up to its theoretical billing.

 From the Archives: If You Miss the 10 Best Days

 From the Archives: If You Miss the 10 Best Days

click to enlarge

Consider the graphs above.  (The first graph uses linear scaling; the second uses logarithmic scaling for the exact same data.)  This test uses Ken French’s database to get a long time horizon and shows the returns of two portfolios constructed with market cap above the NYSE median and in the top 1/3 for relative strength.  In other words, the two portfolios are composed of mid- and large-cap stocks with good relative strength.  The only difference between the two portfolios is that one (red line) goes to cash when it is below its 200-day moving average.  One portfolio (blue line) stays fully invested.  The fully invested portfolio turns $100 into $49,577, while the cash-raising portfolio yields only $26,550.

If you would rather forego the extra money in return for less volatility, go right ahead and make that choice.  But first stack up 93 boxes of  Diamond matches so that you can burn 23,027 $1 bills, one at a time, to represent the difference–and then make your decision.

 From the Archives: If You Miss the 10 Best Days

The drawdowns are less with the 200-day moving average, but it’s not like they are tame–equities will be an inherently volatile asset class as long as human emotions are involved.  There are still a couple of drawdowns that are greater than 20%.  If an investor is willing to sit through that, they might as well go for the gusto.

As surprising as it may seem, the annualized return over a long period of time is significantly higher if you just stay in the market and bite the bullet during train wrecks–and even two severe bear markets in the last decade have not allowed the 200-day moving average timer to catch up.

At the bottom of every bear market, of course, it certainly feels like it would have been a good idea (in hindsight) to have used the 200-day moving average to get out.  In the long run, though, going to cash with a high-performing, high relative strength strategy might be counterproductive.  When we looked at 10-year rolling returns, the fully invested high relative strength model has maintained an edge in returns for the last 30 years running.

 From the Archives: If You Miss the 10 Best Days

click to enlarge

Surprising, isn’t it?  Counterintuitive results like this are one of the reasons that we find testing so critical.  It’s  easy to fall in line with the accepted wisdom, but when it is actually put to the test, the accepted wisdom is often wrong.  (We often find that even when shown the test data, many people refuse, on principle, to believe it!  It is not in their worldview to accept that one of their cherished beliefs could be false.)  Every managed portfolio in our Systematic RS lineup has been subjected to heavy testing, both for returns and–and more importantly–for robustness.  We have a high degree of confidence that these portfolios will do well in the long run.

—-this article originally appeared 3/5/2010.  We find that many investors continue to refuse, on principle, to believe the data!  If you have a robust investment method, the idea that you can improve your returns by getting out of the market during downturns appears to be false.  (Although it could certainly look true for small specific samples.  And, to be clear, 100% invested in a volatile strategy is not the appropriate allocation for most investors.)  Volatility can generally be reduced somewhat, but returns suffer.  One of our most controversial posts ever—but the data is tough to dispute.

In more recent data, the effect can be seen in this comparison of an S&P 500 ETF and an ETN that switches between the S&P 500 and Treasury bills based on a 200-day moving average system.  The volatility has been muted a little bit, but so have the returns.

trendpilot zps9227da43 From the Archives: If You Miss the 10 Best Days

(click on image to enlarge)

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From the Archives: Perfect Sector Rotation

June 4, 2013

CXO Advisory has a very interesting blog piece on this topic.  They review an academic paper that looks at the way conventional sector rotation is done.  Typically, various industry sectors are categorized as early cycle, late cycle, etc. and then you are supposed to own those sectors at that point in the business cycle.  Any number of money management firms (not including us) hang their hat on this type of cycle analysis.

In order to determine the potential of traditional sector rotation, the study assumes that you get to have perfect foresight into the business cycle and then you rotate your holdings with the conventional wisdom of when various industries perform best.  A couple of disturbing things crop up, given that this is the best you could possibly do with this system.

1) You can squeak by with about 2.3% annual outperformance if you had a crystal ball.  If you are even a month or two early or late on the cycle turns, your performance is statistically indistinguishable from zero.

2) 28 of the 48 industries studied (58.3%) underperformed during the times when they were supposed to perform well.  There’s obviously enough noise in the system that a sector that is supposed to be strong or weak during a particular part of the cycle often isn’t.

CXO notes, somewhat ironically:

Note that NBER can take as long as two years after a turning point to designate its date and that one business cycle can be very different from another.

In other words, it’s clear that traditional business cycle analysis is not going to help you.  You won’t be able to forecast the cycle turning points accurately and the cycles differ so much that industry performance is not consistent.

Sector rotation using relative strength is a big contrast to this.  Relative strength makes no a priori assumptions about which industries are going to be strong or weak at various points in the business cycle.  A systematic strategy just buys the strong sectors and avoids the weak ones.  Lots of studies show that significant outperformance can be earned using relative strength (momentum) with absolutely no insight into the business cycle at all, including some studies done by CXO Advisory.  Tactical asset allocation is finally coming into its own and various ways of implementing are available.  Business cycle forecasting does not appear to be a feasible way to do it, but relative strength certainly is!

—-this article originally appeared 3/30/2010.  Although the link to CXO Advisory is no longer live, you can get the gist of things from the article.  Things don’t always perform in the expected fashion, and paying attention to relative strength can be some protection from the problem.  Instead of making assumptions about strong or weak performance, relative strength just adapts.

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The Wonders of Momentum

April 18, 2013

Relative strength investors will be glad to know that James Picerno’s Capital Spectator blog has an article on the wonders of momentum.  He discusses the momentum “anomaly” and its history briefly:

Momentum is one of the oldest and most persistent anomalies in the financial literature. The tendency of positive or negative returns to persist for a time seems like a ridiculously simple predictor, but it works. There’s an ongoing debate about why it works, but the results in numerous tests speak loud and clear. Unlike many (most?) reported sources of alpha, the market-beating and risk-lowering results linked to momentum strategies appear to be immune to arbitrage.

Informally, it’s fair to say that investors have been exploiting momentum in various forms for as long as humans have been trading assets. Formally, the concept dates to at least 1937, when Alfred Cowles and Herbert Jones reviewed momentum in their paper “Some A Priori Probabilities in Stock Market Action.” In the 21st century, an inquiring reader can easily find hundreds of papers on the subject, most of it published in the wake of Jegadeesh and Titman’s seminal 1993 work: “Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency,” which marks the launch of the modern age of momentum research.

I think his observation that momentum (relative strength to us) has been around since humans have been trading assets is spot on.  It’s important to keep that in mind when thinking about why relative strength works—and why it has been immune to arbitrage.  He writes:

Momentum, it seems, is one of the rare risk factors with features that elude so many other strategies: It’s persistent, conceptually straightforward, robust across asset classes, and relatively easy to implement. It’s hardly a silver bullet, but nothing else is either.

The only mystery: Why are we still talking about this factor in glowing terms? We still don’t have a good answer to explain why this anomaly hasn’t been arbitraged away, or why it’s unlikely to meet an untimely demise anytime soon.

Mr. Picerno raises a couple of important points here.  Relative strength does have a lot of attractive features.  The reason it is not a silver bullet is that it underperforms severely from time to time.  Although that is also true of other strategies, I think the periodic underperformance is one of the reasons why the excess returns have not been arbitraged away.

Although he suggests we don’t have a good answer about why momentum works, I’d like to offer my explanation.  I don’t know if it’s a good answer or not, but it’s what I’ve arrived at after years of research and working with relative strength portfolios—not to mention a degree in psychology and a couple of decades of seeing real investors operate in the market laboratory.

  • Relative strength straddles both fundamental analysis and behavioral finance.
  • High relative strength securities or assets are generally strong because they are undergoing fundamental improvement or are in a sweet spot for fundamentals.  In other words, if oil prices are trending strongly higher, it’s not surprising that certain energy stocks are strong.  That’s to be expected from the fundamentals.  Often there is improvement at the margin, perhaps in revenue growth or operating margin—and that improvement is often underestimated by analysts.  (Research shows that investors are more responsive to changes at the margin than to the absolute level of fundamental factors.  For example, while Apple’s operating margin grew from 2.2% in 2003 to 37.4% in 2012, the stock performed beautifully.  Even though the operating margin is expected to be in the 35% range this year—which is an extremely high level—the stock is getting punished.  Valero’s stock price plummeted when margins went from 10.0% in 2006 to 2.4% in 2009, but has doubled off the low as margins rebounded to 4.8% in 2012.  Apple’s operating margin on an absolute basis is drastically higher than Valero’s, but the delta is going the wrong way.)  High P/E multiples can often be maintained as long as margin improvement continues, and relative strength tends to take advantage of that trend.  Often these trends persist much longer than investors expect.
  • From the behavioral finance side, social proof helps reinforce relative strength.  Investors herd and they gravitate toward what is already in motion, and that reinforces the price movement.  They are attracted to the popular and repelled by the unpopular.
  • Periodic bouts of underperformance help keep the excess returns of relative strength high.  When momentum goes the wrong way it can be ugly.  Perhaps margins begin to contract and financial results are worse than analysts expect.  The security has been rewarded with a high P/E multiple, which now begins to unwind.  The herd of investors begins to stampede away, just as they piled in when things were going well.  Momentum can be volatile and investors hate volatility.  Stretches of underperformance are psychologically painful and the unwillingness to bear pain (or appropriately manage risk) discourages investors from arbitraging the excess returns away.

In short, I think there are multiple reasons why relative strength works and why it is difficult to arbitrage away the excess returns.  Those reasons are both fundamental and behavioral and I suspect will defy easy categorization.  Judging from my morning newspaper, human nature doesn’t change much.  Until it does, markets are likely to work the same way they always have—and relative strength is likely to continue to be a powerful return factor.

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