July SMA Performance Update

August 1, 2016

The broad U.S. equity market moved through resistance to all-time highs in July.  International equity markets also had a strong month.   All 7 of our Systematic RS portfolios made gains for the month and most all remain well ahead of their benchmarks for the year.  See detailed performance below.

Also, click here for a Q&A with our Senior Portfolio Manager, John Lewis, CMT.

July performance

To receive the brochure for these portfolios, please e-mail andy@dorseywright.com or call 626-535-0630.  Click here to see the list of platforms where these separately managed accounts are currently available.

Total account performance shown is total return net of management fees for all Dorsey, Wright & Associates managed accounts, managed for each complete quarter for each objective, regardless of levels of fixed income and cash in each account.  Information is from sources believed to be reliable, but no guarantee is made to its accuracy.  This should not be considered a solicitation to buy or sell any security.  Past performance should not be considered indicative of future results.  The S&P 500 is a stock market index based on the market capitalizations of 500 leading companies publicly traded in the U.S. stock market, as defined by Standard & Poor’s.  The Barclays Aggregate Bond Index is a broad base index, maintained by Barclays Capital, and is used to represent investment grade bonds being traded in the United States.  The 60/40 benchmark is 60% S&P 500 Total Return Index and 40% Barclays Aggregate Bond Index.  The NASDAQ Global ex US Total Return Index is a stock market index that is designed to measure the equity market performance of markets outside of the United States and is maintained by Nasdaq.  The Dow Jones Moderate Portfolio Index is a global asset allocation benchmark.  60% of the benchmark is represented equally with nine Dow Jones equity indexes.  40% of the benchmark is represented with five Barclays Capital fixed income indexes.  Each investor should carefully consider the investment objectives, risks and expenses of any Exchange-Traded Fund (“ETF”) prior to investing. Before investing in an ETF investors should obtain and carefully read the relevant prospectus and documents the issuer has filed with the SEC.  ETFs may result in the layering of fees as ETFs impose their own advisory and other fees.  To obtain more complete information about the product the documents are publicly available for free via EDGAR on the SEC website (http://www.sec.gov).  There are risks inherent in international investments, which may make such investments unsuitable for certain clients. These include, for example, economic, political, currency exchange, rate fluctuations, and limited availability of information on international securities.

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Weekly RS Recap

August 1, 2016

The table below shows the performance of a universe of mid and large cap U.S. equities, broken down by relative strength decile and quartile and then compared to the universe return.  Those at the top of the ranks are those stocks which have the best intermediate-term relative strength.  Relative strength strategies buy securities that have strong intermediate-term relative strength and hold them as long as they remain strong.

Last week’s performance (7/25/16 – 7/29/16) is as follows:

ranks

This example is presented for illustrative purposes only and does not represent a past or present recommendation.  The relative strength strategy is NOT a guarantee.  There may be times where all investments and strategies are unfavorable and depreciate in value.  The performance above is based on pure price returns, not inclusive of dividends, fees, or other expenses.  Past performance is not indicative of future results.  Potential for profits is accompanied by possibility of loss.

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Managing Lumpy

July 26, 2016

Ben Carlson highlights perhaps the single biggest frustration for investors:

The thing is, stock market returns have always been lumpy. Historical returns always look great on paper, but the actual experience of living through them is never really close to the average results. Average is typically the exception, not the rule.

He used the following chart to illustrate just how lumpy the returns in the S&P 500 have been over time:

S&P 500

Yet, the S&P 500 TR Index had 9.5% annualized return over this time frame.  What’s not to like about those average results?

This makes me think of the following visual depiction of the same reality:

reality

Source: Twitter, @ThinkingIp

Perhaps some clients can be coached into a passive long-term buy and hold approach to investing and can avoid buying high and selling low.  However, for many investors, the lumpy nature of the markets is just too much for them to handle.  Such basics as diversification by asset class and by strategy can help smooth out the ride.

We have found that there is also a place for a strategy that has the ability to raise cash to try to help buffer some of the market downturns.  Psychologically, this can help as clients tend to take comfort in knowing that there is some ability to play defense in their portfolio.  One of the seven strategies in our family of Systematic Relative Strength portfolios is called Growth and this portfolio invests in 20-25 U.S. mid and large cap equities when it is fully invested.  However, it also has the ability to raise up to 50 percent cash in the portfolio when market conditions dictate.

Over time, the performance of the strategy has been better than that of the S&P 500, with less volatility than the S&P 500:

growth 07.14.16

growth stats

Ultimately, good financial advisors help their clients successfully navigate the “lumpiness” of market returns though a combination of coaching/hand holding as well as designing an asset allocation that will create a risk/return experience that the clients can handle.  If you think your clients could benefit from making our Growth portfolio part of that plan, please contact Andy Hyer at 626-535-0630 or andyh@dorseymm.com.  Our family of Systematic Relative Strength portfolios are available on a large and growing number of SMA and UMA platforms.

The performance represented above is based on monthly performance of the Systematic Relative Strength Growth Model.  Net performance shown is total return net of management fees, commissions, and expenses for all Dorsey, Wright & Associates managed accounts, managed for each complete quarter for each objective, regardless of levels of fixed income and cash in each account.  The advisory fees are described in Part 2A of the adviser’s Form ADV.  The starting values on 12/31/2006 are assigned an arbitrary value of 100 and statement portfolios are revalued on a trade date basis on the last day of each quarter.  All returns since inception of actual Accounts are compared against the S&P 500 Index.  The S&P 500 is a stock market index based on the market capitalizations of 500 leading companies publicly traded in the U.S. stock market, as defined by Standard & Poor’s.  The performance information is based on data supplied by the Manager or from statistical services, reports, or other sources which the Manager believes are reliable.  Past performance does not guarantee future results. In all securities trading, there is a potential for loss as well as profit. It should not be assumed that recommendations made in the future will be profitable or will equal the performance as shown. Investors should have long-term financial objectives when working with Dorsey, Wright & Associates.

 

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Weekly RS Recap

July 25, 2016

The table below shows the performance of a universe of mid and large cap U.S. equities, broken down by relative strength decile and quartile and then compared to the universe return.  Those at the top of the ranks are those stocks which have the best intermediate-term relative strength.  Relative strength strategies buy securities that have strong intermediate-term relative strength and hold them as long as they remain strong.

Last week’s performance (7/18/16 – 7/22/16) is as follows:

ranks

Performance by sector for the week is shown below:

sector ranks

This example is presented for illustrative purposes only and does not represent a past or present recommendation.  The relative strength strategy is NOT a guarantee.  There may be times where all investments and strategies are unfavorable and depreciate in value.  The performance above is based on pure price returns, not inclusive of dividends, fees, or other expenses.  Past performance is not indicative of future results.  Potential for profits is accompanied by possibility of loss.

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Sector Performance

July 22, 2016

The table below shows performance of US sectors over the trailing 12, 6, and 1 month(s).  Performance updated through 7/21/16.

sector

The performance above is based on pure price returns, not inclusive of dividends, fees, or other expenses.  Past performance is not indicative of future results.  Potential for profits is accompanied by possibility of loss.  Source: iShares

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Combining Different Momentum Factors

July 18, 2016

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

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

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

Perf

(Click To Enlarge)

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

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

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

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

RndPerf

(Click To Enlarge)

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

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

RndPerfPnF

(Click To Enlarge)

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

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

 

The returns used within this article are the result of a back-test using indexes that are not available for direct investment.  Returns do include dividends, but do not include transaction costs.  Back-tested performance is hypothetical (it does not reflect trading in actual accounts) and is provided for informational purposes to illustrate the effects of the discussed strategyduring a specific period.  Back-tested performance results have certain limitations.  Such results do not represent the impact of material economic and market factors might have on an investment advisor’s decision making process if the advisor were actually managing client money.  Back-testing performance also differs from actual performance because it is achieved through retroactive application of a model investment methodology designed with the benefit of hindsight.  Dorsey, Wright & Associates believes the data used in the testing to be from credible, reliable sources, however; Dorsey, Wright & Associates, LLC (collectively with its affiliates and parent company, “DWA”) makes no representation or warranties of any kind as to the accuracy of such data. Past performance is not indicative of future results.  Potential for profits is accompanied by possibility of loss.

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Combining Equity Momentum (PDP) & Managed Futures (CSAIX)

July 11, 2016

One of the more surprising themes for many market participants thus far into 2016 has been the impressive strength of the commodities asset class.   What started off as a sharp rally from the lows in the energy sector quickly flowed into other commodities such as precious metals, softs (sugar) and ags (soybeans).   A lack of trend in US equities has helped funds flow into commodities as market participants search for yield in other asset classes.   Another tailwind for commodities has been the range bound activity in the US Dollar.  This in turn makes them cheaper to export overseas and therefore increases demand.

Although it may not be the most popular asset class amongst traditional fund managers, there is certainly demand for commodities markets among institutional traders.   For example, managed money (CTA’s and Macro Hedge Funds) is often focused on looking for trending markets in both foreign exchange and commodities to allocate toward their portfolios.  Given the size and leverage these funds have access too, the commodities and foreign exchange markets are often subject to large trending moves which can continue for extended periods of time.  Although momentum and trend following are often used interchangeably, they actually differ in the fact momentum (ex. 12 mo. trailing return) is typically thought of as relative while trend following techniques (ex. moving averages) are more absolute in nature.  Our main point in mentioning CTA’s and Hedge Funds is that given their ability to rotate through various asset classes and take both sides of the market (long or short) they typically have a negative correlation to long only equity managers.

Assuming the negative correlation between the two strategies holds true, combining a long only equity momentum portfolio with some type of managed futures strategy would certainly seem make sense in terms of reducing volatility and drawdowns while maintaining alpha above a related benchmark.  Let’s investigate this matter further by using the Power Shares DWA Momentum Portfolio (PDP) and combining it into a portfolio with the Credit Suisse Managed Futures Strategy Fund (CSAIX).   Note we will be using the returns of the underlying index (CSTHFMF0 – Credit Suisse Hedge Fund Index Managed Futures) in order to pull the historical data for CSAIX since its inception was 9/28/12.

Here is a brief summary of each strategy side by side.     As shown below, PDP outperforms both the Credit Suisse Hedge Fund Index Managed Futures Strategy CSAIX and SPX over the allotted time frame in this study.  However, as we saw in our previous posts it does so with slightly elevated volatility.   All other performance metrics aside, the main concept we want to emphasize is the differences in returns each year between PDP (momentum) and CSAIX (managed futures).  The most obvious example is 2008, when CSAIX posted an impressive 18.33% gain while both PDP and the SPX suffered steep double digit losses.

Click on graphic for larger version

ANNUAL RETURNS

PDP inception date: March 1, 2007, CSAIX inception date: Sept 28, 2012 – data prior to inception is based on a back-test of the underlying indexes Please see the disclosures for important information regarding back-testing.  PDP returns do not include dividends.  Returns do not include all potential transaction costs.  Past performance is not indicative of future results.  Potential for profits is accompanied by possibility of loss. 

The graphic below shows a comparison of annual returns using the time period 1998 and 2015 in which the correlation of excess returns between momentum and managed futures  was  (-0.91)  A few of the outlier years to take note of which contain major differences in performance are 1999, 2008, 2009, and finally 2014.   Some of the largest differences in performance can be attributed to periods of heighted equity market volatility (ex 2008).   Excess volatility tends to create more opportunities for managed futures strategies.  On the other hand, the past 5 years (with the exception of 2014) showed equity momentum outperforming managed futures as the stock market continued its strong bull market while many commodities and foreign exchange rates were lacking volatility and any type of sustained trend (up or down).

Click on graphic for larger version

correlations

PDP inception date: March 1, 2007, CSAIX inception date: Sept 28, 2012 – data prior to inception is based on a back-test of the underlying indexes Please see the disclosures for important information regarding back-testing.  PDP returns do not include dividends.  Returns do not include all potential transaction costs.  Past performance is not indicative of future results.  Potential for profits is accompanied by possibility of loss. 

The table below goes through similar allocation structure of how we set up our low volatility and value portfolios in combination with momentum in previous posts.  Our main goal here is to stay consistent and create a robust process that has very few moving parts.  The portfolios are all re-balanced annually but each allocation remains consistent each year.   The main goal is to emphasize the benefit of combining two negatively correlated strategies in order to take advantage of the performance differences each will achieve throughout different market cycles.

Click on graphic for larger version

HISTORICAL ALLOCATIONS

The returns above are based on hypothetical back-tests of the various allocation options.  PDP inception date: March 1, 2007, CSAIX (inception date: Sept 28, 2012 – data prior to inception is based on a back-test of the underlying indexes. Back-tested performance results have certain limitations.  Such results do not represent the impact of material economic and market factors might have on an investment advisor’s decision-making process if the advisor were actually managing client money.  Back-testing performance also differs from actual performance because it is achieved through retroactive application of a model investment methodology designed with the benefit of hindsight.  PDP returns do not include dividends.  Returns do not include all potential transaction costs.  Past performance is not indicative of future results.  Potential for profits is accompanied by possibility of loss. 

Let’s take it a step further and add one simple risk metric to our portfolio and see if we can reduce volatility even further.   Over time, it’s typically been the case that a long only equity momentum/trend following based strategy tends to perform better while the SPX is above its 200 day moving average.   On the flip side, when the SPX is below its 200 day moving average, periods of heightened volatility are more frequent and can lead to steep draw downs in these portfolios.   This is not always the case but over the years research has shown that the 200 day moving average is often considered a reliable proxy for a risk on/risk off environment.

Interestingly enough, this “risk off” environment is often where managed futures strategies thrive and tend to see their best results.  One main reason for this is the abundance of “fat tail” trades that seem to occur during these market cycles.  The below table compares a model we have created using a 200 day moving average as a risk proxy to determine how we will allocate our portfolio using equity momentum (PDP) and a managed futures strategy (CSAIX).   The allocation will be 80% equity momentum/20% managed futures when the S&P is above its 200 day moving average and 80% managed futures/20% equity momentum when it is below.  In order to reduce turnover, the portfolio will only be re-balanced on a month-end basis.

Click on graphic for larger version

CSAIX

PDP inception date: March 1, 2007, CSAIX  inception date: Sept 28, 2012 – data prior to inception is based on a back-test of the underlying indexes Performance of the switching strategy is the result of back-testing.  Back-tested performance results have certain limitations.  Such results do not represent the impact of material economic and market factors might have on an investment advisor’s decision-making process if the advisor were actually managing client money.  Back-testing performance also differs from actual performance because it is achieved through retroactive application of a model investment methodology designed with the benefit of hindsight. PDP returns do not include dividends.  Returns do not include all potential transaction costs.  Past performance is not indicative of future results.  Potential for profits is accompanied by possibility of loss. 

Let’s take it a step further and add one simple risk metric to our portfolio and see if we can reduce volatility even further.   Over time, it’s typically been the case that a long only equity momentum/trend following based strategy tends to perform better while the SPX is above its 200 day moving average.   On the flip side, when the SPX is below its 200 day moving average, periods of heightened volatility are more frequent and can lead to steep draw downs in these portfolios.   This is not always the case but over the years research has shown that the 200 day moving average is often considered a reliable proxy for a risk on/risk off environment.

Interestingly enough, this “risk off” environment is often where managed futures strategies thrive and tend to see their best results.  One main reason for this is the abundance of “fat tail” trades that seem to occur during these market cycles.  The below table compares a model we have created using a 200 day moving average as a risk proxy to determine how we will allocate our portfolio using equity momentum (PDP) and a managed futures strategy (CSAIX).   The allocation will be 80% momentum/20% managed futures when the S&P is above its 200 day moving average and 20% equity/80% managed futures when it is below.  In order to reduce turnover, the portfolio will only be re-balanced on a month-end basis.

 

 

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Q3 2016 PowerShares DWA Momentum ETFs

July 5, 2016

The PowerShares DWA Momentum Indexes are reconstituted on a quarterly basis.  These indexes are designed to evaluate their respective investment universes and build an index of stocks with superior relative strength characteristics.   This quarter’s allocations are shown below.

PDP: PowerShares DWA Momentum ETF

 pdp 

DWAS: PowerShares DWA Small Cap Momentum ETF

dwas

DWAQ: PowerShares DWA NASDAQ Momentum ETF

dwaq

PIZ: PowerShares DWA Developed Markets Momentum ETF

piz

PIE: PowerShares DWA Emerging Markets Momentum ETF

pie

Source: Dorsey Wright, MSCI, Standard & Poor’s, and NASDAQ, Allocations subject to change

We also apply this momentum-indexing methodology on a sector level:

sector-momentum

See www.powershares.com for more information.  

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

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Commodity Turns

June 28, 2016

With commodities in focus (having moved to the number 2 spot of DALI in the past week), we wanted to draw your attention to some interesting research published by Arrow Funds.

DALI

Source: Dorsey Wright, as of 6/23/16

From their piece Commodity Turns:

The table below illustrates the 10-year, 5-year, and 3-year annualized returns for eight major asset classes through December 31, 2015.  The table also shows the average rolling annualized returns and the worst single rolling time periods from January 1970 through December 2015.

Commodities had a 10-year annualized return of -10.56% as of 12/31/2015, which also happens to be the worst 10-year rolling period since 1970.  But since 1970, commodities have actually averaged 8.38% across all rolling 10-year periods—a difference of 18.94% between the recent 10-year return and the rolling 10-year average.  For those who subscribe to the idea of “reversion to the mean” where extreme returns revert back to the average, a case could be made for commodities to turn in a more favorable direction.

reversion the mean

Like many asset classes, commodity returns go through periods of ups and downs.  Sometimes they have been the best performing asset class, sometimes the worst, and often somewhere in between.  With declining oil prices, a drop in precious metals and slowing global growth, commodities have been out of favor with investors for several years.  Due to the historically low correlation and potential diversification benefits, many investors are eager for commodities to make an upward turn.

The table below illustrates the calendar year performance for each of the eight asset classes and the relative rankings of commodities.  Commodities had back-to-back double digit negative returns and finished in the last place for the calendar years 2014 and 2015.  Since 1970, that has only happened two other times, in 1975-76 and 1997-98, as highlighted in yellow.  The table also illustrates the calendar year performance for 1977 and 1999, which are the years immediately following the back-to-back last place finishes (highlighted in green).

asset class history

The outcome for 2016 and beyond still remains to be seen and past performance is never indicative of future returns.  But history does shown that commodities have had the ability to reverse from sustained lows and deliver significant gains.  After back-to-back last place rankings, the single year returns that followed in 1977 and 1999 for commodities were impressive.  Perhaps even more interesting for investors with a longer time horizon are the average returns for the following three year periods 1977-1979 and 1999-2001, as illustrated in the table below.

3 year commodity

Click here for disclosures.

Two potential solutions for investors who are looking for commodity exposure are The Arrow DWA Balanced Fund (DWAFX) and the Arrow DWA Tactical Fund (DWTFX and DWAT).  Both of these strategies have the ability to allocate to commodities when they are in favor (and both have the ability to rotate away from commodities when they are out of favor.

As shown below, The Arrow DWA Balanced Fund (DWAFX) has the ability to allocate between 10 and 40 percent to Alternatives, including commodities.

dwafx

The Arrow DWA Tactical Fund (DWTFX and DWAT), has the ability to allocate between 0 and 90 percent to Alternatives.  Commodities can be up to 30 percent of that allocation.

dwtfx

Environments where commodities are in favor have the potential to be good environments for the performance of these strategies.  We have seen our commodity exposure in both of these funds increase in recent months.  Click here and here to see the 5/31/16 holdings of these funds.

The Arrow DWA Balanced Fund (DWAFX) is available as a mutual fund — click here for more information.

The Arrow DWA Tactical Fund (DWTFX) is available as a mutual fund and as an ETF (DWAT).  It is also available as the Global Macro portfolio on a number of SMA and UMA platforms, including the Wells Fargo Masters and DMA platforms. —click here for more information.  Contact Andy Hyer at andyh@dorseymm.com for information about the SMA/UMA.

See www.arrowfunds.com for a prospectus.  Dorsey Wright is research provider to Arrow Funds for The Arrow DWA Balanced and Arrow DWA Tactical Funds.  The relative strength strategy is NOT a guarantee.  There may be times where all investments and strategies are unfavorable and depreciate in value.

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Weekly RS Recap

June 27, 2016

The table below shows the performance of a universe of mid and large cap U.S. equities, broken down by relative strength decile and quartile and then compared to the universe return.  Those at the top of the ranks are those stocks which have the best intermediate-term relative strength.  Relative strength strategies buy securities that have strong intermediate-term relative strength and hold them as long as they remain strong.

Last week’s performance (6/20/16 – 6/24/16) is as follows:

ranks

This example is presented for illustrative purposes only and does not represent a past or present recommendation.  The relative strength strategy is NOT a guarantee.  There may be times where all investments and strategies are unfavorable and depreciate in value.  The performance above is based on pure price returns, not inclusive of dividends, fees, or other expenses.  Past performance is not indicative of future results.  Potential for profits is accompanied by possibility of loss.

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Relative Strength Spread

June 22, 2016

The chart below is the spread between the relative strength leaders and relative strength laggards (top quartile of stocks in our ranks divided by the bottom quartile of stocks in our ranks; universe of U.S. mid and large cap stocks).  When the chart is rising, relative strength leaders are performing better than relative strength laggards.    As of 6/21/16:

spread

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

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Manage Your Luck

June 21, 2016

“Luck is the residue of design”

                      –Branch Rickey

There is a lot more luck involved in investing than people think.  I’m not saying there isn’t skill involved in investing or that there aren’t ways to outperform the market over time.  Even if you have a process that can be shown to outperform the market over long time periods, there can be a great deal of variation in returns from year to year.  A well designed investment model can certainly help manage some of that luck, but it is difficult to eliminate it entirely.

Several years ago I looked at some momentum models in a unique way.  Most research on equity momentum involves buying a large basket of stocks (the top decile or top quintile, for example) and rebalancing on some preset schedule (like monthly).  There are a lot of active strategies that aren’t run that way so we wanted to find out how much variation there could be in owning a sub set of the high momentum stocks and rebalancing more frequently.  Do you need to own all of the big winners in order for a momentum strategy to work?

In order to attempt to answer that question I created a process that picked stocks at random out of a high momentum basket and held them until they were weak.  (You can read about it in more detail in the original whitepaper I published by clicking here.)  In the test shown in this post, I am using a universe of the top 1000 market capitalization stocks traded on US exchanges.  That eliminates the problem of holding very illiquid stocks; every stock in that universe should have sufficient liquidity to trade without major slippage costs.  Each week the stocks were ranked by their trailing 12 month performance, which is a very standard way to measure momentum.  Anything that ranked in the top decile based on the trailing 12 month performance rank was considered to be “eligible” for the portfolio.  Anything that ranked below the top quartile of the ranks was eliminated immediately from the portfolio.  The portfolio was set up to hold 50 stocks.

Most tests would just pick the top ranked stock when something needed to be bought.  The difference in my test was we picked something at random from the “eligible” list.  There were about 100 eligible stocks each week – the top 10% of the 1000 stock universe (excluding buyouts, etc…).  Then I ran the process 100 times to create 100 different equity curves.  It would be the same thing as giving the eligible list to 100 different people each week and telling them they can pick anything they want off the list as long as they don’t already own it.  You are going to wind up with 100 totally different portfolios over time with the only thing in common being the process of buying high momentum stocks and selling them when they get weak.

The results of the 100 trials are summarized in the table and graph below (click the image to enlarge).  The table shows the return of the S&P 500 as well as the average return of the 100 trials each year.  There is also a section that shows where the quartile breaks occur each year.  The graph shows the returns year by year with the red bar being the average return, the box showing where the mid quartiles are, and the whiskers extending to the min and max returns.  The green dot is the S&P 500.

Random Numbers

Random Graphs

The biggest thing that should jump out at you is that even by picking stocks at random, all 100 trials outperform the S&P 500 Total Return Index.  That is pretty amazing.  The actual stocks you put into the portfolio don’t matter as much as you would think.  The process is what is important.  Constantly cutting the losers and buying winners is what drives the performance.  The process helps to manage the luck of stock picking over time!

You can also see that from year to year the returns can vary quite a bit.  So what is the difference?  Literally, luck.  Some years the process is lucky, some years it isn’t, but when the process is solid it works out over time.  It is also a good reminder of why it is so important to focus on the process rather than the results over a short time period.  Just because a process underperforms for a year it doesn’t mean it is “broken.”  This, unfortunately, is how most investors think.  There is so much research on poor investor behavior I’m not even going to attempt to address it here!

A solid investment process winds up managing the luck that exists in implementing the system over short time periods.  Momentum is a robust enough factor to handle picking stocks and random from a highly ranked sub set of securities and then selling them when they are weak.  What happens from year to year is a lot about luck, but over time the design of the process overcomes the luck.

The returns used within this article are the result of a back-test using indexes that are not available for direct investment.  Returns do include dividends, but do not include transaction costs.  Back-tested performance is hypothetical (it does not reflect trading in actual accounts) and is provided for informational purposes to illustrate the effects of the discussed strategy during a specific period.  Back-tested performance results have certain limitations.  Such results do not represent the impact of material economic and market factors might have on an investment advisor’s decision making process if the advisor were actually managing client money.  Back-testing performance also differs from actual performance because it is achieved through retroactive application of a model investment methodology designed with the benefit of hindsight.  Dorsey, Wright & Associates believes the data used in the testing to be from credible, reliable sources, however; Dorsey, Wright & Associates, LLC (collectively with its affiliates and parent company, “DWA”) makes no representation or warranties of any kind as to the accuracy of such data. Past performance is not indicative of future results.  Potential for profits is accompanied by possibility of loss.  

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Dorsey Wright Separately Managed Accounts

June 15, 2016

Picture1

Our Systematic Relative Strength portfolios are available as separately managed accounts at a large and growing number of firms.

  • Wells Fargo Advisors (Global Macro available on the Masters/DMA Platforms)
  • Morgan Stanley (IMS Platform)
  • TD Ameritrade Institutional
  • UBS Financial Services (Aggressive and Core are available on the MAC Platform)
  • RBC Wealth Management (MAP Platform)
  • Raymond James (Outside Manager Platform)
  • Stifel Nicolaus (Opportunity Platform)
  • Kovack Securities (Growth and Global Macro approved on the UMA Platform)
  • Charles Schwab Institutional (Marketplace Platform)
  • Envestnet UMA (Growth, Aggressive, Core, Balanced, International, and Global Macro approved)
  • Fidelity Institutional

Different Portfolios for Different Objectives: Descriptions of our seven managed accounts strategies are shown below.  All managed accounts use relative strength as the primary investment selection factor.

Aggressive:  This Mid and Large Cap U.S. equity strategy seeks to achieve long-term capital appreciation.  It invests in securities that demonstrate powerful relative strength characteristics and requires that the securities maintain strong relative strength in order to remain in the portfolio.

Core:  This Mid and Large Cap U.S. equity strategy seeks to achieve long-term capital appreciation.  This portfolio invests in securities that demonstrate powerful relative strength characteristics and requires that the securities maintain strong relative strength in order to remain in the portfolio.  This strategy tends to have lower turnover and higher tax efficiency than our Aggressive strategy.

Growth:  This Mid and Large Cap U.S. equity strategy seeks to achieve long-term capital appreciation with some degree of risk mitigation.  This portfolio invests in securities that demonstrate powerful relative strength characteristics and requires that the securities maintain strong relative strength in order to remain in the portfolio.  This portfolio also has an equity exposure overlay that, when activated, allows the account to hold up to 50% cash if necessary.

International: This All-Cap International equity strategy seeks to achieve long-term capital appreciation through a portfolio of international companies in both developed and emerging markets.  This portfolio invests in those securities with powerful relative strength characteristics and requires that the securities maintain strong relative strength in order to remain in the portfolio.  Exposure to international markets is achieved through American Depository Receipts (ADRs).

Global Macro: This global tactical asset allocation strategy seeks to achieve meaningful risk diversification and investment returns.  The strategy invests across multiple asset classes: Domestic Equities (long & inverse), International Equities (long & inverse), Fixed Income, Real Estate, Currencies, and Commodities.  Exposure to each of these areas is achieved through exchange-traded funds (ETFs).

Balanced: This strategy includes equities from our Core strategy (see above) and high-quality U.S. fixed income in approximately a 60% equity / 40% fixed income mix.  This strategy seeks to provide long-term capital appreciation and income with moderate volatility.

Tactical Fixed Income: This strategy seeks to provide current income and strong risk-adjusted fixed income returns.   The strategy invests across multiple sectors of the fixed income market:  U.S. government bonds, investment grade corporate bonds, high yield bonds, Treasury inflation protected securities (TIPS), convertible bonds, and international bonds.  Exposure to each of these areas is achieved through exchange-traded funds (ETFs).

Picture2

To receive fact sheets for any of the strategies above, please e-mail Andy Hyer at andy@dorseywright.com or call 626-535-0630.  Past performance is no guarantee of future returns.  An investor should carefully review our brochure and consult with their financial advisor before making any investments.

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Relative Strength Spread

June 15, 2016

The chart below is the spread between the relative strength leaders and relative strength laggards (top quartile of stocks in our ranks divided by the bottom quartile of stocks in our ranks; universe of U.S. mid and large cap stocks).  When the chart is rising, relative strength leaders are performing better than relative strength laggards.    As of 6/14/2016:

spread

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

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A Momentum Based Core Equity Strategy (Part 4)

June 14, 2016

To read Part 1 click here

To read Part 2 click here

To read Part 3 click here

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

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

CE

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

CE2

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

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

 

The returns used within this article are the result of a back-test using indexes that are not available for direct investment.  Returns do include dividends, but do not include transaction costs.  Back-tested performance is hypothetical (it does not reflect trading in actual accounts) and is provided for informational purposes to illustrate the effects of the discussed strategy during a specific period.  Back-tested performance results have certain limitations.  Such results do not represent the impact of material economic and market factors might have on an investment advisor’s decision making process if the advisor were actually managing client money.  Back-testing performance also differs from actual performance because it is achieved through retroactive application of a model investment methodology designed with the benefit of hindsight.  Dorsey, Wright & Associates believes the data used in the testing to be from credible, reliable sources, however; Dorsey, Wright & Associates, LLC (collectively with its affiliates and parent company, “DWA”) makes no representation or warranties of any kind as to the accuracy of such data. Past performance is not indicative of future results.  Potential for profits is accompanied by possibility of loss.  

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Core/Satellite International Equity Exposure

June 14, 2016

The technical picture of the U.S. Dollar Spot Index (DX/Y) continues to show signs of weakness.  Over the past year, we have seen a series of lower lows, a trendline break in March of this year, and then a reversal to a column of O’s this month, as shown in the chart below.

dxy II

The strength or weakness of the U.S. Dollar has far-reaching effects on a variety of asset classes.  However, for investors in international equities, this U.S. Dollar weakness can actually be a welcomed development as international securities can help protect investors from a falling dollar.  All other things equal, if the currencies of the foreign markets you are invested in (i.e. Euro) strengthen while the dollar continues to fall, these investments will be worth more when converted back into dollars. What a great way to hedge the greenback.

Among the most popular ways that investors tend to get that international equity exposure is through the $60 billion iShares MSCI EAFE ETF (EFA), which provides investors with broad exposure to companies across Europe, Australia, Asia, and the Far East.  However, this ETF–like many other international equity ETFs–is weighted by capitalization.  That may be fine if the large and mega caps are generating strong returns.  However, it can be a challenge if there are better returns in small or mid cap companies.

For a variety of reasons, many advisors like to keep a core position in a capitalization-weighted ETF, like EFA.  However, see the efficient frontier below to get a sense of the potential benefits of adding an actively managed “satellite” strategy to core EFA exposure.

intl efficient frontier

Source: Dorsey Wright and Yahoo! Finance.  Period 3/31/06 – 5/31/16.  Returns include dividends.

The efficient frontier above shows the risk and return profile of different combinations of the iShares MSCI EAFE ETF (EFA) and our Systematic Relative Strength International portfolio.  As shown above, the Systematic Relative Strength International portfolio has had much higher returns over time, albeit with slightly higher standard deviation.  Deciding how to get international equity exposure doesn’t have to be a binary decision between these two strategies.  An investor may choose to make a satellite allocation to Systematic RS International.  Note that from the efficient frontier above, a 50/50 allocation between EFA and Systematic RS International had returns that were several percent higher with standard deviation that was only about 1 percent higher than EFA alone over this period of time.

Some quick facts on our Systematic RS International portfolio:

  • Inception: 3/31/2005
  • Invests in 30-40 ADRs from both emerging and developed international markets
  • Available on a variety of SMA and UMA platforms.  For example, it is available at Stifel, UBS, RBC, Envestnet, Schwab, TD Ameritrade, Fidelity, and more.

Performance is shown below:

intl performance

intl performance II

Period 3/31/05 – 5/31/16.

A core/satellite approach to international equity exposure is a popular way to construct an allocation and we believe that our Systematic RS International portfolio can be an effective way to get that satellite exposure.

For additional information about Systematic RS International, please contact Andy Hyer at 626-535-0630 or andyh@dorseymm.com.

The performance represented in this brochure is based on monthly performance of the Systematic Relative Strength International Model.  Net performance shown is total return net of management fees, commissions, and expenses for all Dorsey, Wright & Associates managed accounts, managed for each complete quarter for each objective, regardless of levels of fixed income and cash in each account.  The advisory fees are described in Part 2A of the adviser’s Form ADV.  The starting values on 3/31/2006 are assigned an arbitrary value of 100 and statement portfolios are revalued on a trade date basis on the last day of each quarter.  All returns since inception of actual Accounts are compared against the NASDAQ Global ex US Index.  The NASDAQ Global ex US Index Total Return Index is a stock market index that is designed to measure the equity market performance of global markets outside of the United States and is maintained by Nasdaq.  A list of all holdings over the past 12 months is available upon request.  The performance information is based on data supplied by the Manager or from statistical services, reports, or other sources which the Manager believes are reliable.  There are risks inherent in international investments, which may make such investments unsuitable for certain clients. These include, for example, economic, political, currency exchange, rate fluctuations, and limited availability of information on international securities.  Past performance does not guarantee future results. In all securities trading, there is a potential for loss as well as profit. It should not be assumed that recommendations made in the future will be profitable or will equal the performance as shown. Investors should have long-term financial objectives when working with Dorsey, Wright & Associates.

 

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A Momentum Based Core Equity Strategy (Part 3)

June 13, 2016

To read Part 1 click here

To read Part 2 click here

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

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

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

Mom

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

ValMom

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

LVMom

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

FactorRet

 

The returns used within this article are the result of a back-test using indexes that are not available for direct investment.  Returns do include dividends, but do not include transaction costs.  Back-tested performance is hypothetical (it does not reflect trading in actual accounts) and is provided for informational purposes to illustrate the effects of the discussed strategy during a specific period.  Back-tested performance results have certain limitations.  Such results do not represent the impact of material economic and market factors might have on an investment advisor’s decision making process if the advisor were actually managing client money.  Back-testing performance also differs from actual performance because it is achieved through retroactive application of a model investment methodology designed with the benefit of hindsight.  Dorsey, Wright & Associates believes the data used in the testing to be from credible, reliable sources, however; Dorsey, Wright & Associates, LLC (collectively with its affiliates and parent company, “DWA”) makes no representation or warranties of any kind as to the accuracy of such data. Past performance is not indicative of future results.  Potential for profits is accompanied by possibility of loss.  

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A Momentum Based Core Equity Strategy (Part 2)

June 10, 2016

To read Part 1 click here

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

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

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

Val

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

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

LowVol

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

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

 

The returns used within this article are the result of a back-test using indexes that are not available for direct investment.  Returns do include dividends, but do not include transaction costs.  Back-tested performance is hypothetical (it does not reflect trading in actual accounts) and is provided for informational purposes to illustrate the effects of the discussed strategy during a specific period.  Back-tested performance results have certain limitations.  Such results do not represent the impact of material economic and market factors might have on an investment advisor’s decision making process if the advisor were actually managing client money.  Back-testing performance also differs from actual performance because it is achieved through retroactive application of a model investment methodology designed with the benefit of hindsight.  Dorsey, Wright & Associates believes the data used in the testing to be from credible, reliable sources, however; Dorsey, Wright & Associates, LLC (collectively with its affiliates and parent company, “DWA”) makes no representation or warranties of any kind as to the accuracy of such data. Past performance is not indicative of future results.  Potential for profits is accompanied by possibility of loss.  

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A Momentum Based Core Equity Strategy (Part 1)

June 9, 2016

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

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

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

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

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

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High RS Diffusion Index

June 8, 2016

The chart below measures the percentage of high relative strength stocks (top quartile of our ranks) that are trading above their 50-day moving average (universe of mid and large cap stocks.)  As of 6/7/16.

diffusion

The 10-day moving average of this indicator is 81% and the one-day reading is 90%.

The relative strength strategy is NOT a guarantee.  There may be times where all investments and strategies are unfavorable and depreciate in value.  Investors cannot invest directly in an index.  Indexes have no fees.  Past performance is no guarantee of future returns.  Potential for profits is accompanied by possibility of loss.

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Ode to Smart Beta

June 7, 2016

Nir Kaissar at Bloomberg recently published a nice summary of the historical returns of a number of Smart Beta factors, including Value, Size, Quality, Momentum, and Low Volatility:

In the brave new world of next generation, active investment management, smart beta is all the rage. Smart beta relies on five major strategies — or “factors” — to fine tune market returns:

Value – buying cheap companies

Size – buying small companies

Quality – buying highly profitable and stable companies

Momentum – buying the trend

Low Volatility – buying defensive companies

Investments built around each of those factors have historically beaten the market, as the following chart shows:

smart beta returns

blend

A couple of observations:

  • The market is efficient?  Really?  I don’t think so.  Excess returns are available if investors focus on the right factors.
  • These factors move in and out of favor so nobody should expect each factor to outperform all the time.  In fact, Kaissar highlighted the benefits of mixing these factors in order to smooth out the ride.
  • It’s hard to miss the fact that Momentum had the highest returns of any of the factors listed in this study.  Yes, Momentum had the highest volatility, but it also had the highest Sharpe ratio.

Smart Beta is far more than just a catchy marketing phrase.  It is a much more effective way to seek excess returns than traditional active management.

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

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Relative Strength Spread

June 7, 2016

The chart below is the spread between the relative strength leaders and relative strength laggards (top quartile of stocks in our ranks divided by the bottom quartile of stocks in our ranks; universe of U.S. mid and large cap stocks).  When the chart is rising, relative strength leaders are performing better than relative strength laggards.    As of 6/6/2016:

spread

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

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Weekly RS Recap

June 6, 2016

The table below shows the performance of a universe of mid and large cap U.S. equities, broken down by relative strength decile and quartile and then compared to the universe return.  Those at the top of the ranks are those stocks which have the best intermediate-term relative strength.  Relative strength strategies buy securities that have strong intermediate-term relative strength and hold them as long as they remain strong.

Last week’s performance (5/31/16 – 6/3/16) is as follows:

ranks

This example is presented for illustrative purposes only and does not represent a past or present recommendation.  The relative strength strategy is NOT a guarantee.  There may be times where all investments and strategies are unfavorable and depreciate in value.  The performance above is based on pure price returns, not inclusive of dividends, fees, or other expenses.  Past performance is not indicative of future results.  Potential for profits is accompanied by possibility of loss.

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Sector Performance

June 3, 2016

The table below shows performance of US sectors over the trailing 12, 6, and 1 month(s).  Performance updated through 6/2/16.

sector

The performance above is based on pure price returns, not inclusive of dividends, fees, or other expenses.  Past performance is not indicative of future results.  Potential for profits is accompanied by possibility of loss.  Source: iShares

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Factor Investing: The Benefits of Combining Momentum & Value

June 2, 2016

After our previous write up regarding the idea of combining momentum and low volatility into a portfolio, we had a few requests asking about the concept of combining momentum and value. As long time Dorsey Wright readers know, while we absolutely believe momentum can work as a stand-along strategy in a portfolio, combining momentum with a value based strategy does have certain advantages.   As is the case with low volatility, a value based approach can also typically be thought of as a reversion to the mean type of trade as market participants seek value in underperforming stocks or asset classes.   Obviously, a momentum based approach is focused on finding stocks that have outperformed their peers over a certain period (ex. 12 month trailing), hoping those strong trends continues to maintain leadership in the market.

Here is a brief summary of each strategy side by side.   Note we are using total return for the RPV (Pure Value) and SPX (Benchmark).  As shown below, PDP (Momentum) outperforms both RPV and SPX over the allotted time frame in this study, but not without slightly higher volatility.   We can also see a number of years in which momentum and value have substantially different performance numbers.   Let’s take this a step further and dive into some of the details on the correlation of excess returns between the two strategies.

PDP

As we pointed out in our previous post, the correlation of excess returns between low volatility and momentum came in at roughly -.70.  Given what we mentioned about the underlying theme of momentum investing (trend following) while compared to value investing (mean reversion), it’s logical to think a similar type of figure would exist between these two strategies as well.   The table below shows a comparison of annual returns using the time period 1998 and 2015 in which the correlation of excess returns between value and momentum comes out to be -.50.  A few of the outlier years to take note of which contain major differences in performance are 1998 – 2002, 2007, 2009, and finally 2015.  The main concept again being not only does having the ability to rotate or combine these two factor based strategies help improve performance; it also helps in reducing volatility. (click on below graphic to enlarge)

PDPVSRPV CORR

PDP inception date: March 1, 2007, RPV inception date: March 3, 2006 – data prior to inception is based on a back-test of the underlying indexes. Please see the disclosures for important information regarding back-testing.  PDP returns do not include dividends.  Returns do not include all potential transaction costs.  Past performance is not indicative of future results.  Potential for profits is accompanied by possibility of loss. 

Let’s take this a step further to answer the one question that is usually on most market participants minds.   “How do I implement these products into a portfolio for my clients?”   The number of possibilities is endless but to keep things simple we set up a “static allocation” model that rotates through a number of different portfolio’s starting with a 90% PDP/10% RPV and all the way to 10% RPV/90% PDP during our time period stated above.   This gives a very detailed description of how each one of these portfolios would have performed on a cumulative, annual, and risk –adjusted (volatility) basis.   However, what if we could improve on these returns and be more flexible in our allocations.   We know that often times combing some sort of trend following proxy (typically a moving average) in addition to a stand-alone momentum strategy can often times help improve these numbers.    This will be part of our final discussion. (click on below graphic to enlarge)

pdprpvhistorcials

Over time, it’s typically been the case that a momentum/trend following based strategy (assuming a long only portfolio) tends to perform better while the SPX is above its 200 day moving average.   On the flip side, a choppy market with a lack of sustained leadership (which can favor a value based strategy) is more likely to presents itself when the SPX is below its 200 day MA.  This is not always the case but over the years research has shown that the 200 day moving average is often considered a reliable proxy for a risk on/risk off environment.  The below table compares a model we have created using a 200 day moving average as a risk proxy to determine whether or not we will invest in a momentum (PDP) or value (RPV).   We thought it would be interesting to take these allocations to the extreme, relying on a 100% momentum based strategy when the SPX is above its 200 day MA, while flipping to Value when the SPX is below its 200 day moving average.   Our momentum/trend following model which incorporated the 200 day risk proxy averaged just over 10% annual return, while minimizing volatility to just 21%.   Comparing these towards using PDP or RPV as stand-alone vehicles as was in table 1, we can see the benefits when it comes to having access to both products. (click on below graphic to enlarge)

200ma NEW

PDP inception date: March 1, 2007, RPV inception date: March 3, 2006 – data prior to inception is based on a back-test of the underlying indexes. Performance of the switching strategy is the result of back-testing.  Back-tested performance results have certain limitations.  Such results do not represent the impact of material economic and market factors might have on an investment advisor’s decision-making process if the advisor were actually managing client money.  Back-testing performance also differs from actual performance because it is achieved through retroactive application of a model investment methodology designed with the benefit of hindsight. PDP returns do not include dividends.  Returns do not include all potential transaction costs.  Past performance is not indicative of future results.  Potential for profits is accompanied by possibility of loss. 

As this paper shows, there are a number of ways to combine both momentum and value into a portfolio.   For those investors willing to accept slightly higher volatility to achieve higher returns, a portfolio with a larger allocation towards momentum certainly is favorable.   The same can be true for those investors looking to low annualized volatility who might not be as concerned about achieving a certain level of excess return.   Finally, we showed adding a trend following proxy (the 200 day moving average) can help aide in substantial performance over the benchmark (SPX), and also help achieve better risk management when using a momentum or value based strategy as a stand-alone vehicle.

Performance data for the model is the result of hypothetical back-testing.  Performance data for RPV prior to 03/01/06 and PDP prior to 3/01/2007 is the result of backtested underlying index data.  Investors cannot invest directly in an index, like the SPX.  Indexes have no fees.  Total return figures are used in RPV and SPX calculations.  Back-tested performance is hypothetical (it does not reflect trading in actual accounts) and is provided for informational purposes to illustrate the effects of the strategy during a specific period.  Back-tested performance results have certain limitations. Back-testing performance differs from actual performance because it is achieved through retroactive application of an investment methodology designed with the benefit of hindsight. Model performance data as well as back-tested performance do not represent the impact of material economic and market factors might have on an investment advisor’s decision making process if the advisor were actually managing client money. Past performance is not indicative of future results. Potential for profits is accompanied by possibility of loss.

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