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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