Politics and Investing

February 21, 2017

Last week Bloomberg reported that Americans recently broke the American Psychological Association’s anxiety meter for a record level of stress.  You read that right.  No, this is not from late 2008.  This is from January 2017.

“The results of the January 2017 poll show a statistically significant increase in stress for the first time since the survey was first conducted in 2007,” the APA said on Wednesday in a report on the survey of 1,019 adults living in the U.S., conducted from Jan. 5 to Jan. 19 by Harris Poll.

Americans’ stress levels in January were worse than in August, in the middle of the angriest, most personal campaign in recent memory, when some believed the anxiety would abate after the election. At 57 percent, more than half of respondents said the current political climate was a very or somewhat significant source of stress. Stressors for everyone, including Republicans, were the fast pace of unfolding events and especially the uncertainty of the current political climate, said Vaile Wright, director of research and special projects at the APA.

What is it that has everyone so worked up?  Politics.  How many of your clients invest their politics?  When the resident of the Oval office is of their same political party, do they tend to be more bullish and when the opposite is true, do they tend to be more bearish?

When I read that article I couldn’t help but think back to something that The Motley Fool wrote last year as it relates to the problem of conflating politics and investing:

Economics is a close cousin of politics, which is dangerous because politics is a close cousin of emotional decisions detached from reality.

Not only do most of us have emotional opinions about who should/shouldn’t run the country, but we unfailingly overestimate how much influence presidents have over the economy and stock market. When presidents do impact the economy, good luck guessing how markets will respond. Lots of smart people predicted that Barack Obama’s spending plans meant surging interest rates and a collapsing dollar.

Growing the economy means getting everyone to win, whereas politics by definition means getting the opposing party to lose. Rationality melts when you set up this kind of my-team-versus-yours dilemma. Psychologist Geoffrey Cohen showed that Democratic voters supported Republican proposals when they were attributed to fellow Democrats more than they supported Democratic proposals attributed to Republicans, and vice versa. Imagine the same part of your brain analyzing investments. It’s a disaster.

I like politics, and I love investing. But I run from anything conflating the two.

Thus, the power of an emotionless method of investing.  The chart just reflects what is, not what we fear might be.  And what does that chart—using the S&P 500 as a proxy for the market—look like right now?

spx

As of 2/16/17

Well it doesn’t look bearish…  Invest accordingly.

Investors cannot invest directly in an index. Indexes have no fees. Past performance is not indicative of future results. Potential for profits is accompanied by possibility of loss.

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Sector Performance

February 16, 2017

The table below shows performance of US sectors over the trailing 12, 6, and 1 month(s).  Performance updated through 2/15/2017.

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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To constrain or not constrain?

January 4, 2017

Fixed income allocations continue to be a major question in client portfolios. Do you continue to hold traditional long term bonds that will price deteriorate as rates rise with the hopes that clients will be able to hold them to maturity, or do you layer in differentiating bond funds that generate returns through multiple asset classes? Bloomberg wrote:

Donald Trump’s election has reignited the prospects for inflation and growth both in the U.S. and abroad, which will likely lead to higher interest rates and spell the end of the bond market’s three-decade Bull Run.

And that will favor funds that can go wherever the highest returns are — without constraints on maturity, geography or even credit quality.

The buy and hold methodology gives you reliable income flows with the possibility of price deterioration as rates rise. While the multi asset class funds give you more ways to protect your downside and generate upside, which comes with higher volatility and ever-changing sector allocations.

It is our belief that the ability to move between sector allocations will be key moving forward into 2017 as the world continues to change. Unlike most unconstrained bond funds however that shy away from long term bonds, we believe that long Treasuries, at times, are a valuable holding.  Our Tactical Fixed Income strategy can hold up to 40% in long term government exposure, along with the ability to hold convertible bonds, short-term Treasuries, emerging market debt, TIPs, high yield and Investment grade Corporates.

If you would like more information on our fixed income strategy please reach out to Andyh@dorseymm.com.

Dorsey, Wright & Associates, LLC, a Nasdaq Company, is a registered investment advisory firm.  Neither the information within this article, nor any opinion expressed shall constitute an offer to sell or a solicitation or an offer to buy any securities, commodities or exchange traded products. This article does not purport to be complete description of the securities or commodities, markets or developments to which reference is made.  Past performance, hypothetical or actual, 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.  Advice from a financial professional is strongly advised.

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Looking to 2017

December 30, 2016

As we close out 2017 the final week of the year the markets have not been as cooperative as investors would have liked.  The markets have pulled back from historic highs and the Dow was not able to hit the elusive 20,000 mark. Looking forward to 2017 here are several issues that may impact clients and markets.

Political

New President: President elect Trump takes the oath of office on Friday January 20th in Washington, D.C.  The US markets have reacted mostly positive to the Trump election based on promises of lower taxes, faster growth and more US based jobs. Here is a link if you would like more information on the inauguration.

Tensions with Russia?: Obama announced sanctions against Russia yesterday afternoon and expelled 35 Russian official in response to the allegations of Russian cyber-attacks during the election. Russia has not yet retaliated but Russian Foreign Minister Sergei Lavrov “We of course cannot leave these stunts unanswered. Reciprocity is the law in diplomacy and international relations.”

Social Security

Tax cap increase: Workers and employers each pay in 6.2% of wages into the Social Security system until the salary cap of $118,500. In 2017 the tax rate will stay the same but the cap is being raised to $127,200 to adjust for higher wages in the US.

Payments increase: The cost of living adjustment for 2017 will be a modest 0.3% or about $5 per month. This increase is in line with 2016  which did not see any raise. Increases to Social security are based on the Consumer Price Index for Urban Wage Earners and Clerical Workers.

Earning Limits: The earnings limit for people  65 and younger will increase from $15,720 in 2016 to $16,920 in 2017. While those who turn 66 in 2017 the limit increases by $3,000 to $44,880.

For a full list of changes, here is the 2017 Social Security Changes Fact Sheet.

Currency

China: China is trying to reduce the impact of the Dollar on the Yuan as the Yuan trades at an eight year low. They are introducing 11 more countries into its currency basket.

Euro: The Financial Times polled 28 economists and over 60% believe that the Euro and dollar will hit parity next year for the first time in 14 years. Rising interest rates in the US and continued QE in Europe are thought to be two of the main factors in the shift.

Dorsey, Wright & Associates, LLC, a Nasdaq Company, is a registered investment advisory firm

 Neither the information within this article, nor any opinion expressed shall constitute an offer to sell or a solicitation or an offer to buy any securities, commodities or exchange traded products. This article does not purport to be complete description of the securities or commodities, markets or developments to which reference is made.

Past performance, hypothetical or actual, 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.  Advice from a financial professional is strongly advised.

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Top business stories of 2016–PnF Edition

December 29, 2016

USA Today identified the following as “The top 10 business stories of 2016.”  I’ve added PnF charts for some of the topics mentioned in the article to provide some additional insight.

1. Donald Trump elected president. New presidents always portend massive changes. But the election of Trump, with his promises to upend Washington and roll back regulations, could shake business and economic establishments to their foundations. While he has hinted at keeping some popular provisions of Obamacare, Trump will be politically pressured to repeal much of the health care law that mandated universal coverage. Having promised to bring jobs back to Rust Belt states, Trump is likely to renegotiate trade deals and possibly even raise tariffs, a move that could trigger global disputes. The Dodd-Frank Act, enacted after the national financial crisis to lower excessive risk-taking by banks, could be under assault as lobbyists push for easing its restrictions. Trump has professed a desire to maintain the current low-interest-rate policy.

xlv

xli

tnx

2. Brexit. In late June, the United Kingdom defied polling forecasts and voted to leave the European Union, setting off reverberations across the globe. U.S. stocks fell 5% as fears spread of disrupted trade relationships with Europe and of other countries that could follow the U.K.’s lead. Yet the market recovered within days as investors realized the immediate effects on American businesses were limited. There were even some winners among U.S. banks and tech firms that may have gained from a shift in investment from the U.K. But the economic fallout won’t really be clear until the U.K. renegotiates trade deals with European countries before it leaves the EU in 2019.

ewu

fxb

3. The Dow closes in on 20,000. Wall Street stumbled into 2016, with stocks suffering their worst-ever first week of trading. But the gloom gave way to bullish optimism, especially after the presidential election when the “Trump Rally” put the 120-year-old Dow Jones industrial average on a track for a “Dow 20,000” milestone, racking up more than 25 record highs in 2016 so far along the way. The stock rebound occurred despite the Federal Reserve’s decision to hike short-term interest rates for the first time in 2016  at its December meeting — when the central bank also let investors know it expects three more rate increases in 2017.

djia

4. Prescription drug prices bring controversy. The rising cost of prescription drug prices captured headlines, generated rising criticism and sparked investigations. At center stage was Turing Pharmaceuticals CEO Martin Shkreli’s decision to impose a more than 5,000% price spike of a drug used to treat a parasitic illness suffered by AIDS patients. Summoned to appear before a congressional committee in February, he went silent, invoking his Fifth Amendment right to avoid testifying against himself. But he unloaded after the hearing, calling members of Congress “imbeciles” in a tweet. Turing wasn’t the only drugmaker taking fire. Health care providers, patients and others criticized Mylan for a series of increases that raised the price for a two-pack of EpiPens, a potentially life-saving injection for allergy sufferers, to $600, up from about $100 in 2009. By year’s end, Mylan had introduced a generic version of the medication for $300 per two-pack. All of these events drew fire from a Senate committee report in December that warned “staggering” increases in the cost of some prescription drugs threaten the health of patients and “the economic stability of American households.”

ihe

5. Wells Fargo’s scandal. In September, the San Francisco-based bank agreed to a $185 million settlement with federal regulators after an investigation showed Wells Fargo had secretly opened millions of unauthorized deposit and credit card accounts that weren’t authorized by customers. An estimated 5,300 employees were fired over several years for pressuring customers to accept the largely unwanted accounts, the bank acknowledged in its settlement with the Consumer Financial Protection Bureau, the Office of the Comptroller of the Currency and Los Angeles city and county legal officials. Wells Fargo CEO John Stumpf resigned in October, but investigations of the bank’s conduct continued.

wfc
6. Unemployment rate falls. The unemployment rate, which hit 10% in 2009, continued its remarkable descent, falling to 4.6% in November from 5% early in the year. Many economists believe that rate, the lowest since August 2007, represents full employment and can’t fall much further without generating a run-up in inflation as wages rise. The Federal Reserve is coming around to that view and so, at a mid-December meeting, unexpectedly forecast three interest rate hikes in 2017, throwing cold water on the post-election market rally. The low jobless rate is already pushing up pay increases as employers compete for fewer available workers. That smaller pool of workers is also tempering average monthly job gains, which have fallen from 229,000 in 2015 to 180,000 this year.

unrate

7. Oil prices plunge, then rebound. They fell below $27 per barrel in mid-February as a global glut of production fueled a surplus and concerns about economic growth dealt a blow. The commodity’s sharp descent, dropping nearly in half over a four-month stretch, contributed to bankruptcies of dozens of U.S. energy companies and thousands of layoffs. But oil rebounded to more than $50 per barrel after the Organization of the Petroleum Exporting Countries and certain non-OPEC states, including Russia, agreed in November to slash production in a bid to bolster prices.

crude

8. The U.S. dollar shines. The greenback hit its highest level vs. the euro in 14 years as global investors began pricing in less Fed stimulus and stronger U.S. growth. The dollar surged in value against currencies around the world following the election of Trump. It showed particular strength against the Chinese yuan, which Trump repeatedly targeted in his campaign after accusing the Chinese government of currency manipulation to benefit its economy.

dxy

9. Pressure on free trade. A decades-long movement toward free trade and globalization appeared to stop in its tracks as presidential candidates Donald Trump and Hillary Clinton both vowed to withdraw from the Trans-Pacific Partnership, which would have relaxed trade restrictions with Asian nations. Trump threatened to pull out of the North American Free Trade Agreement if Mexico doesn’t renegotiate the deal and to slap Mexico and China with tariffs of 35% and 45%, respectively. His aim: to partly reverse the millions of layoffs at U.S. factories as jobs were offshored to China, Mexico and other countries. But many economists say those jobs aren’t coming back and tariffs risk retaliation that could ravage U.S. exports and jobs.

eww

mchi

10. Fake news fears. Fake news bubbled up during the political campaign and became a business issue for the place where many people get their news: Facebook. A post-election analysis by BuzzFeed found that fake stories shared on Facebook outperformed real news stories during the final three months of the campaign cycle. The most shared story was a fake report about Pope Francis’ endorsement of then-Republican nominee Donald Trump. Facebook CEO and co-founder Mark Zuckerberg said it was ”extremely unlikely” that it affected the election outcome, but the company is making changes so users of the social network can more easily flag news that is fake. A Pew Research Center survey, released earlier this month, found that 63% say fake news creates “great confusion” among the public about current events.

fb

Source of charts is Dorsey Wright.  Charts are as of 12/28/16.  Dorsey, Wright & Associates, a Nasdaq Company, is a registered investment advisory firm. Neither the information nor any opinion expressed shall constitute an offer to sell or a solicitation or an offer to buy any securities, commodities or exchange traded products.  This document does not purport to be complete description of the securities to which reference is made.  There may be times where all investments and strategies are unfavorable and depreciate in value.  Technical Analysis is not predictive and there is no assurance that forecasts based on charts can be relied upon. Each investor should carefully consider the investment objectives, risks, and expenses of the securities discussed above prior to investing.  Advice from a financial professional is strongly advised.  Dorsey Wright currently owns FB in some of its managed accounts.  Investors cannot invest directly in an index.  Indexes have no fees.  Past performance is not indicative of future results.  Potential for profits is accompanied by possibility of loss.

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Fixed Income Under the Microscope

December 27, 2016

Earlier this month we saw a noteworthy shift in DALI, which ranks six asset classes by relative strength from strongest to weakest.  Fixed Income came into 2016 ranked number two in DALI and spent much of the first half of the year ranked number one.

dali-ranks

As of 12/22/16

As shown below, Fixed Income fell to the fourth rank in the middle of this month after having been ranked three or higher since early 2012.

fixed-income-rank-in-dali

Period: 1/3/07 – 12/19/16, based on weekly tally ranking

Bonds are typically the portion of the allocation that investors worry least about, especially over the past 35 years.  Fixed Income tends to have lower volatility than most other asset classes and if you just look at the nominal returns of bonds over time, you would probably be reassured.  After all, as shown in the first table below, nominal Fixed Income returns have been positive in every single decade since the 1870s.  In the tables below, losing performances are shaded in red, those with annualized gains of 0% to 1.9% in yellow, and left unshaded are any gains of 2% or higher.

morningstar-1

Source: Morningstar, Research Affiliates

However, real annualized returns show a very different story.  After accounting for inflation, bonds have not always been so stellar.

morningstar-2

Source: Morningstar, Research Affiliates

What action should be taken if fixed income were to enter into another extended period where its real returns weren’t so favorable?  That will depend on each client’s circumstances, but DALI will be a good way to gauge the overall environment for this very important asset class.

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

December 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 (12/19/16 – 12/23/16) 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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All I want for Christmas is… low volatility?

December 23, 2016

The month of December sees some of the lowest volume trade days of the year. People are on vacation, holiday parties are in full swing and allocations for the next year are being finalized. S&P 500 volatility has also decided it wants to take a step back from the roller coaster of the year. VIX futures hit a 16 month low on Wednesday to 10.97, a level not seen since August of 2015.

The VIX had sharp increase in volatility leading up to the election, post-election it has been on a sharp decline following the announcement that Trump beat Clinton.

vix-price-12-22-2016

With the S&P 500 trading almost flat for the week as of market close on December 22nd  and today shaping up to be another non eventful day, all eyes will be looking to the markets next week. In the next week we will see the final tax loss trades of the Obama Presidency and we may even see the Dow Jones finally break through 20,000.
Investors cannot invest directly in an index. Indexes have no fees. Past Performance is not indicative of future results. Potential for profits is accompanies by possibility of loss.

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Quadfecta Highs

December 20, 2016

How uncommon is it to see the S&P 500, Dow Jones Industrial Average, The Nasdaq Composite, and the Russell 2000 Indexes all break out to new all-time highs on the same day?  Since 1979, there have only been 13 such occurrences, including the most recent occurrence on 11/21/2016.  Piper Jaffrey’s December issue of The Informed Investor, included a nice summary the types of market returns we have seen after such “Quadfecta” days.

Following the republican’s ‘trifecta’ sweep during the election, the popular averages posted ‘quadfecta’ highs, representing the rare occurrence when the SPX, DJIA, COMPQ and RUT all close at record highs on the same trading day. The recent November ‘quadfecta’ highs was the first time this has occurred since Dec. 1999. From our perspective, the major averages simultaneously breaking out to new highs confirms broad participation in the rally and provides further evidence to our secular bull market thesis.  A historical review of other ‘quadfecta’ highs offers compelling results in regards to expected future returns.  Although there are a limited number of occurrences since 1979, the major indices have generated meaningfully returns over the ensuing 26-week and 52-week periods. Additionally, the percent of positive returns has far outpaced negative returns on a historical basis.

quadfecta

The table above highlights various return metrics after quadfecta highs have been reached. As you can see, the SPX, DJIA and COMPQ traded higher 75% of the on a 52-week basis. The RUT was higher 67% of the time after the same time period. Average returns also look healthy across the board with at the major indices averaging at least 8% returns over the next 52-weeks.

When clients ask our thoughts on the markets in 2017, this might be something you consider discussing with them.  Strong indications of healthy market breadth have historically tended to be a good sign for future equity returns.

Price performance only, not inclusive of dividends or transaction costs.  Past performance is no guarantee of future returns.

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What Winning Looks Like (Probably Not What You Expect)

December 6, 2016

The blog Basis Pointing has an excellent write up on the performance profile of winning funds.  I suspect most investors will be very surprised at its findings.  It’s not that winning funds don’t exist–they most definitely do.  Rather, it is that the path to long-term outperformance is far lumpier than most investors probably expect.

Investors tend to have some pretty ingrained misconceptions of what “winning” funds look like. For instance, winning funds lay waste to the index and category peers; they do so over the short- and long-term; they corner really well, deftly avoiding big drawdowns and rocking during rallies; they don’t rattle around much; they succeed like clockwork. They’re Tom Brady.

For those who have gotten to know markets, randomness, and the resultant unpredictability of short and even intermediate-term performance, we know this is nuts. Winning funds do not succeed anywhere near linearly. Performance is jagged; success and failure arrive abruptly; it often takes years to grind out an advantage; and so forth. This is pure torture for many investors, who bail (and that pattern reveals itself in the form of hideous dollar-weighted returns; if there’s any consistency in markets, it’s that, but I digress).

Study

However, this concept is often too abstract so I thought I’d try to semi-simply illustrate it through an example. Here’s what I did (which will win no points for elegance or precision but last time I checked this blog was free):

  1. Grouped together all diversified U.S. open-end equity mutual funds (i.e., the nine style-box categories; active and index funds; no ETFs)
  2. Limited to unique funds (i.e., oldest shareclass)
  3. Calculated the twenty year annual excess returns of the unique funds I grouped (excess returns = fund’s total return minus return of benchmark index assigned to the category that fund was assigned to)
  4. Sorted the funds into deciles by excess returns (top=group with highest excess returns; bottom=group with lowest excess returns)

There were around 680 unique funds that had twenty-year excess returns, so we’re talking about 68 per decile grouping.

Findings

Here’s the predictable stairstep pattern from the top to bottom decile when sorted by excess return:

bp1

Click here to read all of the different elements of this study, be see below for the one that I found most interesting:

As shown below the more-successful funds did indeed lag less often (measured as number of rolling 36-month periods during the twenty year span where the decile grouping had negative average excess returns) than the less-successful funds.

3-yr-lag

But it’s not like they were strangers to underperformance. In fact, the best-performing funds lagged their indexes in more than one of every three rolling three-year periods.So, investors in these funds spent roughly a third of the past two decades looking up, not down, at the index (when measured over rolling three-year periods).

My emphasis added.  As shown in the first chart, there are plenty of funds that have outperformed over the past 20 years.  However, any investor who expected consistent outperformance would have been sorely disappointed.  Even the best performing funds lagged their benchmark about one third of rolling 3-year periods.  The lessons should be clear.  Investors would be well served to do meaningful due diligence on active strategies before putting money to work.  Once investors feel confident that they have settled on strategies/management teams that they believe are likely to outperform over time, they would be well served to demonstrate a very high level of patience.

There may be times where all investments and strategies are unfavorable and depreciate in value.

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Momentum & Value vs. Growth & Value

September 20, 2016

At Dorsey Wright, we believe momentum can be used as a stand-alone investment strategy, however, combining it with other smart beta factors to which momentum is negatively correlated has its advantages.  We have referenced this in previous blog posts, noting that it allows for a portfolio to capture alpha at different periods of the market cycle, which in turn can reduce both drawdowns and volatility.   In this post we would like to discuss the potential benefits of combining momentum with value versus combining growth and value.   Furthermore, we will take a look the correlation of excess returns for each portfolio, and wrap things up by comparing the returns of each.

To begin, let’s take a look at the side by side performance (annual figures) for the products we will be using in our study:  PowerShares DWA Momentum Portfolio PDP, Russell 1000 Growth Index RLG, and the Guggenheim S&P 500 Pure Value ETF RPV.  We can reference this table in comparison to the results we get when combining the smart beta factors we mentioned earlier.  In order to get proper historical data, we used the underlying index (total return) for both RLG and RPV.  For PDP, total return figures were used starting on 3/1/2007.  The table below confirms that when using each of these products as a stand-alone investment product.  As we can see, momentum outperforms all other factors but also at a slightly elevated volatility.   Perhaps the most surprising theme is the underperformance of the growth factor throughout this time frame.

all

PDP inception date: March 1, 2007, RLG inception date: May 22, 2000, RPV inception date:   March 1, 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 prior to 3/1/2007.  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. 

Next, let’s take a look at the correlation coefficients when comparing the returns of each portfolio.  Below we’ve plotted the returns of each portfolio against each other on a year-to-year basis.   The correlation of excess returns between PDP and RPV came out to be -.50 during this time period, just slightly better then RLG vs. RPV (which registered -.40).   Again, both of these are impressive in terms of negative correlation which hopefully will give us the ability to capture alpha at different areas of the market cycle once we construct our portfolios.   Typcially our goal in doing this is lowering portfolio volatility and reducing max drawdowns when compared to using them as stand alone investments.

pdp-vs-rpv

PDP inception date: March 1, 2007, RLG inception date: May 22, 2000, RPV inception date:   March 1, 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 dividend prior to 3/1/2007.  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. 

rlg-vs-rpv

PDP inception date: March 1, 2007, RLG inception date: May 22, 2000, RPV inception date:   March 1, 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 dividend prior to 3/1/2007.  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.  

In conclusion, the portfolios we will construct are going to be based on a static allocation of 70%/30%.  To clarify, both the momentum and growth allocations will remain at 70%, while the value portion will be 30%.  The portfolios are re-balanced annually (although as we mentioned the allocation will remain static).    Looking at the table below, we can see that the momentum/value combination portfolio outperformed has over the growth/value combination.   The returns are nearly double, while volatility remains the same at 22%.   Market participants looking to combine a portion of their value portfolio with another allocation would certainly seem to benefit by using a momentum product vs. a growth product.

summary

PDP inception date: March 1, 2007, RLG inception date: May 22, 2000, RPV inception date:   March 1, 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 dividend prior to 3/1/2007.  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. 

DWA provides the underlying index for PDP (discussed above) and receives licensing fees from Invesco PowerShares based on assets invested in the Fund.

Some information presented is the result of a strategy back-test.  Back-tests are hypothetical (they do not reflect trading in actual accounts) and are provided for informational purposes to illustrate the effects of the strategy during a specific period.  Back-tested 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 also differs from actual performance because it is achieved through retroactive application of a model investment methodology designed with the benefit of hindsight.  

Neither the information within this email, nor any opinion expressed shall constitute an offer to sell or a solicitation or an offer to buy any securities, commodities or exchange traded products.  This article does not purport to be complete description of the securities or commodities, markets or developments to which reference is made. 

The relative strength strategy is NOT a guarantee.  There may be times where all investments and strategies are unfavorable and depreciate in value.  Relative Strength is a measure of price momentum based on historical price activity.  Relative Strength is not predictive and there is no assurance that forecasts based on relative strength can be relied upon.

 

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Weekly RS Recap

September 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 (8/29/16 – 9/2/16) is as follows:

ranks 09.06.16

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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Quote of the Week

August 31, 2016

Leda Braga via Financial News:

The world is learning the value of data-driven activities through science and technology. I can imagine in a few years’ time calling a self-driven cab – it will be reliable and cost-effective. Investment management is one of the most data-driven activities there are. It is the perfect ground for the systematic approach.

HT: Jerry Parker/Twitter

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Weekly RS Recap

August 29, 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 (8/22/16 – 8/26/16) 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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Weekly RS Recap

August 15, 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 (8/8/16 – 8/12/16) 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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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 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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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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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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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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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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