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Smart(er) Beta and a New Tactical Index

Recently there has been considerable focus on smart beta or fundamental factor weighted indexing.     

Smart beta indexes are transparent, rules-based indexes designed to provide focused exposure to specific factors, market segments or investment strategies. Typically not weighted by market capitalization, smart beta indexes provide unique opportunities for investors to increase portfolio diversification, reduce risk and enhance returns over time.

Supporters have been quick to point out that that they have outperformed the S&P 500 for the past 5 years.  

Where Active and Passive Meet

At the core of these strategies is a transparent, rules-based design that provides focused exposure to specific factors, say for instance dividends or revenue.

Academic research on many of these strategies has found that they have historically delivered excess returns and improved risk-adjusted returns over long time periods, and so they are called “smart.”  But as should be pointed out, styles and factors drift in and out of favor, so it’s important to realize that “smart” strategies will have periods of underperformance when compared to a benchmark.  It is often this point that strategist who speak against smart beta, cite.

To my knowledge, most of the these new indexes are based upon preset fundamental factors.  But what if we instead used an adaptive strategy that again was rules-based, but also shifted as styles and asset-classes moved in and out of favor?  Would not that be, umm…smarter?   This would also overcome my own personal objection that smart beta will not be sufficient to protect investors during the next bear market.  (Yes, I still believe in them).   Smart or not, when the ships starts to sink…everyone aboard is going down.  Thanks, but I want my strategy to have a lifeboat.

There are a number of index-based financial products, most of them tied to a passive indices such as the S&P 500, the DJIA, FTSE 100, or the Hang Seng Index.  Less common are index products linked to a formulaic index such as these newer smart beta strategies.  Personally, I think there is a ton of opportunity here.

Which is why I am pleased to announce that a new global and 100% formulaic index I developed is now live on Bloomberg.   This new index utilizes the same systematic, rules-based approach that I use to develop Portfolio Cafe models and combines the use of dual momentum, trend, and volatility analysis.

The index actively rotates between U.S. Equities, International Equities, Emerging Markets, Commodities, REITs, International Bonds, U.S. Treasuries, Corporate Bonds, High Yield, and Gold.   The index can be as much as 100% cash and bonds to as much as 60% equities depending upon market conditions.

While monthly rebalancing is the normal best practice, a new aspect to the construction is a “time diversification” element that is achieved by rotating a fixed percentage of the index every so many days – each held for a 28 day period.   The purpose of this is to capture emerging opportunities and to reduce the exposure to market changes that may occur shortly after a monthly rebalance with the goal of a very smooth equity curve.

This new index was designed to be the basis for several new financial products under various stages of development – more about these to come.

lifeboatThe index strategy has done a nice job of capturing upside while keeping risk low…and as is common among all of my strategies, the lifeboat is included as part of the package.

Summary Statistics

 
CAGR
Sharpe
Correlation to SPX
Maximum Drawdown
Index Strategy17.1%1.030.03-13.5%
S&P 5006.7%0.28-55.2%

Index Annual Performance

 
2007
2008
2009
2010
2011
2012
2013
2014
Index Strategy17.8%19.1%21.4%9.2%37.8%4.5%12.3%9.8%
S&P 5005.1%-36.8%26.4%15.1%1.9%16.0%32.3%8.0%
Index strategy performance for period 12/29/2006 to 07/25/2014

 

bbindex

Three Investment Lessons from a Baseball Legend

tony gwynnGrowing up I was a baseball fan for as long as I can remember.  The Reds and the Mets were my early favorites but once my parents set down roots in the midwest I became the Cardinals fan I remain today.

Despite team favorites, there was one baseball player that all fans liked and his name was Tony Gwynn.

Tony Gwynn was one of the best pure hitters of all-time. He won the batting title 8 times and a .338 lifetime batting average (for those non-baseball fans, it was the highest career average for any player who started after WWII). 

And when Tony Gwynn was eligible for the Baseball Hall of Fame – he got in on the first ballot (not that there was any question he would get in on the first try).

This past weekend, Tony Gwynn passed away at the age of 54 from salivary gland cancer and the baseball world is mourning.

So what does a baseball legend have to do with successful investing?

LESSON #1: PROLIFIC AND CONSISTENT SMALL HITS

For Tony Gwynn, it was about getting base hits. He didn’t try to hit a home run – he would just beat you with singles and virtually never strike out. Sure, the home run hitters get all the glory, but Tony didn’t care about the glory, he beat you with the singles. And it was those thousands of nice and easy base hits that added up to a hall of fame career.

Investing Takeaway:  When it comes to investing, what makes a long term winning track record is not the occasional 10 bagger that you are lucky to find yourself owning.  Rather, it is the small but consistent wins that compound over time…the dividends collected, option premium earned, the costs saved, and the consistent profits that can be earned from trading a system that provides positive mathematical expectancy.

Show me an investor who has blown up his portfolio and I can guarantee you that it was because he or she was swinging for the fences…always trying to hit the ball over the fence and paying little attention to managing risks or capital control.  As in baseball, small consistent wins can keep you in the investment game for a lifetime.

LESSON #2: LONGEVITY
Tony played for twenty years. By knowing what he was good at and focusing on that one thing (base hits), he played professional baseball for two decades. He knew by just getting hits, it will help his team win and he wouldn’t be the “flavor of the month”. The final result was 3,141 hits over twenty years.

Investing Takeaway:  Successful investors find what they are good at and once they do, they stick with it.  A successful investment approach is one that meshes with an investor’s personality and goals.  For some investors, it might be the intensity of day trading and the idea of going home flat every night that works.  For others, it might be growing a portfolio of dividend achievers and letting compounding do its magic over time.  Regardless of where you fit within the spectrum, the key is to find an approach that fits and to stay with it.

Ideally, I want an approach that can capture most of the upside while protecting me from most of the downside.

For me, systematic, rules-based investing is the approach that I am most comfortable with.  It keeps my emotions in check, it provides discipline, it manages downside risk, and it automatically adapts to the ever changing markets.   I don’t have to worry about guessing what the markets are going to do next.  I know that my models will adapt and that’s all I need to know.

Investors who are constantly chasing systems and investment fads typically make no progress and are often near dead broke.  There is no Holy Grail and no Get Rich Quick Scheme…give it up before its too late if you find yourself getting caught up in this.

LESSON #3: LOYALTY
In a era where professional athletes hop from team to team for a bigger paycheck, Tony Gwynn was loyal. He played his entire career for ONE team, the San Diego Padres. The relationship he had with San Diego fans led to the team literally building a statue of Tony at the ballpark!

Investing Takeaway:  Once you have found the approach that works for you, follow it day in and day out.  Yes, I know that can be boring.  Within all us is the desire for excitement and for change….but when it comes to investing, the more times we can repeat a process that has positive mathematical expectancy, the more likely we will be successful.  Don’t believe me?   Think about a casino.  They know and they have built their lavish buildings on players staying at the table, hand after hand, roll of the dice after another.  The problem here though is they have the edge and you don’t.   In investing, it is easy enough to find an approach (Portfolio Cafe, cough, cough) that gives YOU the edge.  You simply have to be willing to stick to the discipline of following a system.  Find what works and become loyal to the process.

They broke the mold when they made Tony Gwynn, both the baseball player and the man. And there are many lessons to be learned by looking back and reflecting on his life.

Monthly Asset-Class Scoreboard – Are Stocks Falling From Grace Despite the Record High Stock Market Averages

Over the past several months our ETF models have made shifts away from high beta to dividend paying stocks, REITS and bonds.  In fact some models such as Sleep Easy have been holding a bond position for several consecutive months.  To a casual observer this may come as a surprise since what gets reported on the evening news is the record high stock market averages.

But behind the headlines, some interesting trends have developed.   The following is a list of the major asset-classes I track on a monthly basis.  They have been ranked on the basis of their combined  3-month and 6-month trailing returns.   I will let the diagram do most of the talking.

asset classes

Not Just Hypothetical…A Look at a Real-Time Fund Trading Systematic Models

flasksTwo of the common things that I hear when “non-believers” review the performance returns of systematic investment models is “that’s just hypothetical” or “that’s a backtest”.   I get it.  Sometimes the numbers look to good to be true.

In a moment, I will share with you the results of a live, real money account, but before I do, let’s review.

It is agreed that a model’s inception often begins with testing ideas and then ultimately a backtest.  That is the nature of model building.  As a general rule of thumb, all model ideas that I develop must be grounded in academic research and the efficacy of the approach must have been clearly demonstrated.  Only then do I become interested.

“What Works on Wall Street” or more recently, “Quantitative Value” are excellent examples of books that can be mined for investment concepts that are both well thought out and well tested.  There is also the content to be found in the numerous white-papers that can be found at sites like SSRN.

As a general rule of thumb, the simpler the model, the better I like it.   Moving average systems such as outlined in Meb’s paper are about as simple as you can get but the approach works by managing downside risk and thus positively impacting long term compounding.

The use of momentum is hardly new.  There are countless papers and references in the literature discussing its efficacy across all asset classes.  There are hedge funds and large institutions managing billions of dollars around momentum strategies.

Take a simply moving average system and add a relative momentum ranking and you’ve got the basis for a solid model that can be applied across just about any portfolio.   If you want to get extra fancy, you can add an absolute return criteria and perhaps some measure of volatility but my point is that a successful system does not require hard to understand or arcane rules.  In fact, I think the more specific and complex a model becomes, the more likely I believe it will eventually fail in the future.

When it comes to stock portfolios, the above cited books display quite clearly that factors such as value, yield, and momentum have consistently been attributes of stocks that beat the market.   Intersect all three attributes and you’ve got a winner.

Models work because they remove human emotions.  The reason Dalbar has been reporting for decades that individuals underperform (by a lot!) the indices is because of no other reason than human behavioral flaws.  It’s not our fault…it’s how we are wired…it’s nature.  I am sorry to be pragmatic but it’s also why I believe that the Dalbar report will never show anything different.

To overcome this deficit…we use models because models are concise.  Models are consistent.  Model’s do not care about the “story”.  Models only care about statistical and factual data that can be accurately measured.

So now back to topic.

During the past year I was asked if I would be interested in managing a fund that utilized the exact models you will find on this site.  I agreed.  An account was established at Interactive Brokers and was initially funded with multiple six figures.  I mention that only so readers understand that there is real skin in the game and real accountability.  I also mention that so that you can get a sense of my order size which ranges anywhere from a few hundred shares to a few thousand shares.   In other words, not too different from what I would imagine is the order size of most readers.

So at the beginning of the investment process I created a blend of the following models.

  • CANSLIM
  • Smallcap Fiscal Momentum
  • Smallcap Rockets.
  • Dividend Value

I more or less evenly allocated the capital across these models and began to follow the exact recommendations generated each Monday morning.  So in other words, I was doing exactly what I recommend that our members do.  I was also eating my own cooking as all investment newsletter editors should.

In February I introduced a MidCap model to the fund that used many similar factors.  This was done to improve trading liquidity.

Here now are the results I promised earlier:

account

Fund Return Using Our Systematic Models

YTD return:  9.98%

Sharpe:  1.60

Sortino:  1.79%

At its peak in early April, the fund was up about 12%.

In the same period a blend of small and mid caps (50% IWM and 50% MDY) is up 0.35%.

I am happy with the fund’s start for several reasons, aside from the obvious positive performance differential.

What I am most happy about is that I think this demonstrates that trading model signals can be translated into real-time trades.  Aside from three stocks where I was uncomfortable with the bid/ask spread size or I felt the volume was insufficient, all trades were taken.

The dollar weighted returns from the “published” model returns as compared to the  “actual” real returns are almost the same.  This suggests that investors can indeed take a published model and reasonably expect to achieve similar results even with slippage and transaction fees considered.

Second, I think the good news for individual investors is that it demonstrates that there is still alpha out there if you are willing to exploit stocks that are either misunderstood or simply not followed as in the case of many small caps.

Proponents of “efficient market theory” will tell you that everything is already priced into the market…to invest passively.  But our results and the results included in the books such as “What Works on Walls Street”  will tell you differently…that market beating returns are possible when using a systematic and disciplined approach that compound factors leading to positive expectancy.

I realize that as the fund grows that I will be eventually forced to increase market cap to maintain liquidity and some of the “size” factor I am exploiting now will diminish.  But even in that case, models such as Dividend Value where the average market cap is well over $1 Bln, continue to display a high degree of positive expectancy.

In the coming weeks I will be exploring signal to trade automation.  I am also tilting my bias to large-caps given the recent underperformance of small caps.

I will keep readers updated on future performance, but for now, let’s agree on one thing – this is not just hypothetical.

 

 

New Global Tactical Model Series added…

For the convenience of those interested in tracking Mebane Faber’s popular Global Tactical Asset-Allocation model that he originally featured in his 2006 paper, I have added that model among our tactical ETF models. As I discussed in a Seeking Alpha article, I have replaced the original 10 period moving average with the 8 period…it just seems to work better. Overall though, readers will find this tracks pretty close to what Meb has on his own site.

As some of you are probably aware, Meb has published some updates to his original paper, the most recent being February 2013.  In the latest update, Meb introduced 8 additional asset-classes for a total of 13.   He also discussed several alternative methods of allocating funds, one which included the discussion of combining a momentum-based ranking system along with the original use of the trend following moving average.  (Links to both the original and the updated paper can be found on the Reference page.)

This inspired me to conduct some research of my own given the tremendous success I have had with applying momentum across a variety of asset-classes.  After testing many combinations of factors, the finished model ranks the 13 ETFs using a ranking system that combines both momentum and volatility.  Any ETF that is not above the 8 period moving average is eliminated.  Finally, instead of selecting  the top five ETFs as Meb does in his paper, I settled on buying the top three in equal weight as the best choice. Below in the table are my test results.

Number of ETFs
CAGR
Sharpe Ratio
Maximum Drawdown
Total Return (dividends reinvested)
115.8%.72-21.6189.6%
216.3%.85-13.2198.1%
316.8%.92-13.5207.4%
413.4%.77-16.7148.4%
512.3%.74-19.7131.7%

To summarize, less than three and the portfolio volatility reached unacceptable levels and more than three, the portfolio returns quickly diminish.   Thus, I have aptly named the new model, GTAA – Focus Three.

For some who may think that owning just three ETFs each month is too limited, remember that each ETF is in itself extremely diverse.  For example, the iShares Russell 1000 Growth ETF, a current model holding, is diversified with 625 separate securities.

I personally really like the stability of this model and how well it navigated the the 2007-2009 period.   Unlike some of my other models, this model does not include an inverse fund but never the less, the inclusion of fixed-income ETFs provided enough opportunity to keep downside risk contained to acceptable levels.

For individual investors looking for a single model that covers most all of the major asset-classes, I believe this could be a good choice.  And for financial advisors who want a simple but effective way to managed their client book, I would have to think this model could serve them well and provide the ability to build a very scaleable practice.