How Important Is Asset Allocation?

My favorite investment writer is William J. Bernstein, author of several investment books and the editor of the Efficient Frontier.  This morning I was scanning the data files of ITA Wealth Management and found where one reader clicked on one of the older articles from Bernstein’s Efficient Frontier. [Be sure to read the section, ‘Let George Do It.’]  I myself have written about the Brinson et. al. articles, published back in 1986 and 1991.  Those two articles are part of an asset allocation file 5.0 cm thick, stored in my filing cabinet.  Since those original Brinson papers were published, Ibbotson and Associates conducted several follow-up studies.  Their results as to the importance of asset allocation are all over the map.  The latest results, and I may not be up on the most recent studies, conclude that the broad movement of the market controls 75% of the movement of the portfolio.  The remaining 25% is divided equally between asset allocation and security selection.

The problem I’ve always had with these studies is that they segment asset classes into equities, bonds, and cash.  To my knowledge, no study breaks the asset classes down any further.  Why not divide equities into U.S. Equities, developed international markets, and emerging markets.  Divided bonds between U.S. Bonds and International Bonds.  Should anyone ever run into such a study, be sure to pass along the reference.

Bernstein sums up the asset allocation issue succinctly with this message to the small investor.

“So how important is “asset allocation?”  Wrong question.  More relevant to the investor is the question of how worthwhile professional efforts at both asset allocation and security analysis really are. The film “Less Than Zero” comes to mind.”

For small investors the answer seems to be:

  1. Decide how much equity you can stomach, and adjust your stock/bond allocation accordingly.
  2. Allocate your stock assets among a wide variety of global regions in a prudent manner.
  3. Let George do the rest while you get on with life’s more salient matters.

Can you stomach a stock/bond mix of 80/20, 70/30, or 60/40, knowing that even with 40% investing in bonds, over 90% of the portfolio volatility is linked to the 60% stock holdings?   For more on portfolio risk, reread this article, and then look up Risk Parity under the Categories section.

If I Were Starting A New Portfolio Today

Bike Races

What would a portfolio look like if I were starting from scratch today?  Here are a few basic principles to consider before making the initial Strategic Asset Allocation decision.

  • Begin by using index funds or index ETFs.  My preference, as Platinum members well know, is to use “non-managed” index ETFs.  Yes, someone needs to make a decision what goes into the ETF, but for practical purposes the Vanguard and iShares ETFs can be considered to be non-managed index Exchange Traded Funds.
  • Include a minimum of 10 to 12 asset classes.  This does not mean one will be invested in all asset classes at all times.
  • Asset classes will include U.S. Equities, Developed International Markets, Emerging Markets, U.S. REITs, International REITs, Precious Metals, U.S. Bonds, International Bonds, Commodities, and Cash.
  • Break the U.S. Equities Market up into different cap sizes, value, and growth asset classes.
  • Consider adding sector ETFs provided they rank higher than the U.S. Equities (VTI) standard.
  • Consider adding dividend oriented ETFs such as VIG, DVY, and IDV as potential investments.
  • Understand how to use either the ITA Risk Reduction model or the Momentum-Optimization Model to hold down portfolio volatility.

The following array of ETFs are a starting point for investors.  Many of the ETFs can be purchased commission free if one is a TDAmeritrade client.  Other discount brokers provide many commission free ETFs so do your own research in this area.  Controlling costs makes a huge difference over a lifetime of investing.

Rankings:  The 31 ETFs selected for this “Starter” Portfolio are ranked below.  In future blog entries I’ll likely try to narrow the options down to 25 as it takes a long time to run the analysis on a list this long.  The Buy-Hold-Sell recommendations trigger off this ranking data table.


Efficient Frontier:  The follow graph paints a quick picture of the Return/Risk ratio for the Starter Portfolio.  Minor adjustments in the asset allocation plan moved the current portfolio a small amount from the former or saved portfolio (blue dot).


Buy-Hold-Sell Recommendations:  This portfolio was set up following the optimizer with no consideration given to the momentum factor.  If one were following the ITA Risk Reduction model we would sell VWO and DBC.  Note how few ETFs are recommended when the price of the ETF drops below its 195-Day Exponential Moving Average (EMA).

The best buying opportunities are those ETFs with a Buy signal showing up in the far right column.

To see how rebalancing works when using the optimizer with the Feynman portfolio, go to this blog entry.  Be sure to download all the available Word documents.



Asset Allocation: Working With Optimizer

Engines waiting to asset ships through the Panama Canal.

Engines waiting to asset ships through the Panama Canal.

How does one combine the power of the Dashboard and the Hoadley optimizer when it comes to setting up an asset allocation plan for a portfolio.  ITA readers are familiar with the Dashboard as it is shown every time a portfolio is updated.  In this post I will explain how one might combine the Dashboard and Hoadley optimizer.  Since the Hoadley optimizer is confined to nine (9) asset classes, I’ve chosen to break the market down into the following asset classes:  Large-Cap, Mid-Cap, Small-Cap, Bonds, Developed International, REITs, Emerging Markets, Commodities, and Precious Metals.  This is a major reduction from the 17 or 18 asset classes we use with the Dashboard.  Here is an example of the percentages one might assign to each of these nine asset classes.

  1. Large – Cap (LC) – 12%
  2. Mid-Cap (MC) – 10%
  3. Small-Cap (SC) – 8%
  4. Bonds – 15%
  5. Developed International – 15%
  6. REITs – 20%
  7. Emerging Markets – 10%
  8. Commodities – 5%
  9. Precious Metals – 5%

This allocation plan gives us the 30% in U.S. Equities and 20% in REITs recommended by David Swensen.  We are a little light on bonds and a little heavy with our international markets allocation.  Also, Swensen did not include commodities and precious metals in his “Swensen Six” portfolio.

When using an optimizer such as the Hoadley, it makes good sense to include constraints or collars around each asset class.  We do the same when we assign a threshold percentage to each asset class in the Dashboard.  I’ve been experimenting with 40% constraints around each of the nine asset classes.  For example, Large-Cap stocks are targeted at 12%.  A 40% constraints works out to be 0.40 x 0.12 = 4.8% or rounded to 5%.  We can set constraints for LC to be 17% on the high side and 7% on the low side.  If those thresholds seem too large, then round down to 4%.  I prefer to provide a little more room for the optimizer to do its work so long as the end portfolio is not completely irrational.

Commodities and Precious Metals work out to 0.05 x 0.40 = 0.02 or 2%.  These two asset classes can be as high as 7% each or as low as 3%.  I’ll make an exception with these two asset classes as there are times when one does not wish to hold any shares.  Therefore, I’ll most likely set my limits or constraints to range from 0% to as high as 7% and let the optimizer do its calculations.

Rebalancing Portfolio: Blending Dashboard and Optimizer

Now that an optimizing tool is available, how does one rebalance a portfolio by combining the output of an optimizer with the Dashboard guidelines?  The larger topic is one of asset allocation so I will start with the following Dashboard that defines an asset allocation plan.

Dashboard:  Assume the following Dashboard lays out the Strategic Asset Allocation plan for a portfolio.  Only pay attention to the percentages with the white background as those are the target percentages.  Since the optimization software only permits 20 securities and nine (9) asset classes, I’ll focus on the asset classes as I use it for first order constraints.  As mentioned in an earlier blog, reducing to nine asset classes from the 17 listed below leaves us with these major classes; Large-Cap, Mid-Cap, Small-Cap, Bonds, Developed International, REITs, Emerging Markets, Commodities, and Precious Metals.  In the following Dashboard I do not include Precious Metals so we will work with eight asset classes.

Large-Cap includes value, blend, and growth in this portfolio and the target is to hold 14% in those three asset classes.  If we use a 30% threshold, then 0.30 x 0.14 = 0.042 or rounded to 4%.  This means we can go as high as 14% + 4% = 18% or as low as 14% – 4% = 10%.  When working with the optimizer we set the Large-Cap asset class constraints to be no lower than 10% with a maximum of 18%.  If our Large-Cap ETFs happen to be VTV, VTI, and VUG, we then leave it to the optimizer to filter out where the allocation is to fall and which ETFs will capture the allocated percentages.  If that does not fit the managers risk level, constraints can be placed on each ETF.  This permits the money manager to tilt the portfolio toward value or growth based on individual ETF constraints.

If we move to bonds and income as another asset class, we combine the target percentage in Cash (1), Bonds & Income (14%) and Emerging Markets Bonds (5%) for a total of 20%.   Using the same threshold or target limits of 30% we set the maximum constraint to be 0.30 x 0.20 = 0.06 or 6% above 20%.  This means we can go as high as 26% for bonds or as low as 20% – 6% = 14%.  If a money manager determine the threshold is too larger or too small, it can easily be adjusted.

With the optimizer software is is possible to use different thresholds for different asset classes.  I tend to use the same percentage threshold as there is little sense in over complicating this rebalancing process.  For each of the nine asset classes we set the maximum and minimum target limits and this is how one blends the Dashboard with the optimizer software projections.


Going Wobbly On Equities

With the market hovering around all-time highs, perhaps it is time to “go wobbly” on equities.  What might a 20-ETF portfolio look like if the bond requirement were elevated to 30% of the portfolio.  The following is not an operational portfolio, but a sample of securities one might use to build a well-diversified portfolio.

Efficient Frontier:  One ETF stands out in the following graph and it is gold (GTU) as there seems to be little bang for the buck.  The risk is high and return low.  This is not a winning combination.  If you look carefully you will see the current portfolio allocation (diamond dot) his a little higher return/risk ratio than the one advocated by the optimizer.  Setting a limit of 30% in bonds pulls the return/risk ratio down the EF curve.

Efficient Frontier

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Pay Attention to Your Portfolio: Sample Portfolio Included


Whether you are a beginning or experienced investor, it is important to review and update your portfolio annually if not monthly.  ITA portfolios are reviewed and updated every 33 days.  The updating difficulty is highly dependent on what securities are used to populate the portfolio.  If individual stocks are used the task is more onerous as each stock needs to be reviewed in detail.  Further, the evidence is that stock oriented portfolios do not outperform index oriented portfolios.  While we have limited evidence of this truth when it comes to private portfolios, there is an abundance of data showing under performance by actively managed mutual funds.  Check out Richard Ferri’s book, The Power of Passive Investing for detailed information.  It seems logical to reason that if the professionals are not able to outperform the market the small part-time investor is unlikely to do better.

What might a basic portfolio look like for an investor planning for retirement?  I limited this portfolio to 20 index ETFs and most can be purchased commission free from TD Ameritrade.  I have no financial connections with TDA other than I use them as a discount broker.  The first screen shot is the Efficient Frontier graph.

This material is not for publication elsewhere on the Internet.

Efficient Frontier:  The number of shares selected for each ETF was somewhat random so one should not expect to see the current portfolio (diamond dot) come close to approaching the optimized (red dot) portfolio.  By taking more risk we can increase the projected return.  If readers are not familiar with the efficient frontier, do some additional research by using a Google search.


Ranking of 20 ETFs:  The twenty ETFs listed below are sufficient to populate any retirement portfolio.  The diversity is global and it covers all the essential asset classes.  Some investors will want to make some substitutions.  For example, BND could be swapped for BIV.  I selected intermediate bonds as I think long-term bonds will take a bigger hit when interest rates begin to rise.  Regardless, we need to watch the price movement of all bond holdings as interest rates are going to rise.  We just don’t know when this will take place.  The top five ETFs are in most of the 11 portfolios tracked here at ITA Wealth Management.


Buy-Hold-Sell Filter:  The following table provides “decision clues” as to what one might do to further optimize this sample portfolio.  For example, I set the upper limit for VTI to be 50% of the portfolio and the optimizer advocates a full 50% be allocated to U.S. Equities.  As the money manager a decision needs to be made whether or not to purchase 411 shares (400 is fine) of VTI.  Where will the cash come from to purchase VTI?  Selling BIV is one place to raise nearly $9,000.  There likely are not sufficient selling opportunities to raise $33,000 in cash so one will likely settle for adding fewer than 400 shares of VTI.  These are the decisions you as money manager will make.


Photograph:  The jumping deer was captured by an action activated camera placed in a rural area in northwest Pennsylvania.


Asset Classes: How Many Should You Own?

With much recent attention given to answering the money managers most perplexing question, what percent of the portfolio should be invested in each asset class, the whole idea of how many asset classes to include in a portfolio has been neglected.  William Bernstein answers the question this way.  “You might as well ask the meaning of life.  About all one can say is, more than three.”  The major three would be equities, bonds, and cash.  However, that does not provide a well-diversified portfolio.

Many of the ITA Wealth Management Portfolios hold as many as 18 asset classes (including cash) if one breaks the U.S. Equities market into the “Big Nine.”  If I were to answer the above question I would recommend between seven (7) and nine (9) at a minimum.  Here are my recommendations.

  • U.S. Equities
  • Developed International Markets
  • Emerging Markets
  • Domestic REITs
  • International REITs
  • Commodities
  • Domestic Bonds & Treasuries
  • International Bonds
  • Cash

Precious metals can be considered a separate asset class or be included under the commodities umbrella.

What ETFs might one use to populate the above asset classes, excluding cash?  To answer the question I’ll walk readers through an optimizer analysis.  First the efficient frontier, followed by a ranking so the individual ETFs are identified, and then on to the buy-hold-sell worksheet.

Efficient Frontier:  The optimized portfolio is just off the graph in the upper right-hand corner of the following graph.  Based on the shares I selected for each ETF in this sample portfolio, the portfolio merits a little tweaking to reach full optimization.


ETF Rankings:  The following list of ETFs will cover all eight primary asset classes.  The “Big Six” U.S. Equities asset classes are broken down into VTV, VOE, VBR, VUG, VOT, and VBK.  These are all Vanguard ETFs.  In addition, the total U.S. Equities market is covered by VTI.  If a money manager wanted to hold to 20 ETFs, but needed to make room for a special ETF such as DVY, my suggestion is to eliminate a bond ETF such as BIV, LQD, or JNK.


Buy-Hold-Sell Ranking Filter:  The number of shares for each ETF was selected at random so I was not following any specific Strategic Asset Allocation (SAA) plan.  If the current held shares was aligned with the SAA, then some adjustments are in order.  What changes would you make?  Readers are welcome to comment on what they would do with this portfolio of 20 ETFs.


Optimizing The Swensen Six Portfolio

The prior blog post shows what happens when no constraints are placed on an optimized portfolio.  To follow up on the Swensen Six post, assume we begin with the asset allocation plan recommended by Swensen and then optimize the six ETFs using reasonable constraints.  The following efficient frontier graph show the Swensen allocation (diamond dot) falling well short of the optimized portfolio (red dot).

Efficient Frontier:  While the Swensen Six, with the basic allocation, comes close to the efficient frontier, we can improve on the potential return.  Those changes will show up in the second screen shot.


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Ranking Vanguard’s Sector ETFs

One of the portfolios up for review this week is the Gauss.  In preparation for that update I’m looking for ways to improve the performance of this portfolio as it still lags the VTSMX benchmark.  I thought I would rank the Vanguard sector ETFs (minus VNQ) to see if there are any potential “momentum” ETFs among this group.  Below is the latest ranking.

Sector Rankings:  The financial (VFH) ETF rises to the top and that is of particular significance as that is the same ETF that is flashing a Buy signal by the “Delta Factor” projections.

Sector Rankings

Ranking Model Favors Small-Cap and Value ETFs

While the Quantext Portfolio Planner (QPP) software shows a high correlation between the “Big Nine” U.S. Equity asset classes, the Ranking Model, recently unveiled, shows a definite bias toward small-cap and value ETFs.  Below is the data table based on the latest three years of data.  To readers who are aware of the Fama-French research, this comes as no surprise.

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