ITA Wealth Management

If you are building a portfolio from scratch, are you better served using Vanguard or Barclay (iShares) ETFs?  That is the question I posed to myself now that I have access to the Quantext Portfolio Planner. What I did was to populate a portfolio using the “Big Six” ETFs plus developed international, emerging markets, and REITs.  For Barclay or iShares I used the following ETFs.

Barclay

• IVE
• IJJ
• IJS
• IVW
• IJK
• IJT
• EFA
• EEM
• ICF

I invested 11% in each asset class with exception of international.  For that asset class, I invested 12%.  The time of examination was from 10/8/03 through 10/8/2008 or a five-year period.  Here are the results.

• Return = 10.90%
• Standard Deviation = 20.18%
• Diversification Metric = 12%  (See definition below)
• Portfolio Autocorrelation = 26.51%  (See definition below)

Vanguard

From the Vanguard ETF garden I plucked identical asset classes.  These are the ETFs I selected.

• VTV
• VOE
• VBR
• VUG
• VOT
• VBK
• VEU
• VWO
• VNQ

Again, I invested 11% in each asset class with exception of VEU, the international asset class.  I invested 12% in VEU just as I invested 12% in the international iShare, EFA.  Here are the Vanguard results.

• Return = 12.32%
• Standard Deviation = 9.11%
• Diversification Metric = 61%
• Portfolio Autocorrelation = 7.91%

It is obvious Vanguard is the better choice based on this analysis.  The return is greater with lower SD.  The Diversification Metric is much higher and the Portfolio Autocorrelation is closer to zero.

What is portfolio autocorrelation?

QRP and QPP both calculate an historical statistic called portfolio autocorrelation.  This is a recent feature and is not yet included in the user manual / textbook.  Portfolio autocorrelation is the correlation in portfolio returns from one month to the next.  If it is positive then high returns tend to be followed by high returns and vice versa.  If portfolio autocorrelation is negative, then the portfolio returns tend to be ‘mean reverting’ which means that very high return months tend to be followed by returns closer to the mean–the portfolio tends to damp out periods of very high or very low returns.  Portfolio theory generally assumes that autocorrelation is zero–the random walk.  QPP and QRP model the market as though autocorrelation is zero, and the metric shown is for historical performance.  If you have a portfolio that shows a lot of positive autocorrelation, this is a flag–this means that big swings get amplified.  These effects are widely debated, but there is evidence that they can be meaningful:

What does the Diversification Metric (DM) mean?

In our latest release of the software, we have added a new analytical function that accounts for non-market correlation between portfolio components.  This is important because many asset classes have correlation to one another beyond what can be captured by Beta.  This is a major challenge for many portfolios, but especially those with concentrations in a sector.  This problem is described in a recent article. (Go to QPP site to find active link.) Our software generates a statistic that measures how effectively the non-market component of returns actually diversify one another.  In the best possible case, the non-market component of returns would be totally uncorrelated with one another.  In the worst case, they would be highly correlated.  The diversification metric (DM) measures how un-correlated the non-market returns are across the portfolio.  Higher values of DM mean that the non-market component of returns shows low correlation across the portfolio.  Higher DM means that your are getting more real diversification out of your portfolio.

I’ve participated in a few discussions as to whether it is better to diversify the portfolio by using Sectors or Asset Classes.  It has always been my contention that spreading ones wealth over numerous asset classes is preferable to using sectors.  This morning I set up a portfolio built around ten sectors.  Here are the ETFs I used in this portfolio.

• IYK
• IYC
• IYE
• IYF
• IYH
• IYJ
• IYM
• IYW
• IYZ
• IDU

In each of the 10 iShares, an investment of 10% was allocated.  The test was to check the projected annual return, SD, Portfolio Autocorrelation, and Diversification Metric and compare them with the same metrics from a portfolio built around asset allocation.  It is not even a contest.

1) The projected return for ETFs using asset allocation is nearly 2% better when compared with sector diversification.
2) The standard deviation is more than double if one goes the sector route.
3) The Portfolio Autocorrelation is almost four times as high with sectors.  This is “Red Flag” country.
4) The Diversification Metric is about 1/3 with sectors.  That does not surprise me as that has been my argument against diversifying via sectors for a long time.  This only provides data for something I thought to be true.

To improve the sector diversification, I added an international ETF as well as an emerging market ETF.  The results did not change significantly.  In fact they got a little worse in three of the four metrics.

Until there is compelling evidence, I plan to stick with diversification via asset classes and let others use sectors.

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Lowell Herr

Photograph: Weaver in China

Next month I plan to examine the software, Quantext Portfolio Planner in greater detail.  Over the next few weeks you will see some very brief portfolio analysis using this software.  There are many articles on the Internet related to this software and the whole idea of reversion-to-the-mean (RTM). I suggest interested investors read up on this approach to portfolio management.

The goal is to design a portfolio around this forward looking approach to populating and managing investments.  Stay in touch for more information.  The development of the portfolio will likely take place over on the Premium Content side of the blog.

Lowell Herr

Photograph:  Science Museum in Florence, Italy.  This is where one will find many pieces of apparatus used and designed by Galileo.  Also, his middle finger is on display, likely saluting his past foes.

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Here is an example of an analysis for the Passive Portfolio, an account that has been in operation for nearly eight years.  The portfolio holds the following investments with the respective target percentages.

• IVE = 14%
• IJJ = 15%
• VBR = 13%
• IVW = 13%
• VOT = 13%
• IJT = 5%
• EFA = 15%
• ICF = 5%
• EEM = 7%

The projected return for such a portfolio is 11.03% and the risk is 16.44%.  I consider this risk to be too high and unacceptable.  This issue is, what does one do about it?  That is now part of the analysis.  What ETFs should be replaced to maintain a return greater than 11% while reducing the standard deviation?

Lowell Herr

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Investors will find the Asset Allocation Performance Spreadsheet of interest.  Move to the right-hand edge of this page, scroll down to Links and click on the second option.  Download the Asset.xls spreadsheet.  If you have questions, post them in the Comments section of this entry.

The spreadsheet includes data on eight different asset classes beginning in 1989. Be sure to check out all three worksheets.  The first includes the RAW data.  Performance shows how the asset classes rank from year to year and the third worksheet shows the “Horizontal Performance” or how well each asset class performed over the period studied.

Lowell Herr

Photograph:  Mark Twain sculpture

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Why use the principles of asset allocation when constructing a portfolio?  The logic for this approach is based on research done by Ibbotson & Associates and many others.  Look up Ibbotson on this blog for more information.  We skew or tilt the portfolio toward the value side of the market spectrum as value tends to outperform growth over the long run.  Fama & French have studies that back this position.  Personal experience also supports this conclusion.

If one needs more supporting proof as to the advantages of asset allocation, I highly recommend skeptics begin reading my five top investment books.  Some readers will be won over to the asset allocation argument quickly, while for others it may take years.

Lowell Herr

Photograph:  Wetlands in Ocean City, Maryland

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“Academic theory and the Wall Street establishment (including the financial media) are clearly at odds over the issue of the value of security analysis and stock selection. The active managers of Wall Street and the mutual fund industry must believe that they can add value or they would be committing economic suicide. Academics have no such biases. They study the historical evidence and report their findings.”

The above quote comes from Larry E. Swedroe’s book, “What Wall Street Doesn’t Want You To Know: How You Can Build Real Wealth Investing in Index Funds.” While I subscribe to the above argument, there is still room for additional research when it comes to the benefits of Asset Allocation. For example, I would like to see more work focus on portfolios that are using multiple asset classes as opposed to the three and four-factor models that have been studied. Although I know it is difficult to gain access to private investor data, it would be useful to see studies that analyze more than large pension and mutual funds. The new research should embrace a similar number of portfolios managed by small investors.

A comprehensive study would include investors who build portfolios using a variety of index funds as well as portfolios constructed using only individual stocks. Would such an investigation support the Ibbotson & Associates study that portfolio return is 100% dependent on portfolio policy and that stock selection and market timing add no value to the portfolio return? I need to quickly add that the Ibbotson study looked at averages. Individual managers will break from the average results to both the high and low sides of market returns.

When it comes to the AA-Mosaic, Mosaic2, and GLW portfolios, we seek to outperform the Vanguard Total Market Index (VTSMX) by tilting the portfolio toward the value side of the market spectrum and allocating a significant percentage of the portfolio to developed international countries as well as emerging markets. Right now, the decision to go with international and emerging market equities is having a negative impact on portfolio performance. Including these asset classes is definitely a policy decision and not one tied to market timing.

Lowell Herr

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Photograph: Entrance to the interior of Fort McHenry in Baltimore, MD

Harry Truman once said, “If you want to live like a Republican you’d better vote like a Democrat.” Which party is best for the stock market? Is there a statistical difference to market returns and risk when the country is controlled by one party over the other? Contrary to popular belief or urban myth, the results may astound many readers. Here are two references that point out the differences when the different parties are in power. Read on.

http://money.cnn.com/2004/01/21/markets/election_demsvreps/

The above link is an article written by Alexandra Twin, a CNN/Money Staff Writer. In her article, the academic article reference below is mentioned. Note the graph in the article ends in 1999. The market from 2000 through today would further depress the Republican record as the annual return is approximately 1% for the past eight years.

http://personal.anderson.ucla.edu/pedro.santa-clara/Politics.pdf

The above link takes one to the academic paper supporting the summary provided in the “popular” article.

And here is another article I recently found.

Lowell Herr

Photograph: Lucy, poodle belonging to daughter-in-law

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The Fundamental Index hypothesis has its critics and one is none other than John Bogle. While Bogle will concede cap-weighted indexes such as the S&P 500, as represented by VFINX, will allocate more money to over-priced stocks, he also contends that we do not have the foresight to determine which stocks fall into that category. Therefore, it is better to stick with low-priced cap-weighted index funds.

Paying higher fees for index funds constructed around fundamental principles of sales, cash flow, book value, and dividends is my primary concern. Is it possible to engineer a portfolio by over weighting mid-cap value and small-cap value through the use of inexpensive ETFs or index funds, thereby capturing the value tilt at lower cost?

Photograph: Elan Gallery & Gifts, West Linn, OR

Back in the mid 1990s, I heard about a company named Cisco Systems. At that time I knew little about routers, but I had a sense they were going to be big as the Internet grew. With very little information and analysis, I purchased shares, only to watch them rise in price almost daily. A relative was working in the bay area so I called him and asked him about Cisco. Yes, he was very aware of the company. In fact they were in the process of expanding their campus and expected to double their workforce within a short period. With no more analysis, I added shares to my holdings despite the higher price.

The price of CSCO continued to climb. This stock did so well it was soon the stock of choice for charitable contributions. With that background, let me shift to a long quote from Rob Arnott’s book, “The Fundamental Index.”

“Having a clear and informed belief regarding price efficiency is one of the most critical elements to fomulating an investment strategy. Consider this: $500 billion lost in only 30 months. It is a staggering amount of money–more than 50 times the collective annual casino takings from Las Vegas tourists and two-and-a-half times the estimated losses domestic airlines and associated travel industries suffered after September 11, 2001. Shockingly, it’s more than 100 times the losses incurred in the collapse of Long-Term Capital Management (the most spectacular hedge fund collapse in history) that many knowledgeable people–including former Federal Reserve Board chairman Alan Greenspan–thought could potentially bring down the entire global economy.”

“This massive wealth destruction wasn’t the result of rogue traders with leveraged balance sheets. It occurred in the stock market–in the 30 months following the collapse of the technology bubble in March 2000. The $500 billion figure isn’t even the total stock market loss over this dreadful stretch. This astronomical loss resulted from one stock: Cisco Systems, the largest stock in the world based on market capitalization at the peak of the tech bubble. This stock was valued at nearly $600 billion at a time when its sales were less than $20 billion, its trailing 12-month operating earnings were less than $3 billion, its cumulative profits since inception were well under $8 billion, and it had never paid a dividend. Additionally, Cisco’s workforce numbered fewer than 30,000 people.”

“Index fund investors as a group–people who believe in market efficiency and who do not believe in betting on single stocks–lost nearly $100 billion in Cisco. An average 401(k) participant with $100,000 invested in a Standard & Poor’s (S&P) 500 Index fund lost more than $45,000 in those 30 bleak months, almost $4,000 of which was lost on Cisco alone.”

Why go into such detail about this single stock? Arnott is making a strong case that price alone is a dangerous metric and that even index fund investors, who embrace diversity, can be hurt when funds are built around cap-weighted stocks. He is making the point one would not be hurt the same way if the funds are constructed around fundamentals such as sales, cash flow, book value, and dividends.

Just to complete the story, I still hold shares of Cisco with a rather nice IRR value since the shares purchased many years ago were bought at very low prices.

Lowell Herr

Photograph: Image by Dennis Dean