Global Giants and Diversifiers To Supercharge a Portfolio

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 |  Includes: CPK, DTE, FCN, FE, GE, GIS, NOC, NVS, PEP, PG, SO, TM, TOT, WFC, WGL, WMT, WYE
by: Phil DeMuth

In our new book Yes, You Can Supercharge Your Portfolio, Ben Stein and I describe a series of investing stages. We hypothesize that most investors do not begin with an overarching theoretical framework for their holdings, but rather own what we term a “Glom” (for agglomerative) portfolio, made up of scraps of whatever looked good at the time they made the initial purchases. The problem with this “cats and dogs” approach is that there is no overall sense of what it means: the likely risks and returns are unknown, as is its suitability to meet the investor’s short- , intermediate-, and long-term goals. There is no match between assets and liabilities, or knowledge of whether the assets are invested in an efficient manner.

We recommend that investors in this position could “supercharge” their holdings by moving toward what we call a “Glob” portfolio – a great global glob of securities held in some conventional, common-sense allocation. This is essentially the John Bogle move: using index funds to pursue a low-expense, highly diversified set of holdings. This captures the free lunch of low expenses and a market-wide degree of diversification to improve the risk/return portfolio efficiency in harnessing the returns from global capital markets. For many investors, we think this would be a giant step forward.

A third stage involves “supercharging” the globally-indexed portfolios. This involves the use of portfolio management tools as opposed to the seat-of-the-pants approaches outlined above. Here we employed Geoff Considine’s Quantext Portfolio Planner Monte Carlo simulator [QPP] to address the remaining deficiencies in the “Glob” portfolios. In the first place, we used targeted assets – ETFs and/or individual stocks – that had low correlations with the rest of the global portfolios to boost the risk-adjusted returns. An example of how this worked during the recent market correction is shown in a previous article.

Then we recommend adding bonds to control portfolio volatility, and tuning the overall allocation so that it meets the investor’s goals. For most, the big long-term goal here is successfully preparing for retirement.

The advantage of the supercharged “Glob” portfolio is that the core of the portfolio is familiar, anchored in major market indexes. The tracking error is not severe – investors holding this type of portfolio will not be shocked to discover large discrepancies between their holdings and the daily headlines in The Wall Street Journal. The projection is for the returns to be slightly better and the risks to be slightly lower – small but meaningful differences that will compound into a large advantage over time.

Finally, the book comes full circle – to a portfolio composed entirely of individual stocks, this time not picked haphazardly but with each security selected with an eye toward its contribution to the portfolio as a whole. We do not recommend this for most retail investors, because individual stock selection calls for skills that may be beyond their expertise, and because the tracking error may be considerably greater than with the “Glob” portfolios above. For example, if this portfolio were to go down when the indexes are up, amateur investors might become discouraged and sell when the market is low, leaving them worse off than before.

While we did not recommend any particular portfolio, but we did sketch one methodology how such a portfolio might be assembled, following a fairly conservative approach (pp. 140-148).

We began by looking at global-dominating companies – giants with over $10 billion in market capitalization whose stock prices had exhibited low volatility (standard deviation) in recent years. Next, we weeded through the list to pick just one company from each industry group: for purposes of this illustration, we didn’t want to own four oil companies, four pharmaceutical companies, etc. – not that there’s anything wrong with that. We came up with a core list of ten companies.

Next, we sought to diversify these global giants by adding ten more companies that had a low-correlation to them and that appeared to provide a valuable diversifying function when included with them. That is, these companies tended to either boost the expected returns of the resulting portfolio, lower the expected standard deviation, or both. Many of these companies also were low in volatility themselves, and some of them had performance histories that were largely independent (low R-squared) of the stock market as a whole. The final list is shown below.

click all images to enlarge

We allocated 80 percent (8 percent/each) to the ten global giants and 20 percent (2 percent/each) to the ten diversifiers. We used the Quantext Portfolio Planner to gather five years of historical data (from 12/31/2001 through 12/31/2006) and project future risks and returns for the resulting portfolio. While the individual company names taken one-by-one did little to get our hearts racing, taken as a group they seemed to display admirable characteristics – making it an almost textbook example of Modern Portfolio Theory in practice, as shown in the following screenshot from QPP.

Note that the expected return (12.4 percent) is even higher than the expected standard deviation (10.3 percent) – a feat seldom achieved in markets with normal levels of volatility.

A further look at the correlation table shows this to be a highly diversified slate, with the highest correlation of any security to the portfolio as a whole at registering at 0.55 and most falling well below that. This is a portfolio with a high degree of internal diversification.

Historically, QPP shows that the portfolio has a beta of 0.44 and an R-squared of 0.58. It looks to be much less volatile that the stock market as a whole and is not just another closet index portfolio. QPP makes a “value at risk” calculation, and over a three month period it projects that this portfolio has a 1-in-20 chance of losing 5 percent of its value – a remarkably small expected loss for an all-equity set of holdings. This suggests that we are banking on portfolio effects to come through for us during bad times.

This last projection became of singular importance when, no sooner was our book released, than world stock markets entered a major correction. For the three months from 10/31/2007 through 1/31/2008, the S&P 500 (NYSEARCA:SPY) fell 10.6 percent, foreign stocks (NYSEARCA:EFA) declined 13.8 percent, and emerging market stocks (NYSEARCA:EEM) lost 17.1 percent of their value. How did our all-equity, all-individual-stock portfolio fare during this period? Since indexes, with their diversification into thousands of individual positions, are often regarded as lying at an extreme point of risk-adjusted returns, had our portfolio – with its much smaller set of holdings – really held up, or had centripetal forces torn it apart as all asset classed autocorrelated into a tailspin during those months?

Our portfolio of individual stocks was down 3.4 percent from 10/31/2007 through 1/31/2008. It performed much better than the indexes, and in fact had behaved in line with the expectations engendered by the QPP’s calculation of a possible loss in the 5 percent range. This is all the more remarkable when one considers that the portfolio had a market-like weighting toward financials, with Berkshire Hathaway, Wells Fargo, and about half of General Electric (GE Credit) falling in the worst-performing market sector over the period. By way of comparison, QPP showed a value at risk for the S&P 500 (SPY) for a 10 percent loss over a 90-day period – just slightly under what the S&P 500 actually experience during this correction.

In the short run, our experience with a didactic portfolio of individual stocks held up well during a sharp market correction. It remains to be seen how it will perform over longer periods, during bull markets, and how it will compare with simple market indexes.