Low-Beta Portfolio Strategies: Devising A Low Risk Game Plan For the Current Market

 |  Includes: AHBIF, BAC, BBT, BCE, C, ED, GIS, JNJ, PEG, SO, WYE
by: Geoff Considine

Many investors are concerned that future growth in the U.S. economy will be sufficiently lower than historical averages as to make returns from the broader domestic equity markets un-inspiring. Consider, for example, this recent article in The Economist about slowing rates in domestic economic growth.

A related theme that points towards slower growth in the economy is the burgeoning levels of consumer debt. Consumer spending has kept the economy moving, but the wealth effect from seemingly unstoppable real estate appreciation had to end—and appears to be doing so. These forces lead many investors to have substantial concerns about the future prospects for returns from broad equity indices. Just do a web search on the term "U.S. economic growth" and you will find a wide range of discussions—overwhelmingly suggesting that the consensus view is that the future growth prospects for the U.S. economy are not terribly rosy.

As investors get more concerned about the future of the U.S. economy and the baby boomers become increasingly risk averse, there is a lot more interest among investors in controlling portfolio exposure to swings in broad markets. A recession in the next few years could have a strongly negative impact on the ability of boomers to retire. A substantial decline in portfolio value at the start of retirement is a major detriment to the ability to fund retirement because retirees then draw a disproportionate percentage of income at the start of their retirements. Many investors seek to insulate themselves from the swings in equity markets by shifting disproportionately to bonds. The problem with this approach is that you may also sacrifice potential for future growth that you may need in order to sustain retirement income. A wholesale shift to bonds may be appropriate for some investors, but there are other alternatives.

Concern with the prospects for returns from the broader U.S. stock market does not mean that you can’t invest in equities or that you need to shift focus to be over-weight in foreign markets. There are ways to limit exposure to broad economic swings in equity markets. Hedge funds have a wide variety of strategies to minimize exposure to movements of the broader market indices and these are broadly known as market neutral strategies. In a similar vein, covered call writing funds have become quite popular as a vehicle to limit exposure to market swings. I am not, in general, a fan of covered-call funds---for some reasons as to why this is, see this article.

The boom in commodities in recent years has also been partly motivated by the fact that commodities typically have relatively low correlation to equities, thereby reducing portfolio correlation to the broader equity indices. For investors who want to minimize their exposure to swings in the broader U.S. market, there are some good strategies that do not involve hedge funds, covered-call funds, heavy commodity exposure, or ‘alternative investment strategies.’ It is quite simple, in fact, to build a portfolio of substantial company stocks with low P/E ratios and decent dividend yields that does not move with the broader market.

In developing an investment portfolio, an investor needs to manage two key statistics in order to control exposure to the broader market: portfolio Beta and portfolio R-squared. Beta measures the tendency of a portfolio to move up or down with a reference market—and when I discuss Beta it is always with respect to the S&P500. R-squared measures the degree to which Beta effects describe the entire movement of an investment or portfolio. If you want to limit your exposure to the broader U.S. market, you will need to build a portfolio with low Beta and low R-squared. With that goal in mind, however, you must also consider other criteria for your investments. To provide a sample portfolio with the objective of minimizing exposure to the U.S. market, I first screened Yahoo! Finance for stocks with several criteria:

1) P/E ratio no higher than 20
2) Market capitalization no lower than $10 Billion
3) Dividend yield no lower than 2%

This screen yielded a list of twenty eight stocks. I removed some obviously redundant stocks—those that were highly correlated to one another, despite having low Beta. Having high correlation between stocks—even those with low Beta—will hurt the total diversification potential of the portfolio. I then further limited the selection to stocks with at least ten years of data. Finally, I added a couple of bond funds to the mix: Vanguard’s intermediate term bond index fund (MUTF:VBIIX) and Vanguard’s high-yield corporate bond fund (MUTF:VWEHX). The final list of components for the model portfolio is shown below. Most of these are household names:


A few of these firms are less well known. BCE is a major Canadian telecom firm. BBT is a mid-Atlantic bank. This portfolio has some major weight in utilities, including Con Ed (NYSE:ED), Southern Company (NYSE:SO), and PSEG (NYSE:PEG). This is to be expected, given that utilities as a group tend not to track the economy as a whole. Demand for electricity is relatively inelastic, so Beta and R-squared tend to be low. The danger in having multiple companies in the same industry, of course, is that over-concentration can limit diversification benefits in a portfolio. This can be accounted for in portfolio design and analysis. The correlation coefficient (usually simply referred to as correlation) measures the degree to which stock returns move together. A lot of what I was looking for in developing this portfolio was low correlations between the returns of the various components. These low correlations (shown below in a correlation matrix) enable us to maximize offsetting risks and thereby improve return relative to total portfolio risk. The intersection of two ticker symbols on the matrix shows the correlation. The lower the correlations, the better.

[Note: For a basic introduction to correlations and examples of correlations between asset classes, see my recent article Targeting Low-Correlation Assets for a Portfolio.]

beta correlations

If you compare the correlation numbers in the table above to correlations between major equity indices—both foreign and domestic—in the linked article above, you will appreciate that many of these correlations between individual firms are extremely low.

Johnson and Johnson (NYSE:JNJ) is one of my favorite low-Beta / low-correlation stocks. General Mills (NYSE:GIS) is also a good choice for a low-Beta stock. In good times and in bad, people continue to buy basic essentials from these companies. People also seem to buy beer, regardless of the S&P500—see Anheuser-Busch (NYSE:BUD). BUD, JNJ, and GIS all have R-squared of between 21% and 26%. This means that only a quarter of the variability in returns from these stocks can be attributed to the moves in the S&P500. Bank of America (NYSE:BAC) exhibits a fairly low Beta (49%) and a low R-squared (10%). Some of the highest correlations we see in the matrix above are between utilities (SO, ED, PEG) and the bond funds (VBIIX and VWEHX). This is a well known phenomenon. Rounding out the list, we have Wyeth (WYE), a major pharmaceuticals firm. Wyeth has a Beta of 46%, one of the highest in the portfolio, but an R-squared of only 5%. WYE is the high risk/high return stock in the portfolio, with high volatility and high expected returns.

To be able to analyze and use the Betas, R-squared values, and correlations between individual portfolio components to full advantage, you need a tool that can capture all of these effects. Quantext Portfolio Planner [QPP] is a Monte Carlo portfolio management tool that analyzes historical data and generates outlooks for portfolio risk and return, accounting for diversification effects between individual stocks and funds that include both systematic correlation (i.e. via Beta) and non-systematic effects (non-market correlations). QPP combines both recent history and long-term statistics on risk-return balances across asset classes to generate an outlook for future performance. I used QPP to develop a portfolio using these components with the following goals:

1) Low R-squared
2) Low Beta
3) High dividend yield
4) High projected future return relative to risk
5) Total risk well below the S&P500

Using QPP to develop the portfolio, I came up with the percentages in each holding shown in the "Sample Model Portfolio" table shown earlier. Please be sure to understand that this portfolio is not a suggestion for any specific person. Each person needs to find the total risk/return balance that maximizes their chances of funding desired future income. The total risk/return balance that this portfolio ends up with is a matter of my arbitrary choice for this sample analysis.


When we examine the historical and projected future performance of the model portfolio using the trailing three years of data (see above), we see the key features that the portfolio was designed for. Beta for this portfolio is only 29%, very low for a portfolio with only 19% in bonds. The dividend yield over the past three years has averaged 4% per year. Perhaps most important, R-squared (notated as R^2 in the table above) is only 17.7%. This means that only 17.7% of the variability in this portfolio tracks with the S&P500. Over the past three years, this portfolio has averaged 10.2% annual return, with a standard deviation (a measure of total risk) of 5.1%. The return is higher and the standard deviation is lower than the S&P500 over this period (see above). The projected future performance of this portfolio is an average annual return of 12.7% per year, with a standard deviation of 11.1% per year. This projected future value uses QPP’s standard assumptions that the volatility of the S&P500 will increase back to historical levels. This assumption is well validated [pdf].

QPP’s baseline assumption for the future average annual return on the S&P500 is 8.3% per year.

An important feature of this portfolio from a statistical standpoint is that even though this portfolio has concentration in utilities, the effective final diversification is high—as measured by QPP’s diversification metric [DM]. This portfolio yields a DM value of 58%. The general features of this portfolio are quite stable—including the low Beta and low R-squared. When I analyzed the performance of this portfolio for a far less happy three year period (11/1/2000-10/31/2003), this portfolio also generated decent returns:

historical performance

During this period, which spanned much of the most recent bear market, the S&P500 returned an average of -6% per year (see above). The model portfolio allocation returned an average of 8.5% per year, with a standard deviation of 8.9% per year. During this period, the yield was 4.2% and the portfolio R-squared and Beta remained low. This kind of stress test is especially important because there is some evidence that correlations between stocks can increase substantially during times of great market stress—such as a bear market—and this was obviously not a significant factor for our model portfolio. In fact, the performance of this portfolio has been remarkably stable if we compare the performance over the last several years to the performance during the bear market three year period.

QPP’s projected performance for this portfolio, with an average of 12.7% per year and a standard deviation in annual return of 11.1%, suggests that this portfolio has good potential to perform well on an absolute and risk adjusted basis. The performance of this portfolio over the last ten years (average return = 13.5% per year and standard deviation = 10%) is actually quite close to the projected future performance levels. This is, in a statistical sense, an all-weather portfolio. This portfolio has generated attractive average returns, with moderate volatility, over the last three years (a market recovery) and during the recent bear market. The dividend yield has been stable—at around 4%--and this portfolio consists of companies with current P/E ratios less than twenty. This portfolio does not track with the S&P500.

We started this discussion with some gloomy projections about U.S. economic growth. I am not in the business of economic forecasting of growth, but I grasp the potential negative factors that are working against high growth in the overall market. Many investors are looking to alternative investment strategies and heavier allocations to commodities to protect their portfolios from a decline in the overall stock market. My purpose in writing this article was simply to demonstrate that you can build a portfolio that does not track the U.S. market using a small number of good solid stocks if you know enough and have the right tools to properly exploit diversification effects.

The sample portfolio presented here is not held out as an ideal. In fact, I am sure that you can do better. A couple of QPP users already have. The portfolio presented here shows a specific strategy that most investors seem to be unaware of. If I were approaching retirement, or simply wanted to insulate myself from the potential for a broad decline in domestic equity markets, I would take a serious look at this type of strategy.

Disclosure: The author owns shares of JNJ.