Category Archives: Market Timing

Is My Portfolio at the Right Risk Level?

This is the fifth installment in our series on how individual investors can assess their financial health.

RiskAt every stage of investing, you should periodically ask yourself how much risk you can realistically tolerate. The primary way to measure the risk level of your portfolio is to look at its allocation of stocks vs. bonds.  Although some stock and bond ETFs  are riskier than others, your first decision has to be how much of your investments to put in stocks and how much in bonds.

One standard rule of thumb that’s a good place to start is the “age in bonds” axiom. According to this guideline, you invest a percentage of assets equal to your age in a broad bond index, and the balance of your portfolio in a diversified stock portfolio.  The idea here is that your portfolio should become more conservative as you get older. This makes sense for two reasons:

  1. You tend to get wealthier as you age, so any given percentage loss from your portfolio represents an increasingly larger dollar value.
  2. You are gradually converting your human capital (your ability to work and earn money) into financial capital (investments) as you age. And as you get older, your financial assets represent a larger and larger fraction of your lifetime wealth potential.

For these reasons, it makes sense  to manage this pool of assets more conservatively as time goes by.

Beyond “Age in Bonds” – Choosing Your Allocation of Stocks and Bonds

The past decade provides a powerful example of the tradeoffs between risk and return.  The table below shows the year-by-year returns for portfolios comprising different mixes of an S&P 500 ETF (IVV) and a broad bond ETF (AGG).  The returns include the expense ratios of the ETFs, but no adjustment is made for brokerage fees.

2004-2013 Allocation Performance

Source: Author’s calculations and Morningstar

Over the 10-year period from 2004 through 2013, a portfolio that is entirely allocated to the S&P 500 ETF has an average annual return of 9.2%.  In its worst year over this period, 2008, this portfolio lost almost 37% of its value.  As the percentage of the portfolio allocated to stocks declines, the average return goes down. But the worst 12-month loss also becomes markedly less severe.

We cannot say, with any certainty, that these statistics for the past ten years are representative of what we can expect in the future, but they do provide a reasonable basis for thinking about how much risk might be appropriate.

Ask yourself: If these figures are what you could expect, what allocation of stocks vs. bonds would you choose?  Would you be willing to lose 37% in a really bad year to make an average of 9.2% per year?  Or would you prefer to sacrifice 1.5% per year to reduce the potential worst-case loss by one third?  If so, the 70% stock / 30% bond portfolio provides this tradeoff.

Planning around Improbable Events

One might object that 2008 was an extreme case, and that such a bad year is unlikely to recur with any meaningful probability.  One way to correct for this potential bias towards extreme events is to assume that returns from stocks and bonds follow a bell curve distribution, a common way to estimate investment risk.  Using the data over the last ten years to estimate the properties of the bell curve (also known as the “normal” or Gaussian distribution), I have estimated the probabilities of various levels of loss over a 12-month period.

9-30-2014b

Estimated 12-month loss percentiles for a ‘normal’ distribution (Source: author’s calculations)

When you look at the figures for the 5th percentile loss, you can see what might be expected in the worst 5% of 12-month periods for each of the five portfolio types. For example, the 100% stock portfolio has a 1-in-20 chance of returning -21% or worse over the next twelve months. Note that a loss of 35% for stocks, similar to 2008, is estimated to have a probability of 1-in-100.

It’s important to point out that the ability to calculate the probability of very rare events is very poor.  Perhaps 2008 really was a 1-in-100 probability event, but we don’t know that with any certainty.  The most catastrophic events (what Nassim Taleb has famously dubbed “Black Swans”) are so severe and outside our normal range of experience that they tend to catch us totally off guard.

Moshe Milevsky, a well-known retirement planning expert, suggests that rather than thinking in terms of probabilities, it’s sensible to set your portfolio’s risk to a level that ensures that the worst case outcomes are survivable. Based on that, it’s prudent to choose a portfolio risk level that won’t ruin you if there’s another year like 2008. If you can survive a 12-month loss of 23% (the average of the worst loss for this allocation over the past ten years and the estimated worst-case 1st percentile return), for example, you can afford to hold a 70% equity portfolio.

Final Thoughts

If your investments in stocks don’t approximate the S&P 500, the stock portion of your portfolio may be considerably riskier than the table above implies.  Allocations to emerging markets, small companies, and technology stocks can be very volatile. The examples shown here provide a starting point in determining risk.  Combining a wider range of asset classes can provide important diversification benefits beyond their individual risk levels, but this topic is beyond my scope here.

The past ten years have provided examples of very high returns and very low returns from stocks. This period gives us a useful basis for testing our tolerance for volatility.  Many readers, I imagine, will find that their risk tolerance—self-diagnosed from looking at the tables above—corresponds reasonably well to the “age in bonds” rule. If your choice of risk levels is too far from these levels, a closer look is needed—and perhaps a talk with an investment advisor.

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Low Beta Market Sectors

With U.S. equity markets near their record highs and a bull market run that is starting its sixth year, the potential for a correction is a growing concern.  In addition, U.S. equity prices look fairly high when viewed in terms of the PE10 ratio.  Another factor that concerns some market watchers is that volatility (as measured by VIX) is at very low levels, reminiscent of 2007.  This type of complacency has historically been followed by increasing volatility, as levels return to their historical average, accompanied by a sell-off in higher-risk assets as investors adjust their portfolios to mitigate the effects of higher volatility.

Investors seeking to remain invested in equities at a target level but who want to reduce their exposure to market swings and to mitigate the impact of a rise in market volatility have historically been well-served by increasing their allocations to low-beta market sectors.  In this article, I will review the defensive value of low-beta allocations as well as examining the consistency of beta over time.

Beta measures the degree to which a security or a portfolio responds to a move in a benchmark index such as the S&P500.  A portfolio with beta equal to 80% (also written as 0.8) tends to go up 0.8% when the market rises 1.0% and vice versa.  Beta may be thought of as showing whether a security amplifies the moves in the benchmark (beta greater than 100%) or damps the moves in the benchmark (beta less than 100%).

How Beta Varies by Sector

The SPDR Select Sector ETFs provide a convenient way to break out the sectors of the U.S. equity markets by dividing the S&P500 into nine sectors.  These sectors illustrate how much beta varies.

Low Beta Market Sectors - 1

Betas and 10-year average annual returns for major sectors and indexes

The S&P500 has a beta of 100%, by definition.  Some readers may be surprised that emerging market stocks have beta of almost 140%, which means that emerging market equities tend to go up (down) 1.4% for every 1% gain (drop) in the S&P500.  Even before the market crash of 2008, emerging market stocks were high beta—this is not a new phenomenon.

There are three U.S. equity sectors with betas well below 100%: consumer staples (XLP), healthcare (XLV), and utilities (XLU).  It is often believed that low-beta equities have very low average returns.  In fact, a well-known but now widely-discounted model of equity returns (the Capital Asset Pricing Model, CAPM) assumes that beta of an equity or asset class corresponds directly to expected return.  High-beta asset classes have high expected return and vice versa.  Low-beta equities have historically substantially out-performed what would be expected on the basis of CAPM, however, and the past ten years is no exception.  These three sectors have all out-performed the S&P500 over the past ten years.  The return numbers shown here are the arithmetic averages, including reinvested dividends.

Low Beta Asset Classes in 2007-2008

The first question that is worth asking about beta is the degree to which beta corresponds to losses in really bad market conditions.  In the table below, I have tabulated beta calculated using three years of data through 2007 for each of the funds above, as well as the returns for each of these in 2008.

Low Beta Market Sectors - 2

Beta calculated through 2007 vs. 2008 returns

The three sectors with the lowest betas going into 2008 (consumer staples, healthcare, and utilities) had an average return of -22.3% in 2008, as compared to -36.8% for the S&P500.  An equity tilt towards these lower beta sectors could have reduced losses in that year.

Consistency of Beta through Time

The astute reader may notice that the betas calculated using ten years of data through May of 2014 (shown in the first table) are, in some cases, quite different from the betas calculated using three years of data through December of 2007 (shown in the second table).  Beta varies through time.  The betas calculated using three years of data through May 2014 provide an interesting contrast to the three-year betas through the end of 2007.

Low Beta Market Sectors - 3

Comparing betas for two 3-year periods

We are looking at two distinct 3-year periods, separated by almost six and a half years and, in general, low-beta sectors at the end of 2007 remain low-beta today and high-beta sectors back then are still high-beta.  The two most notable exceptions are international equities (EFA) and the technology sectors (XLK).  These changes notwithstanding, the three sectors with the lower betas in 2007 also have the lowest betas in 2014.

There are a number of factors that will determine whether any sector will weather a broad market decline better than others.  Beta is one important factor, but there are others.  In 2008, the financial sector suffered disproportionately large losses—well beyond what would have been expected on the basis of beta alone.  The underlying drivers of the 2008 market crash were most severe in the financial sector.  Small-cap stocks, by contrast, fell considerably less than the beta value of this sector would have suggested.

Low-Beta and Asset Allocation

Low-beta asset classes have historically provided some protection from market declines and increasing volatility.  There are a range of other considerations that potential investors should consider, however when creating a portfolio.  The selection of individual asset classes should be made with consideration of the characteristics of the total portfolio, including desired risk level, interest rate exposure, and income generation.  The target for total portfolio beta is primarily determined by an investor’s total risk tolerance.  A target beta level can be achieved both by choosing how to allocate the equity portion of a portfolio among sectors and by varying the balance between equity (stocks) and fixed income (bonds) investments.  Fixed income asset classes tend to have very low—even negative—values of beta.  In my next blog entry, I will explore these two approaches to managing beta at the portfolio level.

History suggests that low-beta sectors can provide some protection from market downturns.  The length of the current equity rally, and the substantial increases in equity valuations in recent years, are motivating some investors to consider their best defensive alternatives to protect against the inevitable reversal.  The question for investors to ask themselves is whether they are best-served by reducing portfolio beta by reducing their exposure to equities, by shifting some portion of assets from high-beta to low-beta sector, or both.

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Is Twitter the Canary in the Coal Mine?

Investors are shrugging off the suggestion that stocks are over-valued or that the technology innovators are a one-way path to riches if the year-to-date performance of Tesla Motors (TSLA) and Facebook (FB) are any indication. Twitter’s stock (TWTR), which soared from a $45 closing price on its first day of trading (November 7, 2013) to a high of $73.30 on December 26, has fallen 32% since the start of 2014. Twitter is now trading at slightly below $45. Given the excitement surrounding the Twitter IPO less than six months ago, what does this apparent reversal of (expected) fortunes suggest? It is certainly too soon to conclude that a business model that made sense at the IPO has proven to be faulty. Does Twitter’s dramatic decline signal a shift in investors’ willingness to bet heavily on a future earnings stream that is almost impossible to predict? Continue reading

Economic Inequality

Income inequality is increasingly acknowledged as a key economic issue for the world.  The topic is a major theme at Davos this year.  Economic inequality is also an increasingly common topic in U.S. politics.

A new study has found that economic mobility does not appear to have changed appreciably over the past thirty years, even as the wealth gap has grown enormously.   The authors analyzed the probability that a child born into the poorest 20% of households would move into the top 20% of households as an adult.  The numbers have not changed in three decades.

On the other hand, there is clearly a substantial accumulation of wealth at the top of the socioeconomic scale.  The richest 1% of Americans now own 25% of all of the wealth in the U.S.  The share of national income accruing to the richest 1% has doubled since 1980.  In contrast, median household income has shown no gains, adjusted for inflation, since the late 1980’s and has dropped substantially from its previous peak in the late 1990’s.

Why is this happening?

Continue reading

Perpetually Out of Step

There is increasing evidence of big flows of money into equities and leaving bonds.  This is being seen at all levels in the market, including among institutional investors such as pension plans.  The Wall Street Journal just published an article discussing this shift called Are Mom and Pop Heading for Wall Street?   Mutual fund flows suggest that investors are finally returning to equities, after selling in droves over the past several years.  This article summarizes the issue:

From April 2009 through now, mutual-fund investors sold a quarter trillion dollars in stock funds, according to recent data from the Investment Company Institute.

Ironically, that selloff coincided with a period of stellar performance in stocks—when the Dow Jones Industrial Average jumped more than 60%. Continue reading

Top Ten Ways to Deal with Behavioral Biases

Guest post by Contributing Editor, Robert P. Seawright, Chief Investment and Information Officer for Madison Avenue Securities.

Pretty much since the day I wrote it, my Investors’ 10 Most Common Behavioral Biases has been the most popular post on this blog.  It still gets a surprising number of hits all these months later.  Due to the pioneering work of Daniel Kahneman and others, nearly everyone in the financial world acknowledges the reality of cognitive and behavioral biases and their impact on people, the markets and life in general. It’s a very popular subject. Continue reading

Game Theory, Behavioral Finance, and Investing: Part 1 of 5

Watching the market this year has been like observing an exercise in game theory and behavioral finance, and the two fields are closely related.  Game theory is the study of how a rational person makes decisions in uncertain situations.  As the name suggests, game theory was developed with the intent of developing optimal strategies in games in which chance or the decisions of an opponent play a role in your outcome.  Game theory focuses on how rational players can make the best decisions to maximize their satisfaction.  Behavioral finance adds the nuance that, in real life, people do not necessarily have all available information and, even if they do, they often make decisions that are inconsistent with those made by a perfectly-rational and fully-informed decision maker. Continue reading