Making Sense of Past Volatility

Investing Tip of the Month

Although conducting a fundamental analysis of a fund–checking its investment style and concentration in sectors and individual stocks–is one of the best ways to assess an investment’s potential for volatility, past volatility is also good indicator of future risk. If a fund has seen lots of ups and downs in the past, it’s apt to continue to have uneven returns.

Here are some of the key measures of risk and volatility available to Morningstar Investment Research Center users. You can find these on the Ratings & Risk tab of each Fund Report.

Standard Deviation
Standard deviation is probably the most commonly used gauge of an investment’s past volatility, and it enables quick comparisons among funds. Morningstar analysts like standard deviation because it tells investors just how much a fund’s returns have fluctuated during a particular time period. Morningstar calculates standard deviations every month, based on a fund’s monthly returns for the preceding three years.

Standard deviation represents the degree to which a fund’s returns have varied from its three-year average annual return, known as the mean. By definition, a fund’s returns have historically fallen within one standard deviation of its mean 68% of the time and within two standard deviations 95% of the time.

Let’s translate. Say a fund has a standard deviation of 4%, and an average annual return of 10%. Most of the time (or, more precisely, 68% of the time), we can expect the fund’s future returns to range between 6% and 14%–or its 10% average plus or minus its standard deviation of 4%. Almost all of the time (95% of the time), its returns will fall between 2% and 18%, or within two standard deviations of its mean.

If you have a short to intermediate time horizon (meaning you’ll need to sell your investment sooner rather than later), look for funds with lower standard deviations, even though returns might be lower. Over short time horizons, funds with modest standard deviations are less volatile. This means that, during market downturns, they tend to lose less money than those with high standard deviations.

It’s important to remember some caveats. Standard deviation doesn’t tell you much when you look at it in isolation. Knowing that a fund has a standard deviation of 25% for the past three years is meaningless until you start making comparisons. Just like returns, a fund’s standard deviation requires context to be useful. If you’re looking at a fund with a standard deviation of 15% for the same period, you know that the fund with the standard deviation of 25% is substantially more volatile.

An index can be a useful benchmark for evaluating a fund’s volatility as well as its returns. Say you’re considering a fund in Morningstar’s large-cap blend category. The S&P 500 index is a good comparative benchmark for that group, because it emphasizes large companies with a variety of investment styles–growth, value, and everything in between. Through May 2010, the S&P 500 index’s three-year standard deviation was 20.57%. You can tell that the fund with a standard deviation of 25% has subjected investors to more volatile price swings than the index. Unless it also has much higher returns to compensate for the stress of owning it, buying that fund would not represent an attractive tradeoff between risk and return.

You can also compare a fund’s standard deviation with that of other funds that invest in the same way, such as those in the same Morningstar category. If you’re comparing two large-blend funds, the one with the standard deviation of 25% is prone to larger swings in value than the one with a standard deviation of 15%. Unless the more volatile fund has substantially better long-term returns than the less volatile fund, its risk-reward profile probably doesn’t justify buying it.

Beta, unlike standard deviation, is a relative risk measurement–it depicts a fund’s volatility against a benchmark. Morningstar calculates betas for stock funds using the S&P 500 index as the benchmark. We also calculate betas using what we call a fund’s best-fit index, which is the benchmark whose performance most resembles that of the fund. For bond funds, we use the Barclays Aggregate U.S. Bond Index and best-fit indexes.

Beta is fairly easy to interpret. The higher a fund’s beta, the more volatile it has been relative to its benchmark. A beta that is greater than 1.0 means that the fund is more volatile than the benchmark index. A beta of less than 1.0 means that the fund has been less volatile than the index.

In theory, if the market goes up 10%, a fund with a beta of 1.0 should go up 10%; if the market drops 10%, that fund should drop by an equal amount. A fund with a beta of 1.1 would be expected to gain 11% if the market rises by 10%, while a 10% drop in the market should result in an 11% drop by the fund.

Keep in mind the following caveats: The biggest drawback for beta is that it’s really only useful when calculated against a relevant benchmark. If a fund is being compared with an inappropriate benchmark, the beta is meaningless.

There’s another statistic that’s often overlooked in this discussion of volatility: R-squared. The lower the R-squared, the less reliable beta is as a measure of the fund’s Ratings & Risk page. The closer to 100 the R-squared is, the more meaningful the beta is. Gold funds, for example, have an average R-squared of just 3 with the S&P 500, indicating that their betas relative to the S&P 500 are pretty useless as risk measures. Unless a fund’s R-squared against the index is 75 or higher, disregard the beta.

Morningstar’s Risk Rating
Standard deviation is useful because it tells you about the fund’s past performance swings, and big swings usually beget more big swings. But standard deviation doesn’t tell you whether the fund’s swings were gains or losses, and that’s an important distinction for most investors. Theoretically, a fund with extremely high returns year in and year out could have a standard deviation just as high as one that had posted fairly steep losses. That’s why investors should look at the whole picture, not simply measures of returns and standard deviation.

Just as we want to know how successful a fund manager has been at making money for shareholders, we want to know how successful he or she has been at protecting them from losses. That’s why the Morningstar Risk Rating not only looks at all variations in a fund’s returns–just like standard deviation–but also emphasizes a fund’s losses relative to its category peers. The formulas driving the risk rating are complicated, but the underlying idea is straightforward: As investors, we don’t like losing money!

Morningstar’s risk rating looks at funds’ performance over a variety of time periods. We don’t rate funds that are younger than three years old, because shorter periods just don’t give an adequate picture of a fund’s performance. If a fund is three years old, its risk rating will be based entirely on that three-year period. For a five-year-old fund, 60% of its risk rating is based on the past five years, and 40% on the past three years. A 10-year-old fund’s 10-year record will count for 50% of its risk rating, while the five- and three-year periods count for another 30% and 20%. Morningstar looks at this combination of periods because we think long-term investing is important, but we also want to be sure that funds don’t earn good ratings just on the strength of success years ago. We assign funds new risk scores every month.

Because we measure a fund’s risk relative to its category, it’s easy to compare funds that invest in the same way. The least risky 10% of funds in a category earn the low risk designation, the next safest 22.5% are considered to have below-average risk, and the middle 35% are deemed to have average risk. The next 22.5% are deemed to have above-average risk, while the final 10% are considered high risk. If you’re contemplating a large-cap value fund with a high risk rating, you know that it has exhibited more volatility (including real losses) than 90% of large-value offerings.

Bear-Market Rankings
Morningstar also calculates bear-market rankings, which compare how funds have held up during market downturns during the past five years. This measure, also displayed on the Ratings & Risk tab, is unlike the others presented thus far, because it examines performance only during the times in which investors may face the largest potential for losses–during downturns or corrections in the market.

A bear market is officially defined as a sustained market correction, but for the purpose of these rankings, Morningstar identifies “bear market months” that have occurred in the past five years. For stock funds, we consider any month in which the S&P 500 Index lost more than 3% to be a bear-market month. For bond funds, we count any month in which the Barclays Aggregate U.S. Bond Index lost more than 1%.

To generate our current bear-market rankings, we simply total each fund’s performance during bear-market months during the past five years, and separate them into 10 groups from those with the most aggregate losses to those with the least. Funds with ranks of 1 or 2 withstood bear-market periods better than those with ranks of 9 or 10. If a stock fund receives a rank of 10, its performance during the bear-market months was among the worst 10% of all stock funds.

There are two caveats to keep in mind when using bear-market rankings. First, these measures let you know how a fund performed only during certain periods. Although it’s helpful to know how your fund performed during these market downturns, the fund could certainly lose money–lots of it–during a market upturn, too.

The second drawback to bear-market rankings is that not all bear markets are the same. The next market correction may look quite a bit different from the most recent ones. Hence, funds that held up well in one bear market may not do so well in the next. Conversely, funds that were pummeled the last time around might shine in the next bear market.

To find out about your mutual funds use Morningstar Investment Research Center.

A version of this article appeared on on June 14, 2010.


One thought on “Making Sense of Past Volatility

Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Google+ photo

You are commenting using your Google+ account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )


Connecting to %s