Traditionally, stock investors have looked at two different but related forms of risk: systematic or market risk, and unsystematic or company-specific risk. Systematic risk is the risk of investing in the stock market in the first place, as opposed to putting your money in a return-guaranteed investment such as a money market fund or certificate of deposit. Also referred to as "market risk," systematic risk is the quintessential "ya got to pay to play" equation: in order to get the sort of returns the stock market has delivered over time (anywhere from 10% to 15% since the Crash of 1929 to present), you must accept the risk that equities will underperform from time to time. "Underperformance" refers to anything from a stock market return that is less than that attainable through a return-guaranteed investment to a complete loss of invested capital (in the case of a stock that becomes worthless). For those who are uncomfortable with the thought of losses, even temporary ones, systematic risk may be risk too much.
But for those who are willing to accept the risks of investing in the stock market in return for the promise of the returns the market has provided investors on average, over time, systematic or market risk is something to live with. However, living with systematic risk does not necessarily mean riding out every downturn, as the High Church of Buy and Hold has convinced many investors. What it does mean is that investors need a ready tool for determining just how much systematic risk they are willing to endure.
For those with longer time horizons, for example, market risk may play less of a role than for those with shorter time horizons (though we will revisit this truism soon). For these longer-term investors, taking on additional systematic risk may be less of a concern and may even be advantageous in some circumstances. Those with shorter time horizons may look more nervously at systematic risk, knowing that their brief time in the market makes them more vulnerable to a sudden, one-time negative shift in stock values.
This is where beta, a statistical measurement tool used by financial professionals, can come in handy. By using beta, stock investors can get a good sense of how volatile their stock or the stocks in their portfolio have been in the past. While beta has not proved to be especially effective in anticipating the future volatility of stocks or portfolios of stocks, beta is very helpful in letting stock purchasers know just what kind of stock they are getting. This information can be vital in tailoring an investment strategy or portfolio of equities to fit the specific goals of the individual investor.
BETA AND BENCHMARKS
The term beta comes from the world of statistics. Beta describes the slope of any regression line, and the word is used by financial professionals to refer specifically to the measure of a stock's volatility relative to the Standard & Poor's 500. Another way of thinking about volatility in this context is variance in value over time. Beta measures the degree to which a stock's value over time changes compared to the change in value over time of the Standard & Poor's 500.
For example, if a stock has a beta of 1.3 and the S&P 500 moves up 10%, the stock would be expected to move up by a factor of 1.3 (1.3 x 10%), or 13%. Conversely, if the S&P 500 moved down 15%, then a stock with a beta of 1.3 would be expected to move down by a little more than 19%.
However, William Johnson, an analyst with 21st Century Investor of Boca Raton, FL, cautions that beta "won't tell you what your returns are for a given change in the market. Studies have shown that beta is really unstable for individual stocks and not a good estimator for future volatility."
All the same, he says, beta is "nice to look at as a way to see what you're getting into. If you're a risk-averse investor, you probably don't want to be in the high beta stocks."
Why is beta so problematic for individual stocks? It seems to be largely because there are so many factors -- from new competition in a particular market to an energy crisis -- that can weigh heavily and suddenly on an individual stock, making that particular issue far more volatile than it might otherwise be. For this reason, Johnson sees more utility in applying beta to portfolios of stocks, such as in a mutual fund.
"If you know that your portfolio has a beta of 1.1, and you add a stock that has a beta of 1.5, then you know that you have increased the risk of your portfolio," he explains. "How much you increase it would depend on how big that trade was in relation to the entire portfolio."
But even here investors using beta to examine their portfolio need to be cautious. As Mark Murphy, president of the Northeast Private Client Group, an affiliate of Rothstein, Kass and Co. of Roseland, NJ, points out, "If your portfolio is not 100% weighted in stocks, the beta may not be very meaningful. Sometimes it can be like comparing apples and oranges," he suggests, if a portfolio also contains bonds, real estate, money markets, or other nonstock investments.
TIME AND BETA
Legendary investor Warren Buffett recently caused a stir among stock investors when he suggested at a recent shareholders' meeting that "if someone starts talking to you about beta, zip up your pocketbook." His point was there are only two types of risk that investors needed to worry about: market risk and opportunity risk. Market risk, as we've already discussed, is the risk of investing in financial instruments with some speculative character (that is, nonguaranteed returns) as opposed to investing in guaranteed return instruments such as CDs. Opportunity risk, on the other hand, is the risk that one investment will be outperformed by another investment over a certain time frame (for example, corporate bonds outperforming corporate stocks). If market risk can be summed up with "ya gotta pay to play," opportunity risk might be referred to as the "some grass is always greener" syndrome.
Implicit in Buffett's remarks, however, is the time frame for which he is known -- forever. And it is when longer time frames are considered that volatility measures including beta take the severest beatings from their critics. The theory, say the critics of beta, is that above average returns will, over time, eventually compensate for and exceed periods of below average returns in the stock market. This approach, called "time diversification," has history on its side. As Johnson notes of a related measure of volatility, standard deviation, "it can be shown that the standard deviation of your returns will diminish over time. In other words, it does become less and less likely that you will incur a loss over time."
But there's more to it than the simple assurances of standard deviation. "What [investors] fail to realize is that the decreasing probability of a loss is being offset by increasing the amount you have at risk over time," explains Johnson. So while the chance of losing money diminishes even as an investment grows, the growing size of the investment means that when the inevitable losses do occur, they will most likely represent a larger share of money lost, even if the losses represent a smaller percentage of the total capital at risk. To put it succinctly, you're getting less downside risk over time, but more money is at risk. This is one reason why many people adjust their portfolios, particularly after large gains, to move more assets toward safer, less volatile instruments both as the investment grows and as specific investment goals draw near.
On this, Murphy concurs. "Time will correct volatility, and if you have enough time in the market, I think volatility is generally not an issue as long as you've purchased very fundamentally strong companies for your portfolio."
BETA'S BROTHER, R-SQUARED
Another often-used measurement that works well with beta is R-squared. R-squared, like beta, comes from the world of statistics and is often referred to as a "goodness of fit" test. R-squared is different from beta in that R -squared is not a test for volatility; instead, it allows an investor to learn how much of a stock's movement is attributable to the overall movement of the stock market, again using the S&P 500. R-squared uses a measurement between zero and 1, with a score of 1 being a perfect fit. A perfect fit means that 100% of the stock's movement is coming from the movement of the S&P 500.
"So if a stock has an R-squared of 0.9," says Johnson, "that's saying 90% of the variation in that stock's performance is explained, or accounted for, by the S&P 500."
Like beta, R-squared can be an especially effective tool for analyzing the performance of a portfolio of stocks, as in a mutual fund. Adds Johnson, "If you had an S&P 500 mutual fund and you ran a regression, you would expect R-squared to be 1." If the R-squared was less than 1, that would suggest there was stock variance at work in the fund used in the example that was not explained by the variance of the greater pool of stocks in the S&P 500.
Says Johnson: "There are two things that any statistical test tries to do. One is to tell you what your guess is, what the estimated mean is, and the second thing they try to do is tell you how good that guess is %C9 beta is telling you what your best guess is as to how that stock is going to perform for a given change in the market. R-squared is telling you how well that model explains the changes in that stock."
In other words, "One is telling you how the stock will respond. One is telling you how good that estimate is." There are other measures that can be profitably used with beta as well. Murphy considers standard deviation to be among the more common beta companions. (Standard deviation measures the volatility of a portfolio in relation to its own average.) "The Sharpe ratios are sometimes important," Murphy adds, "which use standard deviation as well as risk-adjusted returns to determine how risky a portfolio is."
Another measure Murphy is fond of is called alpha, a calculation that is particularly effective in measuring risk-adjusted returns. "Why I like alpha as another snapshot is that it also can really measure whether the stock gave you a return premium or a risk premium when all is said and done. It can say, 'Hey, we delivered a 20% premium over the S&P 500 and we took 15% less risk to deliver this 20% premium.'"
IT'S ALL ABOUT VOLATILITY
All of these statistical measurements respond to what is the most immediate, if not the most urgent, form of volatility for stock investors: price volatility.
In essence, volatility measures the upswings and downswings in a series, particularly differences from the mean. As such there is both upside volatility (prices straying from the mean to advance higher than usual) and downside volatility (prices dipping below the mean to decline lower than usual). Rather than thinking of "volatility" as a negative force that strips away hard-won gains, or as a benevolent force that jerks prices upward toward new highs during bull markets, a more nuanced view of volatility is helpful for all investors.
"Investors need to consider the source of the volatility before making judgments as to whether it is good or bad," says Johnson. For example, he continues, consider stock price volatility versus earnings volatility. Price volatility signifies risk and earnings volatility does not. Volatility, he goes on to explain, is "a source of uncertainty and that's really what gives a stock its expected return above the risk-free rate. So whether volatility is good or bad depends on what your goals are."
As such, Johnson -- like others -- considers measurements such as beta to be helpful, but not definitive. In fact, he points to a study done in the 1990s by University of Chicago academics that suggested there was no historical relationship between the returns of a set group of stocks and their betas. "You don't get a whole lot of money managers who follow beta with any conviction," Johnson says. "It's just another tool."
Says Murphy, "I do think it's somewhat overused by stockbrokers and financial planning people. %C9 It's something that's more mystical and it's something that people feel like they can talk about without having to really explain." He uses the analogy of six blind men studying different parts of the same elephant: "If you're (just) using beta, all you're going to get is one little slice of that elephant."
One little slice, but perhaps a valuable one when volatility is present and investors are seeking a way to make some sense of their stocks' tendencies to out- or underperform the S&P. As Johnson adds, there is a vast universe of volatility measures, from adjusted beta and semi-variance to standard unanticipated errors and coefficient of variation, but getting a hold of this type of very detailed financial information is difficult -- and costly -- for most investors. "For most people," he suggests, "standard deviation and beta are fine enough" measures of volatile stocks.
"I think you can be a relatively astute investor without being able to give a definition of what an R-squared is, or a beta or an alpha," concludes Murphy. "It's most critical to find somebody who you can trust and who you can work with and have them help you -- to explain those terms not so that you can become an expert in it, but so you can get to the point where you can feel comfortable where your money is."
David Penn may be reached at DPenn@Traders.com.
Why not the source: William Sharpe
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