You can often hear an investment professional say, “That’s a high-Beta stock.” Less frequently, you may see the claim, normally in writing, that someone “is searching for alpha.” Here’s what they’re talking about:
How It Started
After World War II, as the first commercial mainframe computers were developed, an economist named Harry Markowitz proposed using this new computational power to calculate the risk/reward characteristics of portfolios of stocks. With this information in hand, you would be able to determine the best portfolio for you to own, i.e., the highest-return portfolio for a given level of risk.
Markowitz used a statistical concept, variance–a measure of how far away from a trend line a stock’s price might temporarily drift–as his definition of the riskiness of a stock (this is a terrible definition of risk, in my view, although it’s, even today, the academic standard). From this definition, it follows mathematically that the riskiness of a portfolio is the riskiness of the individual stocks in it plus a factor (covariance) of their tendency to move together in herds.
The general idea was that you could quantify, and therefore compare, the riskiness of different portfolios that offered the same return, as well as the returns of portfolios that had the same risk. So you can “optimize,” that is, eliminate unnecessary risk by picking the best portfolio–highest return for a given level of risk, or lowest risk for a given level of return.
There’s a practical problem, though. If the universe has only two or three stocks in it, calculating this information is straightforward. If the universe is the S&P 500, however, figuring out all the interrelationships among all the stocks becomes a real pain in the neck.
(There’s a much bigger problem, though. The virtues of short-term price volatility as a measure of risk is that the data are easily available for many stocks and that variance is part of an established mathematical framework. So it has been widely adopted by academics and consultants. Unfortunately, it’s otherwise not very informative, I think. It’s like saying that the risk in an airplane flight should be measured by the amount of air turbulence en route. By this measure, the plane that recently took a smooth ride into the Hudson River would be classified as a safe flight.) Continue reading →