Using beta and alpha to understand ETF performance.
While ETF performance descriptions might bring to mind Mark Twain’s phrase “lies, damn lies, and statistics,” risk metrics are indispensible when evaluating a fund.
When analyzing ETFs, we often evaluate pairs of data. For example, we compare a fund’s market price against its net asset value (NAV). Or we might look at a fund’s NAV versus the index it tracks. I described these fundamental relationships in a previous piece looking at what I consider to be crucial terminology.
Basic performance statistics that compare data sets aren’t complicated, but the terms themselves often carry baggage that obscures their meaning.
For example, alpha is often associated with risk takers, and beta with the follow-the-herd crowd. I don’t buy these characterizations. Moreover, I think the mystique around these terms just gets in the way.
Beta and alpha come from regressions. Here’s the basic idea:
Take two sets of numbers, such as daily returns. Plot all the returns on a simple grid, with one set on the horizontal axis and the other on the vertical axis. The regression is the best estimate of a straight line that comes closest to fitting these points. Beta is simply the slope of this line and alpha is the intercept.
Beta is typically used to compare a fund to a broad index. Let’s say you’re looking at an equal-weight fund like the Rydex S&P Equal Weight ETF (NYSEArca: RSP). You want to know how the fund stacks up against a comparable cap-weighted fund like the SPDR S&P 500 ETF (NYSEArca: SPY).
Running the regression on 60 months of daily NAV data, we get a beta of 1.10.
Here’s why it matters. Think of beta as a performance multiple. The regression estimates that when SPY is up 1 percent, RSP is up 1.10 percent. When SPY is down 1 percent, the fund is down 1.10 percent. RSP’s 1.1 beta tells us that it’s a bit riskier than SPY, so you should expect more return in compensation.
Bottom line: Beta provides a measure of comparative risk. Beta is not confined to measuring market risk, though that’s often the case. You can use it to compare any two sets of returns. The key is to understand what’s being compared.