Sep

9

Here's more on risky vs non-risky (more precisely, high beta and low beta) stocks, following up on a previous post.

There is a very nice set of data on Eric Falkenstein's website www.betaarbitrage.com. The data gives monthly returns, going back to 1962, on stocks grouped according to beta. I've used that data along with data on market returns and interest rates from other sources.

I looked at two (overlapping) date ranges. The first is from 1993 to present. That range was chosen since it coincides with the lifespan of the S&P 500 etf SPY. The other range chosen was the past 10 years / 120 months, from 8/31/2001-8/31/2011.

The results of this study are summarized in the following table.

There is only one fairly anomalous finding–a Lake Woebegone effect–in that all beta groupings had positive alpha and out-performed SPY. That is interesting, but is nothing more nor less than an artifact of the fact that the S&P "equal weight" 500 index outperformed the standard "cap weighted" index over these periods.

If we compare within the different beta groupings, we find a great big null result, a result consistent with expectations of the "Capital Asset Pricing Model". Neither low, medium, nor high beta stocks excelled or under-performed the others in any statistically significant way. For the past 120 months, the average monthly alphas of the beta=0.5, 1.0, and 1.5 groupings were, respectively, 0.37%, 0.21%, and 0.40%, each with standard error of about 0.2%. That's a tie in statistical terms.

For the 1993-present period, the alphas were 0.27%, 0.06%, and -0.06%, giving the edge to the low beta (beta=0.5) stocks, but with standard errors in the readings that are comparable with the differences, so again it's a statistical tie.

Reiterating points that I made in a previous post, I think that the returns of risky stocks get a bad rap. First, it's not appropriate to use geometric average returns, which unfairly penalize volatile stocks. A real-world investor can re-balance his portfolio periodically if he feels his market exposure is too big or too small. Second, with risky stocks, you don't need to invest as much money to get the same market exposure, so risky stocks should be credited in some way for the interest on the money that you didn't need to invest. The Capital Asset Pricing Model term "alpha" takes care of these problems in an elegant and logical way.

Technical details:

–Monthly returns data are taken from Eric Falkenstein's site www.betaarbitrage.com, specifically from the "beta=0.5", "beta=1.0" and "beta=1.5" portfolio data supplied here.

Eric writes: "The beta portfolios here target a forward looking beta. Using historical daily data, for the most recent period, but monthly for data prior to 1998, I create portfolios filled with stocks that have the betas closest to 0.5, 1.0, and 1.5."

–Monthly alphas are calculated as follows: alpha = (return - return_riskfree) - beta*(return_s&p - return_riskfree)

–For return_s&p I used the monthly total returns, taken from MarketQA, of the S&P ETF ticker SPY.

–For return_riskfree I used the 13-week treasury bill index (ticker ^IRX on Yahoo Finance) as measured at the start of each month.


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