I’ve crunched my first set of numbers. Specifically I’ve computed the beta of XOM (Exxon Mobil) vs. SPY (SPDR S&P 500 ETF) for 365 days ending Feb 4, 2010. My computed beta is 0.125. This is based on daily sampling of closing prices for a 365-day period. Not content with non-uniform sampling (e.g. discarding holiday and weekend data when the markets are not open), I recomputed beta over the same period with interpolated weekend/holiday data and came up with a beta of 0.117. I have not yet bothered to compute R-squared.
These are surprisingly low betas. Also interesting is the difference data interpolation can make… a not insignificant difference of 8.6%
Next I checked out reported betas from other sources. Yahoo Finance reports a beta of 0.35 for XOM (without specifying a time period, sampling method/frequency, or even reference index). MSN Money reports a beta of 0.43. This is a difference of about 23%. This could probably be accounted for by different time periods, etc. But what is most annoying is that these betas are presented without any such context.
I’ve only just started to explore this topic, but I think I’ve started to show that there is significant room for improvement in computing beta. And because beta underlies CAPM and modern portfolio theory, I think this is a big deal.
I’ve already got some more ideas for part III of this series, I just have to crunch some more numbers.