How I did this was to take the S&P 500 data (I'm using daily performance, year to date) and segment it by up days and down days. For each data set, I take the covariance of the Energy Sector's returns and the market's returns, and divide that by the variance of the market's returns. To make sure the number is correct, I ran a regression using Excel's data analysis tool kit, and made sure that the beta number there is the same that I calculated, and it was.

Here's the problem. At the time in which I ran the numbers (about a week ago), the S&P500 was down about 7% for the year, while the S&P500 Energy Sector was up about 8% for the year. The results I got are as follows: On market up days, the energy sector beta was ~0.75, and on market down days the energy sector beta was ~1.24. My understanding of interpreting beta in this case is that when the market is up 1%, the energy sector will typically be up 0.75%, and when the market is down 1%, the energy sector will typically be down 1.24%. But how can it be that the energy sector will be down more then the market on a down day, and up less then the market on an up day, if its performance overall is so much stronger then the market? I'm pretty sure that the numbers are correct as I've ran them a few times, but using the formula [COV(energy,market)/Var(market)] and the Excel regression tool, coming up with the same beta numbers. I'm wondering if my interpretation of the results is incorrect. If anyone could shed some light on this for me I'd be very appreciative. Thanks.