The S&P is down 1.1% from its Sep 5 closing high of 2007.71. The energy sector has led the current selloff. I took a look at past selloffs to see if the cyclical sectors led the way down and to quantify each sector's beta through a regression analysis. The data for this analysis is based on 5 years of daily closing levels of the S&P 500 and the S&P 500 sector indices downloaded from spindices.com and google.com/finance. This graph below is a representation of the data:
On each day, I calculated the percentage loss from the prior all-time closing high. I identified 9 selloffs of 5% or more in the S&P over the last 5 years.
I defined each selloff starting from the day after it made its last closing all-time high until the day it made the low close of the selloff. Then I reorganized the data so each selloff starts at day 1. The average size from top to bottom of the 10 selloffs is 8.4%, and the average duration is 32 days.
Each selloff is labeled by the month and year of its last day and largest loss. Looking at the current selloff, it appears fairly mild so far. Currently, the S&P is down 1.1% over 5 trading days. The average loss during the first 5 trading days of the 9 worst selloffs is 2.2%.
I reorganized the sector data in the same way as the above S&P data. I calculated each sectors' daily closing loss from the date of the all-time closing high of the S&P. For example, the energy sector is down 8.0% from its prior all-time closing high on June 23, but it is down 3.7% over the past 5 trading days. The energy sector has the largest loss of any sector over the past 5 trading days, followed by utilities then telecom.
Energy is a cyclical sector, so it is not out of the ordinary to see this sector lead the downtrend. What surprises me is the second and third worst performing sectors are utilities and telecom, which are countercyclical. The reason for the weakness in these 2 sectors is the dividend or interest rate sensitivity. The bond market has had a stiff 2-week selloff, albeit off quite an elevated level, leading into this week's Fed meeting. Off the bond market selloff, the interest rate sensitive sectors of utilities and telecom have gotten hit. This is reminiscent of the June 2013 selloff of the bond and stock markets when the utility and telecom sectors were the worst performing sectors. There are really 2 selloffs going on here. There is the energy selloff, which is a commodity, USD, and Russian sanction story. Then there is the interest rate sensitive sector selloff, which is a bond market and Fed story, and is related to the performance of the USD.
Looking at the data of the first 5 days of each selloff, only the June 2013 and the current selloff had the countercyclical sectors anywhere near the selloff leaderboard. Every other selloff was led by a cyclical sector: February 2014 - financials, November 2012 - financials, June 2012 - materials, October 2011 - energy, March 2011 - industrials, July 2010 - financials, February 2010 - materials, October 2009 - materials. The average performance of the S&P and each sector during the first 5 days of the 9 selloffs are shown in the table below:
So the staple and healthcare sectors, both countercyclical sectors, were down an average of 1.3%, the least of any sector, and much lower that the loss of the S&P of 2.2%.
To quantify the beta of each sector, I regressed the daily sector data onto the S&P data.
The first 2 rows show the beta of the regression using just the first 5 days of each selloff and the second 5 days. Each contains 45 observations. The third row shows the beta of the regressions using the data of all 9 selloffs (315 observations). The fourth row contains the betas using the entire 5-year data set (1259 observations). The results from the third and fourth regressions are quite similar. The first 2 are also very close, but have a bit more differentiation.
I will refer to the third row regressions, during this discussion unless otherwise noted. Looking at row 3, notice all the countercyclical sectors have a beta of less than 1: utility - 0.2, telecom - 0.6, staple - 0.4, and healthcare - 0.8. Not every cyclical sector has a beta of greater than one. Discretionary has a beta of 0.9 and technology has a beta of 0.8. In the first 2 regressions tech has a beta of greater than one. In the second regression discretionary has a beta of greater than 1. All the other cyclical sectors have betas of greater than 1, and the financial sector has the largest beta at 1.5. A beta of 0.2 (utility) means on average the utility sector was down 0.2% for every 1% selloff of the S&P. The financial sector beta of 1.5 means this sector is down 1.5% for every 1% selloff of the S&P.
It's fair to say that the current selloff is quite out of the ordinary given these data and regression results. Going forward from here, I would venture a guess that it will be difficult for the utility and telecom sectors to continue to lead the market lower. Certainly energy could continue to lead the market down, but there will have to be another cyclical sector to join energy to lead the market lower. Maybe financials will breakdown, if the Scots succeed and cause a run on the Scottish banks. Maybe tech will take a hit on the Alibaba IPO or the NSA snooping fallout. Or it could be industrials that weaken due to a strong dollar hitting sales and earnings estimates or weaker economic numbers.
In summary, the current S&P selloff has been driven by the energy, utility, and telecom sectors. Energy is down on the falling price of oil, and Russian sanctions. Both the utility and telecom sectors have been hit because they are bond market proxies and the bond market is down. I am going to watch closely to see if this selloff spreads to another cyclical sector such as financial, materials, tech, industrials or discretionary. This rotation of weakness into another cyclical sector is critical to continued weakness in the overall market, because the utility and telecom sectors are unlikely to lead the market lower from here.
Disclosure: The author has no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours.
The author wrote this article themselves, and it expresses their own opinions. The author is not receiving compensation for it. The author has no business relationship with any company whose stock is mentioned in this article.