S&P 500, VIX Index, Equity Sector Diversification, Macro – Talking Points
- The S&P 500 has 11 sectors to choose from to diversify equity portfolios
- Expanding the exposure is not always perfect to avoid market volatility
- What levels of VIX undermine this strategy, and what can traders do?
What is stock sector diversification?
If an investor wants to diversify their exposure in the US stock market, there are plenty of sectors to choose from in the S&P 500. On the pie chart below, there are 11 to choose from, ranging from growth-oriented information technology to value-centric industrial firms. To hedge against sector-specific risks, a trader can spread their portfolio between a combination of these.
In such a case, if the S&P 500 hits a bump, losses in one corner of the market may be offset or reduced by gains in another. This may work if all sectors of the market do not collapse. But when almost every corner of the index falls in a binary move, a stock diversification strategy becomes increasingly unreliable.
This is not a case against a stock diversification strategy. This is rather to analyze market conditions affecting sectors moving together in the S&P 500. This is done using the CBOE Volatility Index (VIX), also known as the market’s preferred ‘fear meter’. With that in mind, what levels of VIX should traders and investors see that risk undermining a stock diversification strategy?
S&P 500 Sector Division
What is VIX and why should traders see it?
VIX was created in 1990 to use as a benchmark to analyze volatility projections in the US stock market. It trades in real time, reflecting expectations of the price movement over the next 30 days. As such, it tends to have a very close inverse relationship with the S&P 500. In other words, when stocks fall, VIX rises and vice versa. For a deeper dive into VIX, check out a complete guide here.
This inverse relationship can be seen in the next chart, which shows the average S&P 500 performance compared to similar VIX levels since 2002. For the survey, average weekly data is used to calculate monthly results. This is done so that it helps to avoid shortening the ‘volatility of volatility’, whereas a monthly reading may run into the fact that the data may not capture the broader trend.
Looking at the data, April tended to see the most optimistic performance for the S&P 500, averaging 2.06%. Afterwards, this performance declined before reaching the bottom in October, when the leading stock index returned around -0.1%. During this period, we saw the VIX rise, which started at. 18.30 in April and then rose to 21.23 in October. Knowing this, we can now look at what’s happening within the S&P 500.
VIX versus S&P 500
S&P 500 cross-sectoral relationships with VIX
To see when a stock sector diversification strategy can fail, we need dedicated price indices for the 11 sectors in the S&P 500. The data used for the latter only go back to 2002. We can then find correlation levels between VIX and for each sector on a monthly rolling basis. The correlations go between -1 and 1. A -1 reading means perfect reverse movements between two variables, while 1 is perfectly in unison.
Averaging all 11 results in each period yields a cross-sector correlation reading with VIX. Next, the correlations are divided into groups ranging from strong (-1 to -0.75), medium (-0.75 and -0.50) and weak (all values greater than -0.5). A strong reverse reading reflects VIX rising / falling as sectors fell / climbed along with greatest consistency. The weak represent sectors that move more freely.
In 7 out of 12 months, higher levels of VIX were associated with stronger cross-sectoral inverse correlations with the ‘fear meter’. For example, the average weekly price of VIX in March was 26.55 as the S&P sectors moved most in unison. The price dropped to 15.28 as we saw sectors move more freely. Knowing this, what levels of VIX can undermine a cross-sectoral diversification strategy?
VIX Price versus different levels of S&P cross-sectoral inverse correlations
When can a diversification strategy for equity sectors fail?
We can now average the prices of VIX for all months and years since 2002 based on the 3 correlation groupings. At the same time, we take an average of the weekly performance for all S&P sectors and adjust them based on the same categories. On the chart below, we can see that the result was quite predictable. Stronger inverse correlations with VIX aligned with increasingly poorer performance between sectors.
When we saw all the sectors move most opposite of VIX, the average price of the ‘fear meter’ was 22.85. When this happened, the average return for each sector was -0.47%. Conversely, when the sectors moved more freely relative to VIX, the price of the latter was 16.72. At that price, the average return between each sector was + 1.08%.
It should be noted that correlation does not imply causation. Just because VIX is at some arbitrary price does not mean that it is the only cause of trade dynamics between sectors. It is rather used here as a frame of reference. What actually causes markets to fall into binary traits is a combination of fundamental factors: monetary policy, fiscal spending, corporate guidance and more.
What can traders do about volatility?
Knowing this information, what can traders do when they expect high volatility and strong cross-correlations across market sectors? High outbreaks of volatility are often short-lived and temporary. In these times, refuge-oriented assets tend to perform better. This includes the US Dollar, which often increases in times of global market stress. Short selling stocks is another. It also helps reduce exposure to current and new businesses. Combining these can help prepare traders on some bumpy roads.
VIX price versus performance for S&P 500 sectors based on correlation groupings
— Written by Daniel Dubrovsky, Strateg to Technewscity
To contact Daniel, use the comment box below or @ddubrovskyFX on Twitter