Blackouts and Correlations

By datatrekresearch in Blog Blackouts and Correlations

Saturday was my 3rd blackout as a life-long New Yorker, and thankfully it was the least dramatic of the trio. The first, in 1977, caused widespread looting. The second, in 2003, was a good deal calmer but lasted through an entire night. This one was over in a matter of hours and unlike the others only affected a part of one borough (Manhattan).

Regular readers know I (Nick) am something of a preparedness nut, so aside from a 16-flight trudge to our apartment, my wife and I were fine. From our window we could see and hear:

An eerie darkness from 57th to 42nd street on the entire west side of Manhattan, all the way to the Hudson River.
A civic-minded citizen (i.e. not a policeman) competently directing traffic with a flashlight on 9th Avenue and 56th Street.
The cheers when the lights came back on around 10:30pm.

As I watched events unfold I spent some time thinking about human nature and what makes blackouts and other events historically so difficult on large cities like New York. The geeky answer is correlations:

Under normal circumstances, Gotham is 8 million people doing their own (largely) law-abiding thing.
Stress the system with a blackout or natural calamity, and that can be enough to push even otherwise respectful city-dwellers to focus on their own well being above all else.
Since humans all have the same basic needs – food, water, shelter, and mobility – everyone doing the same thing at once can easily overwhelm an urban infrastructure designed for only a fraction of such demand.

The same idea about correlations applies to capital markets. In an ideal world, money flows purposefully to its best possible use just as millions of New Yorkers take mass transit to work every day and mostly arrive on time. When there is stress in the financial system, human herding behavior takes over and investors metaphorically cluster on train platforms and pack their way into already-overcrowded subway cars.

That makes correlation measurements like how much each S&P 500 sector trades in sympathy with the index as a whole a useful construct, and the latest data is quite positive:

The average S&P sector shows a trailing 30-day correlation to the index of just 0.63, for an r-squared of 40% (50% is the classic definition of “normal” correlations).
That is the lowest reading since October 2018’s 0.45 correlation and a notable decline from last month’s 0.74.
Last year’s monthly correlation data shows there is room for further near-term improvement since in both July and August 2018 average sector correlations ran below 0.60.

Why have correlations been declining, and what does that mean for near-term stock market action? Three points:

#1: It’s not just 1 or 2 sectors. Over the last 30 days, 10 of the 11 S&P sectors showed lower correlations to the index. The only exception is Utilities, which at just a 0.06 correlation over the last two months is already about as low as it can go.

#2: You can see them in the VIX “fear gauge”. Lower correlations mathematically drive lower overall volatility, which is why the CBOE VIX Index is sitting near year-to-date lows just now at 12.4. Remember that the biggest driver of the VIX is actual price volatility.

#3: They paint a decently positive near-term picture. Absent a shock, low sector correlations tend to be quite sticky, which have the effect of dampening volatility and pushing stock prices higher.

Also worth noting: other asset classes are also showing lower correlations to US stocks:

EAFE stocks (developed economy non-US equities) show 30-day correlations to the S&P of just 0.67, well below their 3-month trailing average of 0.87.
Emerging market equities’ correlation to the S&P 500 was just 0.36 last month, down from an average of 0.74 over the last 3 months.
Despite US equities clearly benefiting from lower long-term interest rates, the correlation of the S&P 500 to +20 year Treasuries fell to -0.23 from an average of -0.51 for the prior 3 months.

What we take away from this data:

#1: Remember that this environment is different from what we had earlier in this longest-of-all US large cap bull market. Monthly S&P 500 sector correlations averaged 0.82 (r-squared of 67%) from 2010 - 2016. The average from the start of 2018 to now is 0.69 (r-squared of 48%), even with 2 dramatic selloffs in the last 18 months that pushed correlations temporarily higher.

#2: Imbedded in these lower US sector correlations must be investor confidence that both the Federal Reserve will lower interest rates and that the global bond rally will carry long-dated Treasuries along in its wake. Bullish market sentiment drives correlations lower as investors pick and choose winners and losers. The last 18 months clearly show that bearish sentiment pushes sector correlations back to their “bad old days” of 2010 – 2016.

#3: The other big macro story – the US-China trade war and its effect on Eurozone economies – explains why EAFE and Emerging Market equities have decoupled from large cap US stocks. The former is dominated by Financials (19% weight), Industrials (14%) and Consumer Cyclicals (11%), all tough bets when negative interest rates are signaling recession risk. The latter is 52% Greater China (China, Taiwan, and South Korea), directly exposed to trade uncertainty.

Summing up: all this fits with the “strong January playbook” Jessica has outlined in recent reports where July 2019 should show good returns for the S&P 500. Now, we just need to keep the lights on…