Does Crude Matter?
In a Buzz and Banter posting last month, Scott Reamer had the following comments to our suggestion that UPS (UPS) might cringe in response to higher crude oil prices:
"Since the MIM2 conference is about risk, this question deserves a few sentences. This type of causal relationship mining - transports benefit from lower oil prices; retailers get hurt by higher gasoline prices; shrinking margin debt leads to higher stock prices - is one of the riskiest kind of behaviors that investors can engage in. Why? Because it "seems" right but is provably false.
The Dow Jones Transports and oil prices have a positive - POSITIVE - correlation of 88.3% over the last 2 years and a positive 87.9% correlation over the last 5 years. Those are statistically significant correlations.
As the price of oil has gone UP, the price of the stocks making up the Transports have gone up too. Not down. Repeat: NOT DOWN.
Avoiding this type of thinking is one sure way to reduce risk." -- August 18, 2005 B&B
I am afraid we are going to have to respectfully disagree. It is true that if you calculate the correlation function between the raw data price series of crude oil and the Dow Jones Transports index you get a positive number. Correlation functions generally range from -1 (perfectly inverse correlation) to +1 (perfect correlation.) However, most modelers know that time series data suffer from autocorrelation; processing or normalization of the data is needed to better understand statistical relationships such as correlation.
The above table shows the correlations for the Crude Oil and Transports raw data at various periods: All (Past 16 years), the 1st half of the entire data series, the 2nd half of the entire data series, and various more recent periods. The 1st half is -0.25 and the 2nd half is 0.22, but the entire raw data set is 0.41 - not very logical.
My good friend John Bollinger suggested that I simply look at 1-day changes in price. Here we have a bit more consistent picture of correlation, with most periods slightly negatively correlated.
I decided to look at broader rates of change, because we generally are concerned with moves that last more than a day. For the 5-day rate of change we again have slightly negative correlations for most periods.
Looking at 20-day rates of change, the negative correlations are getting a bit larger.
The 200-day rate of change show negative correlations except for the 5-year period. We thought this strange, so we decided to look at the charts to see what we could see, if anything:
The above chart shows the 12-month rate of change for both Crude Oil and the Dow Jones Transports. It looks almost like a horizontal Rorschach psychological inkblot test. There are few periods when these two series move in tandem, but a highly correlated period (see red circle in chart) was the direct aftermath of 911, when everything tumbled, then recovered a bit in early 2002 (many modelers remove outlier periods like 911 from their models.) For the most part, however, these two series move inversely. There is also something to be gleaned by looking at the charts of the raw data series:
The red arrows show parabolic declines in oil price, and are coincident with major rallies in the Transports. The major exception again is 911. Interestingly, the action of late is one of leapfrogging: oil takes a rest while the Transports rally, then the Transports rest while oil surges. The Transports have worked their way up to test 1999 highs while oil more than doubled in price.
The subject of our original comment last month was UPS. Essentially this decade UPS is unchanged while oil has more than doubled. One can see many instances (arrows) when the price action of the two data series move inversely.
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