Don't ask me why (it's a long, complicated story), but I happen to own a bunch of 3M stock. So, I was suddenly dismayed Tuesday to see headlines such as, "3M sinks 8%, eyes biggest drop since 2006."
I thought the economy was doing well and consumer stocks should be thriving! But this is also the season for publicly traded companies to release their quarterly earnings reports, and individual stock prices can show volatile reactions to those intermittent burps of fresh, fundamental information.
In 3M's case, the U.S. economy is doing well, generally, and the company is thriving, generally. During the first three months of 2018, it reported growing revenue across its main lines of business, such as selling sticky notes, scrubbing pads, Bondo and Scotchgard. Only now it slightly trimmed the upper end of its guessed-at prediction for what its earnings might be during the rest of the year.
That was all it took to spook traders and send the stock price plummeting.
It's a bit like the monthly Supply and Demand reports released by the USDA for agricultural commodities, where the numbers could tell a bullish story of growing demand and tighter inventories from one year to the next, but if the guessed-at prediction for tighter inventories isn't quite as tight as traders were expecting to see, futures prices could still react in a volatile, negative way.
The S&P 500 Index, a broad measure of the whole "universe" of stocks (or, rather, 500 of the biggest publicly traded U.S. companies) had a bad day in aggregate Tuesday, falling 1.3%. But what struck my eye was how some commodities seemed to sag at the same time that stocks were falling. Not the grains -- not Tuesday, at least -- but crude oil was also down 1.3%, below $68 per barrel, and June lean hog futures fell 2% to close at 74.825 cents per pound. Maybe when traders expect consumers to buy fewer sticky notes, they expect them to take fewer road trips and buy fewer hot dogs, too?
Being a faithful reader of John Harrington's livestock market analysis here on DTN, I knew that the hog market had its own reasons for moving lower Tuesday, including a stiff basis premium versus the CME cash lean index and a bearish cold storage report that showed 12% higher levels of frozen pork inventory at the end of March compared to a year ago (and disappointing drawdowns even of popular pork bellies).
But it did get me thinking about which agricultural commodity markets are most sensitive to the broader outlook about the economy. I looked at the correlations between the day-to-day returns of the stock market (as measured by the S&P 500 Index) and the day-to-day returns of several major agricultural commodity markets: the nearby futures contracts for corn, soybeans, wheat, cotton, feeder cattle, live cattle and lean hogs. I used returns instead of the day-to-day prices themselves, in order to avoid any spurious correlation or mathematical weirdness comparing a stock market index of 2,634.56 points against a cotton price of 82.26 cents, for instance. I pulled the data from the calendar year of 2017 and through the past three volatile months since the stock market topped out on January 26, giving me 328 trading days' worth of data, which was enough to show some interesting results.
Every single one of those ag commodity markets showed a positive correlation with the stock market's day-to-day returns -- and a pretty significant correlation at that!
A positive correlation between two data streams means that when one stream of data moves in one direction, the other stream tends to move in the same direction. And the larger the positive correlation, the more closely the two data streams tend to match each other in direction and scale. Among this group I investigated: the weakest relationship was between wheat futures (the benchmark Chicago contract) and the S&P 500 Index, with a correlation of only 0.05 between the two markets' daily returns. That might not seem like a large number, but in the world of trading models, it's a large enough result to warrant further investigation.
The other grains showed similar ties to the day-to-day direction of the stock market lately. Cotton returns showed a correlation of 0.06 with stock market returns. The livestock markets showed the strongest ties with the stock market's day-to-day direction -- correlations of 0.09 for live cattle futures, 0.10 for lean hogs, and 0.14 for feeder cattle.
Ag markets are ultimately consumer markets, and perhaps it shouldn't surprise us that the outlooks for grocery shoppers should tie in, from day to day, with the outlooks for shoppers of other things, such as Apple's gadgets and Amazon's digital products and GM's automobiles and Coca-Cola's beverages and, of course, 3M's sticky notes. This may be especially true for markets such as the cattle market, where several futures trading sessions can pass between any guidance from the actual cash market out in the country. On a day when there is no fresh information about what price packers are willing to pay or at what level feeders are willing to sell, perhaps the easiest direction for the futures market to take is simply the direction of the outside markets.
Note that these correlations I calculated over the past 16 months of daily trading data may be a rough description of reality in the general "now," but they might not show relationships that would be true in other periods. They don't even show a very robust description of what's going on now; it wasn't a full regression analysis, just a straightforward calculation of correlations. Further study would be required to see if these positive correlations hold true during other economic conditions, and certainly caution should be used before inferring anything about how the prices might behave in the future.
Nevertheless, the takeaway for traders seems to be: when in doubt, go in the same direction of the other markets. And for agriculture producers, remain vigilant about the overall exposure to the broader consumer economy.
Elaine Kub is the author of "Mastering the Grain Markets: How Profits Are Really Made" and can be reached at firstname.lastname@example.org or on Twitter @elainekub.
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