Yes, Commodities (or at least their futures) can be worth less than nothing

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At some point on the morning of Monday, April 20, I noticed that front-month crude oil futures were worth about $4 per barrel. “Shockingly low,” I thought. “Crude oil will surely be worth more than that eventually.”

Then I shrugged and went back to a different project, because I didn’t really care. It’s been a long time since I’ve had the stomach to dabble in crude oil speculation and the $7,500 margin calls that go along with it. I felt entirely confident that commodities — the physical stuff we use to make the world go ’round — are always worth something and can never be worth nothing. A barrel of crude oil or a bushel of corn contains usable energy, and even if the supply and demand is currently unfavorable, these commodities can be stored away until somebody does demand that energy.

Stored away. Now, there’s the sticking point.

When retail investors get started trading commodity futures, they’re usually spooked by the physical delivery mechanism of the contracts. “If I speculatively buy a 5,000-bushel corn contract,” a rich dentist in Florida might wonder, “and I don’t get out of the contract in time, is someone going to dump five truckloads of farm product here on my beautifully manicured lawn?” No, they’re always assured. Physical deliveries at the expiration of a futures contract must take place at a warehouse facility licensed by the futures exchange. For corn, these facilities are commercial elevators near Chicago, Illinois.

For the U.S. NYMEX light sweet crude oil futures, here’s the delivery procedure (in part) from the CME Group’s website: “Delivery shall be made free-on-board (FOB) at any pipeline or storage facility in Cushing, Oklahoma, with pipeline access to Enterprise, Cushing storage or Enbridge, Cushing storage … At buyer’s option, delivery shall be made by any of the following methods: (1) by interfacility transfer (“pumpover”) into a designated pipeline or storage facility with access to seller’s incoming pipeline or storage facility; (2) by in-line or (in-system) transfer, or book-out of title to the buyer; or (3) if the seller agrees to such transfer and if the facility used by the seller allows for such transfer, without physical movement of product, by in-tank transfer of title to the buyer.”

For the futures traders who found themselves still long in May crude oil futures on Monday (the day before the contract expired, and while there were no daily limits on how far the price could move), we can see that the worry wasn’t about receiving tanker trucks full of light sweet crude delivered to their home address in May. The worry was about how on earth they could be called upon to find oil storage and pay for oil storage, in Cushing, Oklahoma.

Here’s the trouble: the oil storage capacity in Cushing, Oklahoma, was already 72% full as of April 10 EIA data, with inventory rising about 10% each week for the past three weeks. The remaining 21 million barrels’ worth of Cushing storage capacity is likely already spoken for. Oil traders who found themselves facing delivery on expiring May futures contracts could maybe try to buy storage capacity from some existing industry lease-holder, but at what cost?

That’s why the price of the futures contracts was suddenly negative, a condition this world has never seen before, and which most commodity traders probably never considered could happen.

Maybe crude oil would eventually be worth $10 per barrel in June, but if the desperate physical owners had to pay $50 per barrel in storage fees to carry it through the next couple of months, that meant the value — at least during the peak panic of that futures squeeze — was equivalent to negative $40 per barrel.
In the first 30 minutes after the May crude oil contract turned negative, 6,750 contracts were traded at prices between -$1.43 and -$40.32. There’s no way to know the price levels where those traders entered those contracts, but it’s safe to say that a lot of speculative money was lost on Monday. Commercial oil industry participants presumably rolled out of their May contracts before this timeframe and already have their storage needs lined up for the month.

Storage problems will continue amid the profoundly bearish supply and simultaneously profoundly bearish demand scenario for oil. In the grain markets, the prices in later months tend to be higher than the prices for nearby months (called carry spreads or “contango” in energy lingo) to account for the cost of storing the grain. Oil markets typically display the opposite structure (backwardation), with July 2020 crude oil $0.25 per barrel cheaper than June 2020 crude oil, for instance. Since the sudden COVID-19 lockdown, recession occurred and the world’s pumped oil had to scramble to find long-term storage, and these oil spreads have exploded in the “wrong” direction to unprecedented levels. There is now a contango spread of more than $9 per barrel between the June and July futures contracts. The last time drastic contango was seen in the crude oil futures spreads was in January 2009, at a scale of only $2 to $4 per barrel.

What lessons should grain traders, and grain producers, take from this sudden wake-up call? First of all, don’t get caught with open futures hedges in the last days before a contract’s expiration, unless you’re a commercial operation that’s fully prepared to accept or deliver physical grain via warehouse receipt.

The second lesson, we might think, is to worry that maybe grain prices could go below $0 now, too. However, I’m not sure the real physical grain producers and grain traders should worry much about that. Physical spot crude oil prices haven’t turned negative — it was just (temporarily) the futures contracts that were in danger of receiving delivery at the Cushing-specific location. Physical spot grain prices would likely not turn negative in our world, no matter how bearish short-term supply and demand may get, because again — grains contain inherent value, as energy or as animal feed, and dry grain can be stored until such a time as it is eventually demanded. Even if all the grain storage facilities in America were plugged full, and monthly storage costs were unusually high, farmers can (and sometimes do) just pile grain on the ground. You can’t do that with oil. It wouldn’t be ideal for the grain industry to run out of storage and it would result in some losses, but thankfully it shouldn’t zero out the value of the commodity.

Elaine Kub is the author of “Mastering the Grain Markets: How Profits Are Really Made” and can be reached at masteringthegrainmarkets@gmail.com or on Twitter @elainekub.

© Copyright 2020 DTN/The Progressive Farmer. All rights reserved.

The multiple problems with multi-year cycles

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Perhaps, sitting in a bar one evening, a friend told you that corn yields tend to be great during years that end in “6.” Or perhaps you’ve heard of the 18-year cycle in the stock markets? Or the 60-year cycle in wheat prices? Or the 14 3/4-year cycle in soybean prices, which only holds true if the previous year’s price ended with an even number?

Okay, I made that last one up. But that’s alright — other people baselessly fabricated all those other examples, too, and they all have the same statistical significance (zero).

I hadn’t heard of the 60-year cycle in wheat prices until a gentleman told me about it after a recent market presentation. He has many more years of experience in the wheat market than I do, and I’m always willing to learn new things, so I promised I would look into it. More on that later.

All these multi-year cycles are interesting bits of folklore, and they’re kind of neat to think about. If thinking about them and analyzing the underlying economic reasoning behind them helps market participants better understand the world around us, then that’s great. But if blindly believing them motivates farmers to make or postpone marketing decisions based on unsound science, then that’s bad. That’s why I’m going to try to bust the myth of the multi-year cycle as clearly as I can.

In this universe, many phenomena tend to occur frequently near their averages and less frequently at unusual values, measurements or strengths. This is often shown with the bell-curve chart of the normal distribution. But even if a phenomenon isn’t “normally” distributed, if that thing happens a large enough number of times, it will still always tend toward some average value. That’s the Central Limit Theorem, roughly speaking. It is powerful because it allows us to calculate whether a particular event is truly unusual, like someone who’s 6 feet 7 inches tall. Is that just part of the randomness of the universe? All heights will vary somewhat from person to person.

Therefore, among an entire universe of values, taking just one sample — or just a few samples — is extremely unhelpful when it comes to predicting future values. The Chicago Board of Trade was established in 1848 to exchange cash grain, but a standardized record of corn, wheat and soybean futures prices only exists since 1959. That means there are only six samples of an annual corn price from “a year ending in six,” and six samples is way too few to be confident that whatever trend our human brain might think it sees is anything more than just random statistical noise.

I was slightly more willing to believe in a statistically provable multi-year pattern in wheat, however, because I remember seeing an amazing chart of wheat prices from 1750 through 1960, collected by Hugh Ulrich. I updated that data through 2018, and that made 268 years of information — which is a lot! It’s only four samples of any 60-year period, however, once again, it’s difficult to prove there is anything significant beyond randomness in any purported 60-year cycle in wheat prices.

Furthermore, even the 268 years of data was problematic. Some of it was from England in the 18th century, quoted in English cents per bushel. Some of it was CBOT futures quoted in U.S. cents per bushel. More importantly, the structural economic reality of wheat itself has drastically changed between 1750 and today. The number of man-hours that go into a bushel of wheat, the proportion of a farm family’s income that comes from a single bushel, the proportion of an urban consumer’s budget that goes into a single bushel — none of this is apples-to-apples from one economic timeframe to the next. This is called time-period bias in statistical sampling. Even comparing U.S. stock prices from the inflation-plagued 1970s compared to the easy-money 2010s is problematic.
Let’s actually try to test a multi-year cycle. Say we look at the so-called “decennial pattern” in the stock market, which colloquially claims that years which end in “0” tend to have poor performance, and years which end in “5” have “by far the best” performance. We can gather 90 years of stock market returns since 1928. Maybe 90 years sounds like a lot, but it’s only nine sets of 10, or nine samples from years that end in “5.”

Let’s say the average of all 90 annual returns is only 11.4%, but we calculate the average of annual returns from years ending in “5” at 14.6%. Woohoo! Sounds like those years ending in “5” really are winners — notably including 1995’s 37.6% return. However, if we conduct a two-tailed test for statistical significance using the student’s t-distribution, which mathematically considers the standard deviation of all those returns and the small number of samples, and then compares them against what can occur by mere happenstance, the difference between the all-year average returns, and the years-ending-in-5 average returns is proven to be nothing but statistical noise.

However, if we were magically able to use 300 years of stock market data, and therefore had 30 samples to draw from (30 is widely considered to be the minimum statistically useful number of samples), we could calculate a somewhat larger critical value for this statistical test. And then say there’s maybe 80% confidence that the difference between the two sets of returns might actually be significant (and a 20% chance they’re not). There still wouldn’t be any fundamental explanation for why the final digit of a calendar year should affect equity performance.

Anyway, look and see that stock returns in 2015 were only 1.38% — the worst annual performance since 2008. Anyone who actually invested money based on this hokey idea of a decade-long market pattern would have been sorely disappointed.

To all the believers in multi-year patterns or cycles: please continue to tell me about them! I love hearing about these fables, and I collect them like other people collect pretty seashells. But please don’t sell your grain (or not sell your grain) based on someone else’s flimsy idea that has only ever been sampled four times in history.

Elaine Kub is the author of “Mastering the Grain Markets: How Profits Are Really Made” and can be reached at elaine@masteringthegrainmarkets.com or on Twitter @elainekub.

© Copyright 2018 DTN/The Progressive Farmer. All rights reserved.

Unraveling Corn’s Seasonal Highs

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Sell your grain at the seasonal high in the spring. That’s good advice, especially in normal marketing years with normal weather, normal demand, and normal influence from normal outside markets. Unfortunately, even if such a year ever presents itself, we still won’t be able to predict the exact future date on which the markets will hit their annual high.

Predicting that date, or even predicting the seasonal pattern of prices, has only grown more challenging in the last several years with strange influences from unseasonal demand-driven, investor-driven, or drought-driven rallies.
In the past four years, the corn chart has experienced peaks in early August, in early July, in May, and again in July. Even a farmer who genuinely believes the market will always revert back to its time-tested seasonal patterns might feel a little spooky these days about trusting too strongly in normal spring highs.

If the weirdness of the past several years could be sifted out, I wondered, would the data still show something meaningful about grain markets’ seasonal tendencies? Ideally, I wanted to pinpoint which day of the year has the strongest probability of establishing the corn market’s annual high. But it turns out that if you want the corn market to reveal its seasonal movements, the data requires quite a lot of coaxing.

The results turned into gibberish if I used the continuous front-month corn chart, which switches which years’ crop fundamentals it tracks throughout any calendar year or even throughout any September-to-September marketing year. They were also gibberish if I averaged or even indexed all the prices of the past 15 years for each day of the year. Extreme highs or extreme dates pulled the average far off from where the actual yearly highs seemed to cluster. The problem was they didn’t cluster around any one, simple answer.

Instead, I simplified the question to just this: Within a given year, when should a farmer hedge his upcoming crop of corn? The fundamental outlook for the upcoming crop is always reliably reflected in the new-crop December futures contract. So I looked at each full trading year — December to December — of the past 15 December corn futures contracts to find the best timeframe for pre-harvest hedging.

Fifteen lines on a chart showing the performances of the past 15 December corn contracts look like tangled, multicolored spaghetti. Fortunately, the spaghetti could be unraveled by marking the highs of each year and identifying clusters of dates (local modes in a tri-modal statistical distribution) when those highs tended to hit. Amazingly, those clusters happened in groups of years with recognizable similarities.
I pulled out three categories of years.

* Short Crop Years (2001, 2002, 2006, 2010, 2011, 2012)
Corn production in these years was less than 105% of the five-year average. The trading pattern seems to reflect ever-increasing concern about supply throughout the summer and fall. In five of the six years, the new-crop contracts hit their highs on August 21, August 30, September 11, November 4, or November 30. Discarding the one year that didn’t fit the cluster (2001), the average date for the new-crop corn market high during Short Crop Years was therefore October

* Normal Abundance Years (2000, 2003, 2005, 2008, 2009, 2015)
Corn production in these years was between 105% and 114% of the five-year average (yes, the definition of these categories seems arbitrary, but this is how the highs clustered). The trading pattern reflects normal anxiety and risk premium in the spring and summer when weather is most uncertain for the growing crop. In five of the six years, the new-crop contracts hit their highs on May 3, June 2, June 26, July 16, or July 18. Discarding the one year that didn’t fit the cluster (2003), the average date for the new-crop corn market high during Normal Abundance Years was therefore June 18.

* Supply Crush Years (2004, 2007, 2013, 2014)
Corn production in these years was above 114% of the five-year average and typically followed a shortage, therefore straining the industry’s storage capacity. The trading pattern reflects increasing bearishness as the crop develops favorably through the summer and the huge harvest looms closer and closer. In three of the four years, the new-crop contracts hit their highs on February 22, April 8, or April 18. Discarding the one year that didn’t fit the cluster (2013), the average date for the new-crop corn market high during Supply Crush Years was therefore March 26.

Of course, some annual highs were higher than others, and if I opened up the timeframe to consider a multi-year selling window, it all starts turning to gibberish again. Honestly, the best selling opportunity for 2009 corn would have been to tuck it away for three years and sell it in August of 2012. Similarly, a person could have hedged the December 2016 futures contract at $5.57 1/4 back on December 19, 2012. But it’s not advisable for a farmer to depend on predicting three or four years at a time. DTN Grains Analyst Todd Hultman wrote earlier this week about the chances of hitting new highs within a year-long window: “Spot corn, soybean, and wheat prices all reached or exceeded their one-year highs 61% to 68% of the time. While that may not sound bad, each grain had pockets of three or four consecutive years that went without reaching a one-year high — runs of bad luck that could put a producer out of business.”

In nine of the 15 years I studied, the pre-harvest highs for the December corn contract were the best opportunity to sell the crop, even better than anything offered post-harvest for grain that was stored. In the other six years, there were some higher highs the next spring, but they were rarely high enough to reimburse a farmer for his risk and carrying costs. That is not to say that storing *hedged* grain to capture carry isn’t profitable, only that the best-priced hedging opportunities have tended to be prior to harvest. Pre-harvest hedging works, and there are patterns when the best hedging opportunities have tended to appear.

Make what you will of all this. Remember this analysis was a study of historical data, not a prediction of future events. No one has independently verified my calculations, and I’m not recommending that anyone should buy or sell commodity futures or commodity options (or cash commodities) based on this analysis alone.

Furthermore, just knowing the historical highs for certain categories of years doesn’t solve the eternal problem of figuring out what kind of weather year it will be. Nothing will ever solve that problem. There’s no way to truly know, in March, how large the corn crop will be, or whether it will feel like a shortage, normal abundance, or a crush of supply. It’s comforting to know, though, that no matter how strange individual years have seemed, the corn market still shows recognizable seasonal patterns.

As for soybeans, they merit the same treatment and yield similarly interesting results, but for the sake of column space, we will have to wait until the next Kub’s Den column to unravel their seasonal highs.

Elaine Kub is the author of “Mastering the Grain Markets: How Profits Are Really Made” and can be reached at elaine@masteringthegrainmarkets.com or on Twitter @elainekub.

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