Seasonality in commodities is one of the great patterns for capturing profits. Think of it as a gift from Nature. It does require that you compare the current price patterns against the price history to decide if the commodity is acting correctly according to the season patterns.
One obstacle in taking advantage of seasonality is that the only way to trade it was using futures, and futures prices can discount the seasonal pattern, require you to roll the position one or more times, and often subjects you to higher volatility. In exchange, it gives you extreme leverage.
As an analyst, you can’t find the seasonal pattern in commodities using futures because the typical data is back-adjusted. That’s necessary because long-term prices are constructed from many shorter contracts. When building these continuous price series, older data is adjusted up or down to blend the prices into a smooth series. Very often, the tail of that series, the older data, can go negative. Then calculating the monthly returns as a percent of price is impossible. Even when the results are not obviously incorrect, the past prices in the back-adjusted data are not the actual prices on those dates, so none of the percentages are correct.
Enter the ETFs
Then seasonality is found using cash prices, which cannot be traded. Enter ETFs. Commodity ETFs are traded just like any equity ETF. You buy a certain number of shares and you can hold them indefinitely. No rolls, no price distortion, but also no leverage. However, seasonal price moves can be pretty big and using seasonality presents unique diversification. You will probably find that giving up the volatility is a fair exchange for no leverage.
We’ll look at some of the more liquid ETFs, CORN (corn), WEAT (wheat), sugar (SGG), and coffee (JO). These are not trade many shares at the moment, but a seasonal trade can be entered using a limit order spread over a few days. The exits should also have plenty of opportunity. With any luck, volume will increase.
While we will calculate the seasonal patterns automatically, we like to confirm whether they conform to the seasonal fundaments, that is, planting and harvest. The first step is to see where most of the product originates. Table 1 gives shows the largest producing countries for the four commodities.
Table 1. Commodity production by country.
Sources: Corn and wheat, Index Mundi; Coffee, Statistica; Sugar, USDA.
Classic Seasonal Patterns
The US dominates corn production, and the combination of the US and China is overwhelming. Because both countries are in the Northern hemisphere, we can expect the same seasonal pattern. On the left in Chart 1 the monthly returns give a clear pattern of planting in March/April and harvesting in September. Note that the blue line spikes in July, indicating a crop “scare” in the middle of growing season. We normally expect to see a rally in the early Summer when weather creates uncertainty about the health of the crop, but that turns out to be far less often than thought. The greatest problem is in the Spring when extreme rainfall delays planting and farmers may shift from corn to soybeans. The highest prices are in the Winter when inventories are low and planting is uncertain.
Chart 1. Corn seasonality. Cash prices from 1989 (left) and a comparison of cash, futures, and CORN ETF (right). Data source: CSI.
On the right is a comparison of cash, futures, and the ETF CORN, from 2010 when the ETF started trading. During these five years the Summer weather has been less certain and prices rallied in July. Futures, which reflects more speculation, shows a much bigger move. But corn is a hearty crop and prices returned to the normal lows in September. The EFT tracked cash prices fairly well, indicating that it may represent seasonality very nicely.
The US is far from the biggest producer of wheat, but all of the top countries are again in the Northern hemisphere; therefore, Winter wheat would be planted in the Fall and harvested in the Spring. Why can’t we see that clearly in Chart 2? It turns out that it’s easier to see in a more recent period from 2010.
Chart 2. Wheat seasonality from cash (left) and from futures (right). Data source: CSI.
During the past five years, the pattern can be seen in Chart 3. Prices rise from October through March, as inventory is depleted and the new crop is uncertain. Prices then decline into the seasonal low at harvest, about May. The spike in July is related to the Summer crops in corn and soybeans, because they also serve as a substitute for livestock feed. Feed is purchased according to which crop will generate the most protein for the cheapest price. Cattle have little to say in the choice. Then the wheat seasonal pattern turns out to be similar to corn and other Northern hemisphere crops.
Chart 3. Comparison of cash, futures, and WEAT ETF seasonality.
Sugar and Coffee
Sugar has a more complex seasonal pattern because half of production in in the Southern hemisphere and half in the Northern. The comparison on the right of Chart 4 is easier to see. If the Northern season has its season from April to November with its peak in July, the Southern hemisphere is from November to April with its peak in January. That gives two seasons and two trading opportunities.
Chart 4. Sugar seasonality from cash (left) and a comparison of cash, futures, and the SGG ETF (right) from 2010.
Coffee is nearly all grown in the Southern hemisphere, with a small amount of US coffee produced in Hawaii. The coffee ETF (JO) has been traded since mid-2008. Again we can see that the ETF does a good job tracking the cash seasonality. The harvest lows are in June and the new season begins in November.
Chart 5. Coffee seasonality from cash (left) and a comparison of cash, futures, and the ETF JO (right) from 2008.
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