I was mulling around Bloomberg the other day when I came across the agricultural mapping function. It’s not really developed enough to be usable beyond a regional level, but it got me thinking about what the empirical evidence says on the link between soybean and cotton futures.
Empirical evidence of a link
Theoretically, there should be some sort of link given that there is a large overlap in the growing areas of soy and cotton, particularly in the Mississippi delta area in the southern US. And, since the futures are on US deliveries, naturally there should be some sort of lagged link: if soybean prices are relatively higher one year, we might expect more soy planting the following season with at least some of this at the expense of cotton. This isn’t to claim necessarily some sort of direct correlation, but we can assume that there are forces which keep the ratio of prices between the two to be within some range. Otherwise, farmers would switch from one crop to the other (although this process may take up to a year for farmers to react).
Firstly, we can take a look at the active futures price for each. Clearly there is some link, but it’s also obvious that there is room for wild swings. It would also seem at first glance that the correlation between the two is increasing. Interestingly though, the distributions of the two assets are quite different. Cotton futures prices seem to follow a lognormal distribution, while soybeans are more uniform.
But while their distributions are different, the ratio between the two is quite stable. So I took a look at what the ratio of active soybean futures contract prices to cotton futures prices looks like. Looking at the autocorrelation results of this ratio, we can see it is most definitely not the product of a random walk. This leads me to believe that there is at least some predictive power in this ratio.
The trade idea
Which leads me to my actual trade idea. I believe that the ratio of soybean/cotton futures prices is stable enough on a weekly basis to form a solid trading strategy during periods of low to moderate volatility.
In the above graph, the blue line represents the ratio, and the straight solid blue line represents the average of the ratio over this period. The yellow and grey horizontal lines represent 1/2 standard deviations above and below the average respectively. Finally, the orange line in the above graph is another mean reverting GMB function (similar to the one covered in an earlier post and the second part). Important to note as well, however, that the data seen above skips the period of massive volatility in cotton prices in 2010/2011.
As we can see, over the past 5-6 years the average has oscillated around 15.6 , and has gone as high as ~24 and as low as ~12. During periods of relatively low volatility, such as right now before the important spring data releases, I think it is worth trading on this relation holding. Entry/exit points at the 1/2 standard deviation mark provide a good balance between transaction costs and trading levels. Stop loss levels should be placed not too far away from these bounds.
In order to put down roughly equal amounts on each commodity, a ratio of 3:2 cotton to soy contracts, for a total notional of about $200,000 would roughly produce offsetting trade legs. Going long 2 soy contracts and short 3 cotton contracts (for a little under $15,00 in margin) could deliver perhaps $14,850 gross profit should our ratio returns to its historic levels; which seems to happen within a cycle of about 6 months. I didn’t look at further dated contracts, so it would obviously involve rolling over the positions several times.
Of course, there is the large risk that dynamics between the two commodities change, and if the basis actually increases between the two there would be a risk of large losses. From what we saw with the autocorrelation function, the possibility of such a violent basis change should be fairly low, especially during a time of few data releases. Still though, while stop losses could minimize this risk, but the possibility of sudden and violent volatility should never be discounted!