The bottom-line fundamental number is stocks-to-use. I’ve long said stocks-to-use is the Readers’ Digest version of supply and demand, in that this one number can tell us the bullishness, bearishness, or neutrality of a market’s fundamentals. I’ve also argued endlessly over the years with economists, my point being there should be a strong positive correlation between stocks-to-use and cash price. Given this premise, I’ve developed my system between the two for the five major markets (corn, soybeans, and three major wheat classes) with the r-squared[I]for all near 100%. Using this system I can pull data any day of the month, but by using the end of month number it gives us a picture of the available stocks-to-use (as/u) situation at month-end, a system that should smooth out the wide changes seen at the end of a marketing year. It also puts a spotlight on what I call the Marketing Year Misdirection, meaning supply and demand is a constant flow rather than a hard line drawn between old-crop and new-crop.
CORN: The national average cash price for corn was calculated at $4.53 on October 31, 2023, a price that correlates to an end of month available stocks-to-use (as/u) of 11.9%. The end of September showed $4.49 and 12.0% with last October coming in at $6.97 and 8.3%. While corn’s supply and demand situation remains far from bullish, the fact the US actually has supplies as October came to a close has helped spark an increase in demand. Through late last month the US was on pace to ship 1.77 bb during 2023-2024, an increase of 14% from last marketing year’s reported shipments of 1.554 bb. Additionally, the latest Cattle on Feed report for October 1 showed a 1% increase, year-to-year, of cattle on feed.
SOYBEANS: The national average cash price for soybeans was calculated at $12.38 on October 31, 2023, a price that correlates to an end of month available stocks-to-use (as/u) of 7.9%. The end of September showed $12.09 and 8.5% with last October coming in at $13.73 and 5.4%. The US soybean supply and demand situation tightened a bit last month, though not to an uncomfortable level. With harvest rolling along and supplies coming in, US export shipments have seen a seasonal increase. However, in the latest weekly sales and shipments update US total sales (total shipments plus unshipped sales) were running 29% behind last year’s pace. This is not bullish and puts more pressure on domestic crush and soybean meal exports.
SRW WHEAT: The national average cash price for SRW wheat was calculated at $4.93 on October 31, 2023, a price that correlates to an end of month available stocks-to-use (as/u) of 44.9%. The end of September showed $4.61 and 47% with last October coming in at $8.24 and 27.4%. I don’t say this often, but I have serious doubts about the end of October SRW national cash price index. Based on what we’ve seen with Chicago futures spreads, I do not think the overall supply and demand situation changed as much as the 30-cent rally in cash indicates. The bottom line is even with as/u near 45%, SRW fundamentals remain bearish.
HRW WHEAT: The national average cash price for HRW wheat was calculated at $5.65 on October 31, 2023, a price that correlates to an end of month available stocks-to-use (as/u) of 40.3%. The end of September showed $5.95 and 38.8% with last October coming in at $9.39 and 25%. What jumped out at me was the HRW cash index dropped 30 cents during October while the SRW cash index added 30 cents last month. Again, I have my doubts and am willing to view the month of October as a one-off and see what happens during November.
HRS WHEAT: The national average cash price for HRS wheat was calculated at $6.65 on October 31, 2023, a price that correlates to an end of month available stocks-to-use (as/u) of 38.6%. The end of September showed $6.41 and 39.8% with last October coming in at $9.30 and 28%. While the 2023 US spring wheat crop was larger, demand has started to increase as well. That being said the end of October 2023 as/u was much larger than what was registered a year ago.
[i] R-squared is defined as “a statistical measure of fit that indicates how much variation of a dependent variable is explained by the independent variable in a regression model.” (Investopedia). In my world, it is how closely related two (or more) variables are, in this case national average cash price and stocks-to-use.
