The bottom-line supply and demand number is stocks-to-use (s/u). 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 all three major markets (wheat, corn, and soybeans) with the r-squared[I]for all three near 100%. In all cases I’m using the cmdty National Cash Price Indexes (weighted national average cash prices from Barchart), and in wheat that means HRW, SRW, and HRS have been weighted to reflect US production of all wheat supplies. The Darin Newsom Analysis, Inc. (DNAI) stocks-to-use numbers are calculated at the end of every month, and then compared to the previous month and the previous year. The DNAI numbers may not agree with subsequent USDA report estimates, but that is understandable given the DNAI numbers are real (based on national average cash prices) rather than imaginary (based on…I have no idea).
WHEAT: The 2021-2022 combined daily average cash price for the three major wheat markets climbed to $8.10, and with only 1 month remaining in the current marketing year this is the highest average cash index price dating back through at least 2014-2015. The end of April figure correlated to an available stocks-to-use calculation of 20.0% as compared to the previous month’s 21.3% and the previous year’s 40.1%. Think about that for a moment: The US all wheat stocks-to-use situation has been roughly cut in half from a year ago, and increased export demand for already tight US supplies due to Russia’s war against Ukraine has not yet been seen. Additionally, the largest wheat crop the US grows – HRW – is in tough shape due to adverse weather with harvest scheduled to begin in the far US Southern Plains in later May. Meanwhile, the second largest wheat crop the US produces – HRS – is still in the bag as the Northern Plains try to dry out from mid-spring blizzards and rain.
CORN: The 2021-2022 daily average of the NCPI though the end of March increased to $6.09, a gain of 21 cents from the previous month and $1.50 above last year at this time. The 2021-2022 price correlates to available stocks-to-use of 8.4% as compared to last month’s 8.7% and last year’s 10.6%. Fundamentally, corn remains one of the clearest pictures we have a bullish market. The attached chart shows the consistency of demand pulling on available supplies, with only a small increase seen late last fall as newly harvested bushels made it into the pipeline. Historically, this set of studies shows the 2021-2022 supply and demand fast approaching levels not seen since the 3-year US drought from 2010 to 2012. However, another set of studies I’m working on for next month shows the 2021-2022 supply and demand situation to be record tight. I will be posting those studies in this space next month as Q3 comes to an end.
SOYBEANS: The 2021-2022 daily average of the NSPI though the end of March climbed to $13.78, closing the gap on the final marketing year calculation for 2012-2013 of $14.52. This month’s average price calculation was an increase of 33 cents from the previous month and up $1.68 from the previous year. I mentioned in corn I’m creating a different set of studies to show monthly stocks-to-use, due in large part to these ridiculously low readings in US soybeans. The new calculation shows the same basic picture, though this year’s end April available stocks-to-use comes in at 2.4% as compared to the previous record marketing year low of 2.0% from 2011-2012. The bottom line remains the same: This marketing year is fast approaching a record tight supply and demand situation, with the reality much higher than the official make-believe set of numbers would have folks believe.
[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.