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 $7.86, and with only 2 months remaining in the current marketing year this is the highest average cash index price dating back through at least 2014-2015. The end of March figure correlated to an available stocks-to-use calculation of 21.3% as compared to the previous month’s 22.5% and the previous year’s 40.9%. 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. The world will be keeping a close eye on US weather as we get deeper into spring, highlighting the need for improved winter wheat production and better spring wheat planting and growing seasons than what has been seen the past few years.
CORN: The 2021-2022 daily average of the NCPI though the end of March increased to $5.88, a gain of 24 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 8.7% as compared to last month’s 9.1% and last year’s 11.0%. All three legs of US corn’s demand stool remain strong with live cattle futures spreads continuing to show a large number of head on feed, ethanol demand running strong despite the spike in gasoline prices, and US shipments through Thursday, March 24 projecting total exports of 2.64 bb, on part with last marketing year. Meanwhile, futures spreads remain inverted for both old-crop 2021-2022 and new-crop 2022-2023. This tells me merchandisers should be back in the market before the end of this month, looking to lock in spring and summer supplies. This should also bring support back to the basis market. The attached chart is one of the cleanest, and clearest fundamental charts I track.
SOYBEANS: The 2021-2022 daily average of the NSPI though the end of March spiked to $13.45, surpassing the previous marketing year high of $13.18 from 2013-2014. This month’s average price calculation was an increase of 55.0 cents from the previous month and up $1.69 from the previous year. I will be as succinct as possible: Given what the cash market continues to show us, USDA’s latest March 1 quarterly stocks number of 1.93 bb was absolute hogwash. Did the market trade it? Yes. Does that mean the number is accurate? Absolutely not. The intrinsic value of any market is its cash price, and the NSPI closed February at $15.88 before hitting a high of $16.60 during March. In fact, even with the strong selloff seen to close out the month the NSPI was still calculated at $15.65 on March 31. This tells us the marketing year daily average will continue to increase meaning US supplies in relation to demand have continued to tighten.
[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.