KSU AgEcon: “The Impact of U.S.-China Trade Conflict on U.S. Corn Prices”

The Impact of U.S.-China Trade Conflict on U.S. Corn Prices

Daniel O’Brien – Extension Agricultural Economist, K-State Research and Extension

May 17, 2019



Estimates of the impact of U.S.-China trade conflicts from outside University sources range from $0.13-21 per bushel on the high side to $0.08 per bushel.  An analysis by Kansas State University Ag Economist Daniel O’Brien based on an analysis of seasonal price patterns estimates that average monthly impact from January through – mid May 2019 averaged $0.20 per bushel per month.

Underlying CFTC position of traders data confirms the record “bearish” short sale aggregate position of Managed Money (Spec) traders that began in January 2019 and trended to record bearish levels in April.

That market prospects for U.S. corn declined during the January – early May period is in evidence from the USDA World Agricultural Supply and Demand Estimates (WASDE) reports during this time.  The USDA increased its projected U.S. corn ending stocks-to-use from 11.85% in January to 14.45% in May 2019 for the “current crop” 2018/19 marketing year for U.S. corn.  During that time the only changes affecting U.S. corn supply-demand balances were adjustments on the usage side – with market expectations for U.S. corn use declining.  The U.S. corn projected season average price declined $0.10 per bushel from $3.60 in February down to $3.50 per bushel in the May WASDE report.

Crop revenue insurance coverage levels are an additional important factor to consider in assessing the impact of low U.S. corn futures prices during the January through early May 2019 period. The planning price for corn crop revenue insurance in year 2019 in Kansas was determined by taking the average of DEC 2019 Corn futures during the month of February 2019.  The calculated corn planning price for crop revenue insurance in Kansas for conventional (non-high amylose) corn was $4.00 per bushel.

To the degree that U.S.-China trade conflicts may have led to lower DEC 2019 corn futures prices during the February period, then Kansas and U.S. corn producers’ 2019 crop revenue insurance planning prices and revenue coverage are lower than would have occurred otherwise.  In a year with significant 2019 crop production risk to date, negative effects on U.S. corn producers’ crop insurance revenue coverage levels are likely to be a critically important factor.



Since December 2018, U.S. corn prices have been moving in a pattern contrary to normal seasonal price pattern found in Kansas, with essentially no seasonal price increases.  During the January 2019 through projected estimated May 2019 period, on a monthly basis U.S. corn prices were from $0.07 to as much as $0.34 / bushel under the levels they would have been if normal seasonal average price patterns prevailed that we have seen historically in Kansas corn markets.

The main idea of this article is that market perceptions about the progress of U.S.-China trade negotiations or lack thereof seem to have had a negative effect on U.S. corn markets from January through mid-May 2019 – at least until U.S. corn planting concerns began to predominate.

Following is a timeline since June 2018 of a U.S-China trade conflict actions and reactions, quoted from a Reuters article on May 8th, titled “Timeline: Key dates in the U.S.-China trade war”,


July 10, 2018 – S&P 500: +0.35% , United States unveils plans for 10% tariffs on $200 billion of Chinese imports.

Aug. 1, 2018 – S&P 500: -0.10% , Trump orders USTR to increase the tariffs on $200 billion of Chinese imports to 25% from the originally proposed 10%.

Aug. 7, 2018 – S&P 500: +0.28% , United States releases the list of $16 billion of Chinese goods to be subject to 25% tariffs. China retaliates with 25% duties on $16 billion of U.S. goods.

Aug. 23, 2018 – S&P 500: -0.17% , Tariffs on goods appearing on the Aug. 7 lists from both United States and China take effect.

Sept. 7, 2018 – S&P 500: -0.22% , Trump threatens tariffs on $267 billion more of Chinese imports.

Sept. 24, 2018 – S&P 500: -0.35% , U.S. implements 10% tariffs on $200 billion of Chinese imports. The administration says the rate will increase to 25% on Jan. 1, 2019. China answers with duties of its own on $60 billion of U.S. goods.

Dec. 1, 2018 – S&P 500: +1.09% (Monday, Dec. 3) , U.S. & China agree on a 90-day halt to new tariffs. Trump agrees to put off the Jan. 1 scheduled increase on tariffs on $200 billion of Chinese goods until early March while talks between the two countries take place. China agrees to buy a “very substantial” amount of U.S. products.

Feb. 24, 2019 – S&P 500: +0.12% (Monday, Feb 25) , Trump extends the March 1 deadline, leaving the tariffs on $200 billion of Chinese goods at 10% on an open-ended basis.

May 5, 2019 – S&P 500: -0.45% (Monday, May 6) , Trump tweets that he intends to raise the tariffs rate on $200 billion of Chinese goods to 25% on May 10.

May 8, 2019 – S&P 500: -0.16%

From this it seems that the DEC-JAN period started off quite positive for the U.S.-China trade negotiations, with a temporary 90 day halt of tariffs.  Then by the time we get to late February, there is a negative announcement in the market – apparently being interpreted by corn market participants that limited positive progress had been made in the negotiations.


Flat vs Seasonal Prices in the “Current Crop” 2018/19 Marketing Year

If monthly differentials are averaged across the December 2018 through projected May 2019 period, the average monthly price difference between a regular seasonal pattern of U.S. corn prices and what occurred is estimated to be $0.20 per bushel per month based on cash and futures prices available through May 16, 2019.  If the next step is taken to weight these prices by USDA estimates of monthly U.S. percent of cash corn sales, then the average monthly U.S. corn price difference is scaled down to $0.07 per bushel per month.  Which approach to take – weighting by average monthly sales percentages or not, is a matter of debate.

Figures 1abc and Figures 2a-b illustrate this pattern in futures and cash corn prices.  Figures 2a and 2b especially and Table 1a show specific details of how during January through mid-May 2019 U.S. cash corn prices have been less than would be occur should average seasonal price patterns occur based on historic seasonal corn price patterns in Kansas.


“Bearish” Corn Market Impact Shown in CFTC Commitment of Traders Data

The bearish tone of the U.S. corn market during the January to mid-May 2019 period is well documented, as shown by the Commodity Futures Trading Commission (CFTC) commitment of traders data in Figures 3a-d.  Note especially the record bearish or “sell” position of Managed Money Traders as shown in Figures 3a and 3d.  The implication is that grain market speculative traders held record bearish positions during parts of January to mid-May 2019.

The CFTC data for the aggregate trading positions of Managed Money Traders (Specs) or MMT-Specs shows something of the “effect” of a change in market sentiments about a positive resolution to the U.S.-China Trade conflict beginning to occur during January 2019 and continuing through mid-May 2019.

The CFTC Commitment of Traders data indicate that December 2018 was a time of relative optimism for MMT-Specs, as their long positions for the weeks ending 12/2-12/31 were net long by a range of 200-246 million bushels.

Progressing forward, January 2019 showed MMT-Specs week ending positions ranging from 246 mb long on 1/8/2019 to 49 mb short on 1/29/2019.

In February 2019, short positions of MMT-Specs grew from 33 mb short on 2/5/2019 to 590 mb short on 2/26/2019.

In March 2019 the trend to short positions for MMT-Specs accelerated, from 964 mb on 3/5/2019 to a range of 1.092 – 1.415 billion bushels the rest of the month.

Then in April 2019 new records were set for short positions for Corn futures, with a range of 1.319 bb to 1.721 bb for the month, with the record large net short position of 1.721 bb set for the week ending 4/23/2019.

For the week ending May 7, 2019 net short positions for corn futures traders were 1.480 bb.  Since then, planting concerns have taken over and MMT-Specs have been moving away from their short positions and rebalancing toward the long side.

This CFTC commitment of traders information indicates that corn market Managed Money (Spec) traders’ sentiments turned or transitioned to being decidedly bearish as time moved from the beginning the December 2018 through January and February 2019.  And that the bearish trend continued on to record short or “bearish” levels in April 2019 and early May 2019.

The implication here is that the lack of success in U.S.-China trade negotiations have been a primary causal factor in that occurring.   The success of the 2019 Brazilian 2nd corn crop also contributed – likely in a sort of “piling on” negative, confirming manner.


 “Direct” Impact on Soybeans Effect Expected Corn Market Supply-Demand & Prices

The primary news affecting grain markets during that period was the ongoing status of U.S.-China trade negotiations.  While U.S. corn exports to China have not been a main driver in U.S. grain markets and trader sentiments, sharp reductions in U.S. soybean exports to China as a result of these trade tensions had increased the likelihood of reductions in U.S. soybean planted acreage in 2019, and compensatory increases in U.S. corn acreage.

This is consistent with the USDA’s analysis at the 2019 Agricultural Outlook Forum in Arlington, VA in February 2019.  The direct effects on the U.S. soybean market from reduced U.S. soybean exports to China resulted in strong negative indirect secondary impacts on the U.S. corn market, as the USDA and the grain trade expected U.S. corn acreage and production to increase – with prices moving sharply lower for U.S. corn in fall 2019.  And that sentiment has held sway among the corn trade until recently in mid-May 2019 when 2019 U.S. corn planting problems became serious enough to cause corn futures prices to begin trending higher.


Evidence from USDA WASDE Report Projections

Also, it is noteworthy that in its World Agricultural Supply and Demand Estimate (WASDE) reports, since November-January 2019 the USDA has lowered its forecast of U.S. Corn season average prices by $0.10 per bushel from $5.60 to $5.50.  Note that this is calculated as a season average price “weighted by % monthly sales” basis by the percent of annual grain marketings projected for all 12 months of the “current crop” 2018/19 marketing year – starting September 2018 and lasting through August 2019.  So, the USDA’s price projection for “current crop” MY 2018/19 of $5.50 per bushel relies in large part on the accuracy of the USDA’s estimates of past monthly weightings of sales.

If in this marketing year, U.S. farmers have delayed sales during the harvest period of September-November in greater proportion than normal until later (say during December – April).  IF that occurred, THEN the estimated monthly percent of marketings used by the USDA’s calculation method would underestimate the impact on U.S. corn producers of the flat or non-seasonal price action that occurred during the January through mid-May 2019 period.


Impact on Crop Revenue Insurance Planning Prices from February 2019

An important factor to consider for the sake of U.S. corn producer’s risk management purposes is how the corn futures market’s bearish reaction U.S.-China trade issues potentially affected crop revenue insurance payments for the 2019 crop.  Revenue insurance planning prices for 2019 crops are calculated by taking the average of daily closes for DEC 2019 corn futures during the month of February 2019.  Therefore, to the degree that there was a negative effect on DEC 2019 Corn futures from U.S.-China trade negotiations, then planning prices for revenue-based crop insurance coverage for all U.S. corn producers will have been diminished – since DEC 2019 Corn futures prices were negatively affected by U.S.-China trade conflicts during February 2019.


Evidence from Other University Sources

KSU Agricultural Economist Nathen Hendricks cites other analyses that are for the most part consistent with these findings.  Hendricks cites the following studies and results:

  • Researchers at Iowa State University estimate that corn prices decreased by 4-6% or $0.13-$21/bushel as a result of the U.S.-China trade conflict. See p. 8 of their study: https://www.card.iastate.edu/products/publications/pdf/18pb25.pdf . They use a partial equilibrium model that essentially has supply and demand curves to simulate the impact of the tariffs.
  • Researchers at the University of Illinois estimate that corn prices decreased by $0.08/bushel in 2018 due to the U.S.-China trade conflict. See table 1 in their study: https://farmdocdaily.illinois.edu/2019/04/the-trade-conflict-impact-on-illinois-agriculture-in-2018.html. They use regression analysis where they effectively compare how much prices dropped from spring to fall 2018 and how much larger the price decrease was than would have been predicted based on the relatively large yield in 2018.


Joe Janzen of Kansas State University has also addressed some of these and other related issues on KSU Agriculture Today radio on Wednesday, May 15th.  Janzen discussed the probable impact of another round of trade aid for U.S. farmers.  In particular Jaznen examined the probable impact of direct trade mitigation that occurred for farmers through the first round of Market Facilitation Payments (MFP) as a compensation to U.S. farmers for the negative affect of incomes from the U.S.-China trade conflicts.


Examining Corn % Stocks-to-Use vs Cash Prices for the U.S. & the “World-Less-China” (KSU Ag Economics)

Daniel O’Brien – Extension Agricultural Economist, Kansas State University

September 14, 2018

In their September 12, 2018 World Agricultural Supply and Demand Estimates (WASDE) report, the USDA provided estimates of ending stocks and percent ending stocks-to-use for corn markets in the U.S., the World, and for the “World-Less-China”.  The following analysis estimates that U.S. corn prices are projected by the USDA to be $0.25 to $0.60 /bu lower in “new crop” MY 2018/19 than would have been the case following average stocks-to-use versus price relationships during the MY 2012/13 through “old crop” MY 2017/18 period. It also discusses why this may be happening, and what the implications may be for post-harvest price recovery.

A. U.S. Corn % Stocks-to-Use vs Prices

In spite of mostly pessimistic responses to recent USDA reports and other market projections, U.S. corn market supply-demand balances as represented by % ending stocks-to-use (% S/U) are forecast to tighten.  This continues the trend toward tighter U.S. corn % S/U since the 2016/17 marketing year.  Since the drought-induced U.S. short corn crop of 2012, % S/U has trended first higher and then lower.  In MY 2012/13 U.S. corn % S/U was a low of 7.4%, and then trended upward to 9.2% S/U in MY 2013/14, 12.6% S/U in MY 2014/15, 12.7% S/U in MY 2015/16, and the recent high of 15.7% in MY 2016/17.  Since then, U.S. corn supply-demand balances have declined to 13.4% in “old crop” MY 2017/18, and are projected to fall to 11.7% in “new crop” MY 2018/19 which began September 1, 2018. 

With U.S. corn % S/U declining to 11.7%, U.S. corn season average prices are projected to be in the range of $3.00 to $4.00 per bushel – with a midpoint projection of $3.50 /bu.  For market comparison sake, in the 2014/15 & 2015/16 marketing years with 12.6% S/U and 12.7% S/U, respectively, U.S. corn prices averaged $3.70 /bu & $3.61 /bu, respectively.  

From examining historic U.S. corn stocks-to-use versus U.S. corn marketing year average prices, it is estimated that the $3.50 /bu forecast at 11.7% S/U for “new crop” MY 2018/19 is approximately $0.25 lower than would have been the case during the MY 2012/13 – MY 2017/18 time period.  This assumes similar price responses to changing U.S. corn % S/U – all else being equal.  The assumption of “all else being equal” may or may not hold true – especially with current tensions affecting U.S. agricultural trade and market worries and concerns about their impact on U.S. corn supply-demand and prices.

B. “World-Less-China” Corn % S/U vs U.S. Corn Prices (Adjusted for the U.S. Dollar)

Similar to the U.S.-domestic based market analysis above, World corn market supply-demand balances (as represented by % S/U) are forecast to tighten.  Accounting for or “isolating” the impact of China’s large corn stock piles, “World-Less-China” corn % S/U is calculated. Following a similar approach to the U.S.-only calculations above, since the short corn crop year of 2012, % S/U has again trended first higher and then lower.  In MY 2012/13 “World-Less-China” corn % S/U was a low of 9.5%, and then trended upward to 12.2% S/U in MY 2013/14, 13.8% S/U in MY 2014/15, 12.9% S/U in MY 2015/16, and the recent high of 15.3% in MY 2016/17.  Since then, “World-Less-China” corn supply-demand balances have declined to 13.9% in “old crop” MY 2017/18, and are projected to fall to 11.5% in “new crop” MY 2018/19 which began September 1, 2018. 

With “World-Less-China” corn % S/U declining to 11.5% in “new crop” MY 2018/19, the U.S. corn season average price range-midpoint is forecast to be $3.50 /bu (as stated above).  Adjusting for the average U.S. dollar index value of 90.5480 in the first week of “new crop” MY 2018/19, the “dollar adjusted” U.S. corn price equals $3.17 /bu (compared to $3.50 /bu prior to U.S. dollar adjustment).

From examining historic “World-Less-China” corn stocks-to-use versus dollar-adjusted U.S. corn marketing year average prices, it is estimated that the $3.50 /bu forecast at 11.5% S/U for “new crop” MY 2018/19 is approximately $0.60 lower than would have been the case during the MY 2012/13 – MY 2017/18 time period – all else being equal.  

In other words, at an unadjusted U.S. corn price of $4.10 /bu (up $0.60 /bu from the USDA midrange price forecast of $3.50 /bu), the U.S. price would “fit” on the “World-Less-China” corn % S/U vs dollar adjusted U.S. corn price relationship (with 11.5% S/U and a dollar adjusted U.S. corn price of $3.71).

C. Corn Market Implications for “New Crop” MY 2018/19

There are important implications for the U.S. corn market if the findings of this article are accurate that U.S. corn prices are estimated to be $0.25 to $0.60 /bu lower than would have been the case under similar market conditions during the MY 2012/13 – MY 2017/18 period.  

First, the question of WHY this would be the case needs to be asked.  It is possible that prevailing market fears about how U.S. trade tensions with China, Mexico, Canada, Europe, and other countries may be being expressed in a somewhat pessimistic outlook for U.S. corn markets for the remainder of “new crop” MY 2019, i.e., from mid-September 2018 through August 2019.  Restated, fears about U.S. corn export prospects perhaps are being expressed in terms of lower corn bids in futures and cash markets.

Second, the seasonal focus of U.S. corn markets heading into the 2018 harvest is largely on prospects for a large if not near record U.S. corn crop, and this is likely having a strong negative affect on U.S. corn markets.   This would be an expected normal seasonal influence on U.S. corn markets in normal or large crop years. 

Third, IF U.S. corn prices ARE somewhat lower at this time than expected according to past market behavior and responsiveness to similar supply-demand balance scenarios, THEN it may be that post-harvest demand for U.S. corn in domestic and foreign markets may provide support for prices to move above pre-harvest / harvest levels in the U.S. during the post-harvest periodNormal seasonality tends to support post-harvest recovery of U.S. corn prices in normal or large crop years – as buyers typically have to bid higher to motivate farmer’s to sell corn out of on-farmer or even commercial storage. 

However, to the degree that U.S. corn prices may currently be lower than otherwise anticipated given U.S. supply-demand balances, THEN there seems to be an increased likelihood of a higher than normal post-harvest price recovery.

Fourth, the actions and reactions in coming months of farmers in both South America and the United States to corn and soybean market distortions brought about by U.S.-foreign trade tensions are likely to drive planting / acreage decisions and U.S. and World corn production prospects in 2019.   The prospect of higher soybean acres in Argentina and Brazil in 2019 in response to higher prices and other factors – and the possibility of lower corn acres in these countries as a result – may affect U.S. farmer’s expectations of corn versus soybean profitability in late-winter / spring 2019, and also their subsequent 2019 U.S. corn & soybean planting decisions.

D. Conclusions About Efficient Markets & the Purpose of this Article

Any analysis of the level of U.S. grain prices and whether they are justified at their current levels or not quickly turns to questions about the pricing efficiency and effectiveness of agricultural markets. While the seasonality of U.S. corn prices from harvest through post-harvest periods is not generally disputed by market analysts, there are often disagreements among them about the expected final levels of futures and cash markets in futures months or time periods.  

The purpose of this article is not necessarily to criticize the accuracy of current corn market price forecasts in the futures and options market.  However, it is intended to point out changing levels of price responsiveness to adjustments in U.S. and foreign corn percent ending stocks-to-use, and to help those marketing U.S. corn to make more informed sales and merchandising decisions in “new crop” MY 2018/19.

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Source: https://kscorn.com/2017/10/13/kansas-corn-estimate-rises-october-crop-report/

How Algorithms in CME Grain Futures Determine Which Trades are Filled (K-State Ag Economics)

How Algorithms in CME Grain Futures Determine Which Trades are Filled

Prepared by G. A. (Art) Barnaby, Jr., (barnaby@ksu.edu) Professor, Department of Agricultural Economics, and Daniel O’Brien, (dobrien@ksu.edu), Extension Agricultural Economist, K-State Research and Extension, Kansas State University, Manhattan, KS 66506,

Kansas State University Department of Agricultural Economics – May 16, 2018


Electronic trading has replaced pit trading.  So now the question is which orders are filled first?  The computer decision rules or “algorithms” used to determine how orders are filled are intended to be completely objective – i.e., without any human bias or error being entered in the queuing for filling orders. 

The function and use of these “decision rule based algorithms” for Chicago Mercantile Exchange (CME) grain futures contracts was a topic of discussion at the 2018 Agricultural Commodity Futures Conference on April 5-6 in Overland Park, Kansas. 

This conference was co-sponsored by the Commodity Futures Trading Commission (CFTC) (https://www.cftc.gov/) and the Kansas State University Center for Risk Management Education and Research (www.k-state.edu/riskmanagment).  Papers and power point slides from the conference are located at the following web address: http://www.k-state.edu/riskmanagement/conference.html

The objective of this paper is to clarify questions about how the CME fills orders via algorithms that were raised in an April 16th paper posted on AgManager titled “Fixing Arbitrage to Cause Convergence; No Consensus”.  A link to the paper is at:  https://www.agmanager.info/crop-insurance/risk-management-strategies/fixing-arbitrage-cause-convergence-no-consensus

Authors cited the following comments found in the April 16th AgManager paper mentioned above:

“CME has eliminated pit trading in favor of computerized matching of buy-sell orders.  Surprisingly, it is not the oldest futures contract bid that is filled first.  To the surprise of many participants, CME has an algorithm that determines which contracts are filled first.  There was one very upset participant that stated his order was not filled, even though his bid was higher than CME’s posted close.  His question was how was that possible?  Answer, that is how the algorithms work.  Some participants questioned the “equity” and “fairness” of a CME algorithm determined queueing order for filling contracts.”

Unfortunately, the agenda of the April 5-6 meeting did not allow enough time to fully discuss the order or the mechanism by which contracts are filled.  Afterwards the conference and the follow-up April 16th article, it was brought to our attention that the comment by the participant who was quoted above was misleading and caused confusion among some grain futures contract users.  Our objective in this article is to clarify the computer algorithm rules that determine the order used to fill grain futures contracts.  This information taken from CME web-resources and from direct conversations with CME staff.  

Matching algorithms: The CME’s stated purpose for these order-matching algorithms is “to provide the best possible execution of futures contract positions at the fairest price”.  The function of a matching algorithm is to match an aggressor order with a resting order to complete the trade.  Separate bid-ask prices occur and exist for each futures contract throughout the trading period.  As long as the bid-ask prices are different, then both are classified as “resting orders”.  In order for an actual trade to be completed, an aggressor order has to occur in which a new order decides to accept the ask price if she is a buyer, or the bid price if she is selling.  In other words, an aggressor order is required to complete a trade.  Otherwise the bid-ask prices remain apart and there is no trade. 

Anecdotally, this process is like trading pickup trucks.  At some point in time either you or the dealer has to accept the other persons’ offer or there is no trade.  Like futures, the price of that pickup probably changes a lot from your original offer and the dealers’ original asking price.

The Algorithm Used by CME Grain Futures to Determine Order of Contract Fill:

The CME uses what it calls a “K”-type or Split FIFO/Pro-Rata algorithm to determine the ranking by which orders are filled on grain futures contracts.  The CME’s algorithm uses “multiple rounds” of choice rules for determining the sequential order by which orders are to be filled.  These multiple rounds of “allocation order-fill criteria” are described as follows:

Round 1: Top-Order Allocation: To begin the process, a top order allocation is given to the first incoming order that betters the market and is filled at 100% between a minimum of 1 lot and a maximum of 100 lots (note the maximum for KC wheat contracts is 50 lots).  Top-Order Allocations typically make up a small proportion of the total amount of orders filled, i.e., 1% (range of 0% to 5%). 

Top-order allocation is designed to reward a trader that either improves the best bid or the best offer, or rewards a trader who makes an aggressing order that is not completely filled.  For example, if an order was made for 10 contracts, and only 2 contracts were filled, then the remaining 8 contracts will be first in line for the next aggressing order.  However, the remaining unfilled order cannot be changed either in terms of price or the number of contracts involved – otherwise the order goes to the “back of the line” for consideration.  With so many restrictions, it explains why so few contracts meet the criteria of being filled in the Top-Order Allocation.  As a result, these initial Top-Order Allocations typically account for only about 1% of the trades.

Round 2: FIFO Allocation: After a typically small number of Top-Order fills in round #1, then the ranking order of fill is for the next 40% of the orders as determined using FIFO (First In-First Out).  FIFO allocations are based on the timing of when the orders are initially placed, with the first orders placed time-wise being the first orders that are filled. 

Round 3: Pro-Rata Allocation: Then following both rounds #1 & #2, 60% of the remaining volume is allocated via Pro-Rata Allocation, with order size and time being the variables for allocation.  Pro-Rata allocations are calculated by dividing the quantity of contracts bid by an aggressing order by the total quantity of resting orders that are available at a particular price. These “pro-rata” percentages are then used to calculate the amount of allocated orders to be filled by individual resting orders among the total quantity of resting orders available.

In some cases, for outright grain buy/sell positions (excluding grain spread futures in most cases, except apparently for KC HRW Wheat), a small proportion of “aggressing” orders may still remain that need to be filled.  Outright contracts include basic grain futures themselves, but exclude contract-to-contract spreads.  These relatively few remaining orders are filled using a combination of Top-Leveling Allocation and FIFO Allocation methods.  For grain spread futures orders, only FIFO allocations are used in a final “clean up” step. 

Round 4: Top-Leveling Allocation: Any participant that did not receive an allocation via pro-rata allocation due to “percentage rounding” issues, receives a 1-lot allocation.  If there is a volume of orders that remains to be filled.  “Leveling” is an additional round of pro-rata that allocates only one (1) lot per order based on the percent of the remaining resting orders yet to be filled.  

Round 5: FIFO Allocation: Any volume of orders that remain to be filled even after the top-leveling allocation then use a FIFO allocation among the remaining resting orders to complete the procedure.


During the time when pit trading was occurring, the function of “order filling” was carried out by the traders themselves on a face-to-face basis.  Because of most market participants’ preference for electronic trading versus pit trading, the CME has fully adopted electronic trading.  With this change, the function of “order filling” is now determined by computationally-based “allocating algorithms”.  With pit trading, if a trader placed a bid for a position in grain futures that was not filled, it was possible to blame “human behavior” and/or “human error”.

Now with electronic futures being fully adapted by the CME, it is the job of these “allocating algorithms” to fairly and equitably allocate orders among resting positions for grain futures contracts. The authors don’t see any reason to believe this computer allocation of order filling is unfair to farmers – in fact it may be more fair.

In our original paper we included the Lead Market Maker (LMM) allocating algorithm component to be used in the multiple round order filling process before the FIFO and Pro-Rata allocations are calculated.  However, the LMM allocation is not being used by the CME to allocate the order of contract fills in grain futures and option contracts.  LMM has been used for some newly established contracts to help create a market in situations that were plagued with low volume or lightly traded conditions.  However, none of these were grain futures contracts.

Finally, there is a remaining question: how is it possible for one’s buy bid to be higher than the CME closing price and not get contract fill?  The answer is there is a difference between closing price and settlement price.  CME calculates the “posted settlement price” as the volume weighted average price for trade between 1:14 p.m. and 1:15 p.m. of trading each day.  However, the “closing price” is determined to be the price of the last trade at 1:20 p.m. when the market closes

Therefore, if the higher bid was submitted during the 1:15 to 1:20 time frame, it is possible that the bid was above the CME posted settlement price which was calculated from 1:14 to 1:15 earlier that same day.  As result, a higher bid submitted late in the final minutes of trade could exceed the calculated volume weighted average used to figure the “posted settlement price”, resulting with that particular higher priced bid not getting filled.      

Observations from the 2018 Ag Commodity Futures Conf, Overland Park, KS, April 5-6 – No Consensus on Fixing Arbitrage to Cause Grain Price Convergence

The 2018 Agricultural Commodity Futures Conference was held in Overland Park, Kansas on April 5-6, 2018.  This meeting was sponsored by the Commodity Futures Trading Commission and the Center for Risk Management Education and Research in the Kansas State University Department of Agricultural Economics.

The agenda for this conference and an number presentations are available at the following web location:


Following is the first of two articles by KSU Agricultural Economics Art Barnaby and Daniel O’Brien discussing the findings of the conference – with a particular focus on the functions, efficiency, and performance of grain cash and futures markets.  This article is also available at the following web address on the KSU AgManager.info website:


Fixing Arbitrage to Cause Convergence; No Consensus

Prepared by

G. A. (Art) Barnaby, Jr. (barnaby@ksu.edu) , Professor, Dept. of Agricultural Economics

Daniel O’Brien (dobrien@ksu.edu), Extension Agricultural Economist

K-State Research and Extension, Kansas State University, Manhattan, KS 66506

April 16, 2018.


Kansas State University and the Commodity Futures Trading Commission (CFTC) recently held a joint conference on the lack of convergence in grain futures and many other futures trading issues.  Convergence is required for COOPs, grain elevator hedges, farmer hedges and crop insurance claims to work properly.  Without convergence, there is no connection between futures and cash markets, and grain future markets are not likely to survive in the long run without a reliable basis relationship with local cash prices.  Futures are not trading grain; they are trading the value of a shipping certificate that is received by the long when delivery occurs.  Non-convergence occurs when there is no credible threat of delivery.  Shipping certificate receivers have the right to store the grain and pay the storage indefinitely, currently 5 cents/month for corn and soybeans.  They also have the right to pick the date to load the grain out on a train/barge. 

Most grain industry traders don’t favor the Variable Storage Rate (VSR) mechanism now used on Chicago Mercantile Exchange (CME) Wheat futures contracts, and it appears there is little chance that VSR will be applied to corn and soybeans.  Other options for defining storage obligations in the CME wheat futures contracts included: 1) returning to a fixed storage rate; 2) fixed storage at a higher rate; 3) a seasonally adjusted storage rate; 4) a computer model estimated implied market “value of storage” with a committee adjusting the storage rate; 5) expanding the number of entities who can make delivery; and 6) a change to a no-storage futures contract.  Most participants at this conference were opposed to cash settlement and required load out of grain futures. 

Indexed funds, computerized trading, “Spoofing”, livestock contracts, etc. were also covered at this conference, but not included in this summary.  Papers and power point slides from the conference are located at:


Issue #1: CME Algorithm

CME has eliminated pit trading in favor of computerized matching of buy-sell orders.  Surprising, it is not the oldest futures contract bid that is filled first.  To the surprise of many participants, CME has an algorithm that determines which contracts are filled first.  There was one very upset participant that stated his order was not filled, even though his bid was higher than CME’s posted close.  His question was how was that possible?  Answer, that is how the algorithm works[ii].  Some participants questioned the “equity” and “fairness” of a CME algorithm determined queue order for filling contracts.  (See note at end of article on how the CME Algorithm functions)

Issue #2: Variable Storage Rate (VSR) Mechanism for CME Wheat & KS HRW Wheat Futures

As expected the Variable Storage Rate (VSR) generated a lot of discussion. There were a number of grain traders who made it very clear they don’t like VSR.  The argument is VSR leaves the long guessing what the storage cost will be, resulting in reduced liquidity in the deferred contracts. 

Dr. Scott Irwin, University of Illinois, made the case that non-convergence was caused by the futures stated storage rate being set below the market value for storage.  Multi-national grain elevators with delivery rights don’t deliver grain, they deliver a shipping certificate that only they can create.  In addition to delivery, these certificates are sold in a secondary market, but they will sell at a price that is higher or equal to the non-convergence.  If one could buy shipping certificates and gain by arbitraging the futures, then the arbitrage profit would be bid to zero almost immediately.

Dr. Irwin, as the acknowledged primary developer of the VSR, surprised many participants when he didn’t strongly defend it.  He spent most of his presentation talking about non-convergence in the corn market, rather than the wheat market.  He appeared to be more supportive of using the results from a mathematical model’s estimated “market” value of storage, and then a designating committee to determine whether to make any adjustments to the storage rate. If the CME wants to use a different model to adjust the storage rate and the math is made public, then one would expect that to work too.  However, if there is a committee that makes the final decision, then it adds another level of uncertainty; will they act or just go with the status quo?  This committee would likely add a whole new round of controversies about trading futures.

He also suggested that a seasonal storage rate might work for corn.  If one remembers after the first round of non-convergence in KC wheat in the early 2000’s, the exchange added a protein requirement for the first time and a seasonal storage rate.   However, those changes didn’t prevent the most recent round of non-convergence in HRW wheat.  Apparently indicating the higher seasonal rates applied at that time were not sufficient to bring about convergence in the HRW wheat futures contract.

Issue #3: No Storage Grain Futures Contracts

One participant argued for no-storage futures contracts.  Without a storage requirement, it would allow more entities to make delivery and arbitrage futures contract.  Alternatively, CME argues there is only one new crop supply provided each year (two, if you count Brazil) therefore, futures must include storage so that a market mechanism exists to reflect grain prices and grain storage costs.  Those supporting a “no-storage futures contract” counter that clearly someone will store grain, regardless of the futures contract.  They state that there are plenty of farmers who are willing to store grain and most of that grain is unpriced.  They indicate that markets will need to provide a return to storage, even with a no-storage futures contract, but that may require higher deferred prices.

Issue #4: Including Farmer Storage In Delivery of Grain Futures Contracts

Another participant suggested CME should allow farmers to store the grain at the futures storage rate, when delivery elevators don’t want to store grain.  The storage would need to be certified by USDA, utilizing local Farm Service Agency (FSA) offices would likely be certification of choice.  There would also be questions in the case of farm bankruptcies, whether the long still owns the grain the buyer has paid for plus the storage?  For this delivery alternative to be workable, there would need to be rules and procedures developed on how the grain would be moved from farm storage to load out on a train/barge. 

One of the grain merchandizers attending suggested that farmers should have their futures orders filled first.  As explained above, CME’s algorithm determines order that contracts are filled, and that the mechanism used by the CME within that algorithm is not transparent to the public in general or to farmers with futures positions in particular.


There was still no agreement on what the true cash price is for wheat, but at least everyone agreed there was non-convergence in wheat markets.  One participant wanted the protein requirement in the futures contract raised from 10.5% to 11%.  Currently, the Kansas HRW wheat futures contract does require 11% protein, but will accept 10.5% protein with a $0.10 per bushel discount.  It was surprising that many conference participants essentially considered the Kansas HRW wheat futures contract protein requirement to be the discounted 10.5% protein level rather than the 11% par value as stated in the contract.

One key takeaway from this conference was that nearly all of agriculture agrees that convergence is necessary for short hedges and crop insurance to work.  Proposed fixes include VSR, a model determined storage rate with a committee to make the final decision on storage rate changes, fixed storage at a higher rate, a seasonal adjusted fixed storage rate, and no-storage futures contracts.  However, there was no consensus on what if any changes to make to futures to cause convergence.  Among these participants, there was little support for strictly requiring “forced” load-out or cash settlement of grain contracts. They did agree that if there is no connection between futures and cash, then the grain futures are unlikely to survive. 

Lack of convergence also effects crop insurance as tool to cover a farmer’s short hedge.  Crop insurance coverage combined with CME hedging tools will be covered in the next AgManager update.

An Additional Note on How the CME Matching Algorithm Works

A grain trader provided us with the following response on how the CME order matching algorithm works.

“Almost all of the CME ag contracts are matched using tag 1142 (Match Algorithm Value) = “K” (“Algorithm K”). CME generically defines Algorithm K as a “split FIFO/pro-rata algorithm.” However, there are multiple rounds of allocation under Algorithm K:

  • Round 1: Top-Order Allocation: A top order allocation is given to the first incoming order that betters the market and is filled at a 100% between a minimum of 1 lot and a maximum of 100 lots (note the maximum for KC wheat contracts is 50 lots).
  • Round 2: Lead Market Maker Allocation: CME makes various vague statements about there being the possibility of a “lead market maker allocation” after the top order allocation. None of the CME’s published materials confirm whether there is a lead market maker allocation for the ag contracts and, if so, how big is that allocation?
  • Round 3: FIFO Allocation: 40% of the volume after the top-order and LMM allocations is allocated via FIFO.
  • Round 4: Pro-Rata Allocation: 60% of the volume after the top-order and LMM allocations is allocated via pro-rata, with order size and time being the variables for allocation.
  • Round 5: Top-Leveling Allocation: Any participant that did not receive an allocation via pro-rata allocation receives a 1-lot allocation, if volume remains.
  • Round 6: FIFO Allocation: Any volume remaining after top-leveling allocation is allocated via FIFO.”


2018 Soybean Market Situation & Outlook – Salina, KS on January 23, 2018

Following are the slides from a presentation on “Soybean Market Outlook in 2018” presented to 150 people at a “Kansas Soybean School” in Salina, Kansas held on January 23, 2018.   The workshop was sponsored by the Kansas Soybean Commission (http://kansassoybeans.org/) and K-State Research and Extension.

Following are the slides and key points presented by Extension Agricultural Economist Daniel O’Brien of the Department of Agricultural Economics at Kansas State University titled “Soybean Market Outlook in 2018“.  This presentation is available on the KSU AgManager.info website (http://www.agmanager.info/) at the following web address:




Corn and Grain Sorghum Market Situation & Outlook – Amarillo, Texas on January 24, 2018

Following are the slides from a presentation on “Feedgrain Market Outlook in 2018” presented by teleconference to a “Feedgrain Marketing Plan Workshop” in Amarillo, Texas held on January 23-24, 2018.   The workshop was sponsored by Texas Agri-Life Extension.

Following are the slides and key points presented by Extension Agricultural Economist Daniel O’Brien of the Department of Agricultural Economics at Kansas State University titled on “Feedgrain Market Outlook in 2018”.  This presentation will also be available on the KSU AgManager.info website (http://www.agmanager.info/) at the following web address:




KSU Article on “What Caused Wheat Basis to Widen by a Dollar?” on AgManager.info

What Caused the HRW Wheat Basis to Widen by a Dollar?

Kansas State University Extension Agricultural Economist Daniel O’Brien, Elizabeth Yeager, and Art Barnaby met with several Kansas grain industry participants including farm cooperative grain elevators, independent stock-held grain elevators, flour millers, a House of Representative staffer, a commodity broker, representatives of U.S. Wheat Associates and the Kansas Wheat Growers Association, and the Chicago Mercantile Exchange (CME) at various locations around the state during April 10-12, 2017 to discuss current Hard Red Winter (HRW) wheat marketing issues.  Our meeting tour included both non-delivery and delivery elevators, and our primary question was why non-convergence was occurring between CME Kansas HRW wheat futures and local cash wheat prices.  However, many other topics were covered by this group of professionals with different interests in the wheat market.  At the link below is a summary of the information provided by these various industry professionals.  Thanks to each of them for sharing their time.

Read more at: http://www.agmanager.info/crop-insurance/risk-management-strategies/what-caused-hrw-wheat-basis-widen-dollar

Following are key points from the  complete article.

What Caused the HRW Wheat Basis to Widen by a Dollar?

Point #1) Grain Storage Rates as a function of Supply-Demand

Straight from “Econ 101:” – when something is in short supply (storage), the price increases and rations the available supply.  The storage rate in the HRW futures contract is fixed and is below its real market value at this time. Therefore, the only adjustment to be made in this situation is a widening basis in the futures contract to compensate.  It was argued that allowing the storage rate to increase to reflect the true market value of storage would then allow the basis to adjust, and subsequently cause futures and cash prices to converge.

Point #2) Raising Fixed Storage Rates on Delivered Wheat vs VSR Adoption

The CME considered two primary options that would allow the storage rate in the CME Kansas HRW wheat futures contract to reach market value: a)  an increased fixed storage rate, and b)  a Variable Storage Rate (VSR)

Point #3) VSR Adoption by the CME & Associated Concerns

On April 24, 2017, the CME announced that the Variable Rate Storage (VSR) would be applied to the HRW wheat futures contracts, effective Sunday, March18, 2018. The CME-announced change occurred after our return, but it was clear during our tour that the VSR would be a controversial change.  It was the perception of some participants in these discussions that adoption of a VSR mechanism would add uncertainty to long-term hedgers of Kansas HRW wheat futures.

They were concerned that the VSR mechanism had the potential for increasing the hedging uncertainty for bakers and others who use wheat futures to hedge food production process input price risk.  Under the VSR, these long hedgers have a new risk of a storage rate change without a limit on the increase.  They preferred a fixed rate that provided certainty in the storage cost.  They argued that under an “increased fixed storage rate” scenario, the carry in the futures market would allow an increase in the storage rate to reflect the market value of storage during periods of large inventories.  An increased fixed storage rate would allow for faster storage adjustments than the VSR.

Point #4) Separation of VSR and Storage Rates at Local Elevators

Any adjustments made to the storage rate in the HRW wheat futures contract are unlikely to affect the farmer-paid storage rates at their local country elevator.  Increasing country elevator storage rates will increase the incentive for farmers to build their own on-farm storage.  One could even argue that these country and terminal elevators have kept the storage rate artificially low for both long-term economic and customer relation reasons, causing farmers and competing elevators to under invest in storage.  The idea is that once farmers build their own on-farm storage, they are not likely to return to their local country elevator to store grain, but rather use their own facilities. Many of those elevators would then be left with open storage space earning no return in the future when crops are more normal in size.

Point #5) Determining the Cash Price where Cash-Futures Convergence Occurs

One non-delivery elevator manager challenged the argument there was convergence for 11% protein wheat in KC on a rail car.  He stated that if that were a real cash offer, he would ship them a train load of wheat by the end of the week.  We are not sure if the argument matters, because delivery would take place with the greatest market advantage for the delivery elevator and most of the delivered wheat was in Salina.  From the viewpoint of this manager, he had limited access to the KC rail grain market.  With limited access, there would be no way for arbitrage and/or market participation to occur.  Some even question if KC should even be a delivery point because wheat no longer flows through KC, as most HRW wheat goes from terminal elevators to the Gulf or to millers predominantly located in central Kansas.  Why would one expect wheat shipped from Hutchinson, KS or Enid, OK to go to KC before going to the Gulf?

Point #6) Wheat Protein Issues

The issue of how high-protein wheat was handled in the Kansas grain elevator system was discussed, and the degree to which higher proteins were paid for in the Kansas wheat handling and marketing system. What these elevators really pay on is the average protein for the crop, so if one is harvesting wheat in an area with higher protein, then the bid is higher.  However, in the Kansas wheat market with its predominantly bulk blending practices, farmers are paid based on the average protein for the crop.  Therefore, the farmer with 13% protein gets the same price as a farmer with 10% protein, unless they store wheat on-farm in a segregated manner for later sale and capture the protein premium.  We were also told that because of intense harvest pressures, Kansas grain elevators don’t have the time to separate the wheat crop by protein during harvest.

Point #7) Wheat Genetics Impact on Protein & Regional Market Differences

One manager was of the opinion that the KSU wheat breeding program focused too much on yield and not enough on wheat milling quality and higher protein levels.  However, in the current Kansas grain handling system, there are only limited price signals sent through to farmers for high quality wheat under the current marketing system.  This is because farmers are paid predominantly on crop size or “bushels” only.  Price premiums are “implicit” in the price paid.  Higher wheat prices are paid for regions of the state where protein is higher and lower prices for poorer protein regions within any one year.  However, if there are any protein premiums being factored into local wheat prices they are not generally visible to the farmer.

Point #8) Tie-in Between Onfarm Storage & Marketing High Protein HRW Wheat

The general conclusion of these discussions was that farmers who can consistently produce high-quality, high-protein wheat in the Southern Plains region would need to have their own storage facilities to capture any premiums, given the current bulk handling system that exists.  The question is whether they can consistently produce such high protein wheat in order to gain the price premiums. In addition, farmers who want to capture basis improvement will need to own the physical wheat, either in their own storage or in commercial storage.  However, under current conditions, many experts are expecting it will likely require a couple of years before HRW wheat futures and cash converge.  It is unlikely many farmers can afford to carry grain inventory for two years.  In addition, most Kansas wheat producers would need to make greater use of post-harvest storage hedges and/or forward contracts, to regularly capture market carry.

Point #9) Rail Cost Differences by Type of Grain

Perhaps the most revealing finding of these meetings was the amount of the differential in freight rates for different types of grain.  For example, the Burlington Northern and Santa Fe railway (BNSF) charges a higher rate for wheat than grain sorghum for a unit train going from the same location and with the same total freight weight to the Gulf.  The bottom line, the railroad charges what the market will bear.  Wheat has to go to the Gulf, while grain sorghum can be consumed as a feed grain within trucking distance.  Those higher freight rates are then passed back to the wheat farmer in the form of lower cash wheat prices.  Any legislation or regulations that favor truck traffic for longer hauls of grain would provide more competition to railroads in grain markets.  However, longer hauls of grain are likely to continue to favor rail transportation, given the scale of the economies involved.

Point #10) Non-convergence Impact on Crop Revenue Insurance Coverage

It is true that when there is no convergence in futures and cash, the crop revenue insurance contract pays less for a claim when prices fall.  Some farmers have argued that crop insurance claims should be paid based on cash prices.  The problem is: what cash price to use in the calculations?  The Agriculture Risk Coverage (ARC) program settles claims based on USDA’s national average cash price, but that means farmers must wait a year or more for payments.  More importantly, when there is a crop failure and prices increase, then farmers are paid for indemnity bushels only after the deductible measured in bushels is applied.  Farmers will have those indemnity bushels replaced at the futures price.

However if claims were based on cash prices, western Kansas wheat farmers would have their indemnity compensated at a price that would be 40 to 50 cents lower than the current method.  When there is a short crop and the wheat prices increase, most farmers would need to lose at least 25% of their expected bushels before collecting any payments, so it is not a good time to have one’s indemnity payments cut by a change in the price calculation.

Point #11) Other Topics Discussed

There was also extensive discussion of other issues such as:

  1. whether the use of shipping certificates would be advantageous for the Kansas wheat contract;
  2. if some form of rail or track delivery on either an individual rail car or a 110 car train basis were feasible;
  3. the tradeoffs between carrying charges and basis levels in Kansas wheat price determination;
  4. the pattern of grain storage utilization in Kansas and the U.S. grain system, and how growth in inventories has contributed to the current “wide basis” situation in wheat;
  5. whether inclusion of a cooperative elevator among designated delivery facilities would impact price convergence; and
  6. the important role of Gulf wheat export prices in cash wheat price determination in Kansas after transportation adjustments.

In addition, the pattern of increasing rail rates to the Gulf over time and its impact on Kansas wheat basis levels was also examined.

Point #12) Inability of Farmers to Deliver Against CME KS HRW Wheat Futures

It was clear from our discussion that farmers have no right to deliver wheat (any grain) on a futures contract.  Therefore, farmers should not enter the delivery period holding a short future’s position thinking they have delivery rights.  In addition, it was argued that the change to VSR would be of the greatest benefit to farmers who already have their own on-farm storage.  However, at least one person suggested that farmers may over-invest in storage and eliminate farm storage returns in the future.

Final Thoughts: The Need For “Balance” in Grain Futures Deliver Mechanisms

These discussions were of great benefit to those of us from Kansas State University, and provided us a practical, industry level perspective, a viewpoint that is often missing from more “esoteric” academic theory-oriented viewpoints about how markets function.

If a market delivery system is “unbalanced” between the “short” sellers who at times may seek to make delivery of grain, and the “long” buyers who may be forced to take those same deliveries, it hurts the longterm viability and usefulness of the futures contract. In this case the disadvantaged side of these transactions will likely act to limit their risk exposure – possibly by just not participating in trading the futures contract at all.  Consequently, for the sake of market liquidity (i.e., maintaining a healthy pool of both sellers and buyers) and effective futures contract function, such grain futures market delivery mechanisms need to be “fair” to both sides of the transaction.

If the settlement and/or delivery mechanism for an agricultural futures contract such as CME Kansas HRW Wheat futures is not thought to be “fair” by one side of the transaction or the other, then either “shorts” or “longs” may choose not to use the contract at all.  Then if trading volume of the futures contract decreases as traders take their business elsewhere, the effectiveness and usefulness of the CME Kansas HRW Wheat futures contract as a price discovery and risk management tool would drastically decline.