With price risk increasingly at the forefront of agribusiness operations, it is important that these businesses are well equipped to make better decisions on grain price behavior. Historically, producers and agribusinesses have relied on Market Advisory Services (MAS) to assist with price risk management information and price maximization. In the study of producer attitudes, “Information Sources and Risk Attitudes of Large Scale Farmers, Farm Managers,and Agricultural Bankers,” Patrick and Ullerich found that of seventeen different risk management information sources, only farm records were considered to be more useful than MAS.
Interestingly enough, our expanded definition of price risk management seems to be implicitly adopted by the producer community, as MAS are more often used to receive an above average price than to reduce price fluctuations.
Also of note is that on a historical basis, returns from MAS recommendations have been shown to have an outsized impact for producers managing larger operations due to economies of scale.
With this focus on MAS decision support being directed at revenue maximization, empirically demonstrated benefits to scale, and a clear trend of continued increases in the size of an average farming operation, building a replicable decision framework supported by high-quality data inputs has never been more important.
In the sections that follow, we present users with recommendations on how to minimize grain price risk and identify revenue opportunities using tools provided by cmdty. These techniques can also be employed by MAS, Ag Lenders, Grain Processors, Trading Houses, Food Companies, and Grain Marketers.
John Ferris’ “Developing Marketing Strategies and Keeping Records on Corn, Soybeans, and Wheat,” presents users with a simple framework for employing grain marketing strategies based on price expectations across futures and basis. Understanding this framework and being able to effectively deploy each of these contracts is critical to successful strategy implementation.
At the highest level, the type of contract or trading strategy employed by market participants will vary depending on anticipated market conditions.
For example, basis may be particularly strong for this time of year based on seasonal patterns. If a user has the expectation that basis will revert back to historical levels they may want to consider entering a basis contract, selling the cash and buying futures, or selling the grain and buying call options.
As shown in the image above there are essentially four different pricing expectation scenarios that will dictate what strategies a rational producer would want to employ.
Desired outcomes from the grain buyer perspective would largely be the inverse of these strategies in a profit maximization model.
Using the previous example, if spot basis was strong relative to prior seasonality, and the local grain buyer had the expectation that this will revert back to historical levels they would be far less enthused about entering into the same basis contract that was appealing for producers.
That said, a buyer with access to storage facilities and established relationships with downstream buyers has options even when market forces dictate they bid strongly in local markets. While the domestic grain value chain is essentially a zero-sum game, users with access to quality data and analytical capabilities should find that they can 1) competitively buy local grain at an offset to fair value, and 2) identify downstream grain buyers that will allow the local buyer to lock in profit.
How to Profit from Basis Expectations
In Figure 9 below we explore real-life examples using cmdty Corn Basis Indexes to identify opportunities to earn positive carry through relative value positioning in geographically adjacent regions. Each cell represents the spread between current basis values and historical basis for the same region – where the more positive or negative the value, the more out of line with historical pricing the value is.
Cells highlighted in blue indicate an Abnormally Strong Basis (ASB), while orange cells indicate an Abnormally Weak Basis (AWB). An agribusiness with a broad network of grain buying locations can use this information to opportunistically source grain or make better decisions around storage and preferred contract types.
Drilling down further into how a producer can use cmdty Grain Basis Indexes to make better decisions around selling, storing, or transporting their grain, we explore the ASB in Iowa CRD 10 for March 2019 - which was identified in Figure 9 on the preceding page.
With a spread of $0.0.923 between seasonally adjusted Mar-19 and Dec-19 basis values, the entire Iowa CRD 10 is exhibiting strong demand for spot grain vs. historical patterns. Clay County in particular has a strong bid in front-month basis at nearly an $0.13 premium to historical patterns. Producers in or around Clay County – or many other countries easily identified using cmdty Grain indexes - can use this type of information to identify grain buying locations that are paying prices well above historical trends.
Figure 11 highlights actual locations where such bids were available to producers to not only sell above seasonal targets, but to optimize grain marketing strategy relative to transportation costs, on farm storage, and contract types. Depending on a producer’s pricing expectations, they might elect to sell spot outright; or sell their position and enter a corresponding long futures position for a portion of the transaction.
Opportunities to leverage this information scale in non-linear proportion to the user’s geographic coverage and diversity of client base. Large Grain Buyers and Market Advisory Services in particular can dynamically position their portfolios and client recommendations to take advantage of discrepancies in pricing across their distribution networks.
Where an individual producer may be able to identify delivery windows where basis pricing is rich relative to history – providing them with confidence in their grain marketing program – they are still limited in the number of prices that are accessible due to transportation costs, and their default long positioning.
Large grain buyers on the other hand have a broad network of locations with unique local economics built into area-specific prices. MAS have similar diversity in exposure to localized conditions as their coverage reflects their aggregate customer portfolio. This breadth of coverage allows both of these parties to more effectively take advantage of relative value opportunities across their portfolio.
Figure 12 provides an example of how this can be employed across a firm with a broad portfolio of localized pricing exposure. By having a clear view of currently available pricing across the United States, and easy-to-use information on historical fair values for grain, diversified participants can opportunistically enter positions where “theoretical credit” is high, and passively exit positions by adjusting their bids/offers to reflect their desired net long or short position. Such relative value trades can be employed across geographies and delivery windows providing diversified participants with unique profit opportunities.
In addition to providing actionable insight for grain buyers and MAS users through fair value forward curves, the cmdty Grain Index Series is also useful in building predictive models for grain basis. In our study, we attempted to predict the cmdty Illinois Corn Basis Index at 66 business days – or approximately 3 working months – ahead.
Data was used from the first 29 weeks of 2016, 2017, and 2018 to train the regression model. Pricing information from weeks 30 to 34 were then used to predict the cmdty Illinois Corn Basis (Dec 2018) Index 66 days ahead, from week 43 to week 47. The predictions, made at 2018 Aug, represent the basis that we expect to see at 2018 Nov for Dec delivery corn.
Mean error of the model was 0.008, with standard error of 0.00365, and a maximum error of 0.01345 –which is equivalent to an average difference between predicted and actual basis at Nov 18 for Dec 18 delivery of 0.7 cents per bushel, and a max difference of 1.34 cents per bushel. This result suggests that there is sufficient evidence to support using cmdty Corn Basis Indexes to predict basis movements at the state level. Illinois was chosen due to the large quantity of corn produced within the state, and certainly other states can and should be considered for further analysis.
While done at the state level – similar models can be deployed from the county-level up to national-level by adjusting the input dataset.
cmdty Grain Index Family
The cmdty Grain Index Family is a series of continuously calculated volume weighted Basis indexes and Cash Price assessments that represent fair values of physical grain. Available historically and as a twelve-month forward curve for each calculated area, fair values are calculated for Corn, Soybeans, and Wheat.
Consideration with regards to facility capacity, utilization, and throughput is given to more accurately provide a market clearing price for a given geographical region. For each commodity product within the cmdty Grain Index Family thousands of indexes are calculated at the following levels: County, Crop Reporting District, State, Region, and National.
Over 40,000 indexes are calculated each day between Basis and Price, with rolling instruments, forward curves, and rich metadata available for each grain instrument in our ecosystem.
As the leader in workflow solutions for agriculture, cmdty by Barchart has unparalleled insight into physical grain markets and best-in-class cash grain prices from over 4,000 grain buying locations across North America.
Our solutions power single location elevators, through to the largest grain buyers around the world. Users can access the cmdty Grain Index Family through Barchart OnDemand APIs, the flagship cmdtyView® data platform, or the powerful cmdtyView Excel add-in. To learn more about how cmdty can help you earn more from your grain drop us a line at firstname.lastname@example.org.