In the agricultural commodity market, transforming all of the pre-season and in-season supply and demand forces that drive a commodity’s price into an actionable daily ending stocks number is the ultimate goal.
The reason for this is simple: a daily ending stocks number for a commodity provides food and beverage firms, shipping companies, governments, and anyone trying to forecast a commodity’s price trajectory the information that they need to more successfully plan and manage their input, supply chain, and logistical risks.
With this in mind, Gro's Global Balance Sheet application for soy, corn, and wheat, three of the most important global food staples and animal feed crops from Brazil, US, Argentina, and China.
Customers use this application to
Why It Matters
Building a balance sheet model is a complex and time-consuming undertaking, due to the sheer number of inputs required.
For example, our Gro Soybean Balance Sheet includes yield, crush, and export forecast models for Brazil, Argentina, and the US and an import forecast model for China, as well as Stocks-to-use Vs Price inputs. With this information, users can ascertain pre-season and in-season ending stocks.
Because of the complexity associated with building global balance sheets, market participants often rely on government estimates that come out sporadically and that sometimes only measure a commodity’s total supply, rather than its available supply.
But a global balance sheet application that combines the balance sheets of a given commodity’s largest exporters and that forecasts that commodity’s supply availability each day, offers market participants a more complete picture of the dynamics driving a commodity’s prices.
Using this directional stock-to-use information, market participants can make more informed, data-driven purchasing and risk management decisions 6-12 months out.
Gro’s Global Balance Sheets use our Supply and Demand Model frameworks to produce an objective assessment of available supplies and more accurate price signals.
For supply inputs, users can rely on Gro’s proprietary yield estimates, or they can create and add their own estimates using Gro’s suite of growing conditions, crop yield, and climate products. On the demand side, Gro predicts the demand for processing, feed, and/or export demand using historical data.
All of these production and demand variables feed into our daily ending stocks estimate. During the growing season, our daily yield estimates are also added to our daily estimate.