Basis is defined as the difference between a cash price offered for a commodity at a specific location and a futures contract price for that commodity:

Basis = (Local Spot Price - Futures Price)

This difference occurs because futures markets, which capture global conditions and expectations, do not fully reflect the conditions in any particular local market. These discrepancies may be due to local variability in a commodity's quality level, local demand and supply, and transportation modes, among numerous other factors. If none of these factors affected prices in a local market, then the basis would be zero, because the local price would be the same as the futures price.

Basis is a combination of local and futures market information and is a much more stable indicator of relative market conditions than the local spot or futures contract price alone. For example, while spot or futures prices may change drastically between years, basis is subject to much smaller movements. As such, basis can be a better indicator of how favorable or unfavorable current markets are relative to historical average. If current basis is stronger (less negative; more positive) than a historical basis level, current markets are more favorable for selling grain and less favorable for procuring grain. Conversely, weaker basis (more negative; less positive) is suggestive of more favorable conditions for buyers and less favorable conditions for sellers.

Basis is a highly specific measure for a particular crop, particular quality level (for wheat, this is typically protein content), particular location, and particular time period. Using basis information that does not specifically describe the details of your particular market and crop can lead to incorrect marketing insights.

Basis levels are most useful when they are used to examine current market conditions relative to historical marketing conditions. If current basis is stronger (less negative; more positive) than a historical basis level, current markets are more favorable for selling grain and less favorable for procuring grain. Conversely, weaker basis (more negative; less positive) is suggestive of more favorable conditions for buyers and less favorable conditions for sellers.

You can download historical basis information using the Historical Basis Data tool. You can also create visual comparisons of current and historical basis by using the Basis Charts tool.

Basis can be calculated using prices of futures contracts that expire at different time periods. The most commonly reported basis value is the "nearby basis," which is calculated as

Nearby Basis = (Local Spot Price - Nearby Futures Price)

The nearby futures price refers to the price of the futures contract that is closest to its expiration month. For example, in the Kansas City (KCBT) hard red winter wheat futures market, there is no futures contract that expires in January. The closest (or nearby) contract is the March futures, which expires in March. Therefore, the nearby basis may be a more informational tool for understanding current marketing conditions.

The "harvest-period basis" is calculated as

Harvest Basis = (Local Spot Price - Harvest Month Futures Price)

The harvest month futures price refers to the price of the futures contract for which the expiration date is closest to the month during which a crop is harvested. For hard red winter wheat, this is the July Kansas City (KCBT) HRW futures contract. For hard red spring wheat, this is the September Minneapolis (MGEX) HRS futures contract. Therefore, the harvest period basis may be a more useful informational tool for understanding what market conditions are currently being anticipated at the time that producers are harvesting and delivering a large proportion of their crop.

Basis is a highly specific measure for a particular crop, particular quality level (for wheat, this is typically protein content), particular location, and particular time period. Because producers grow and grain elevators procure wheat with different protein levels, different prices are offered for wheat with different protein levels. As such, just as separate markets (and prices) exist for different wheat classes (e.g., hard red spring wheat class, hard red winter wheat class), so do separate markets, prices, and basis exist for wheat with different protein levels.

Montana's wheat marketing landscape is somewhat unique to many other major wheat-producing states because producers grow and grain elevators procure wheat with different protein levels. Therefore, knowing whether markets are offering / will offer a higher or lower premium for higher-protein wheat can aid in developing marketing strategies. One measure that can provide this information is the price spread (i.e., difference in prices) between the Minneapolis Grain Exchange (MGEX) hard red spring wheat futures contract and the Kansas City (KCBT) hard red winter wheat futures contract.

The MGEX-KCBT price spread provides information about how much more (if the spread is wider) or less (if the spread is narrower) markets value higher protein wheat relative to lower protein wheat. If the MGEX-KCBT spread is wider, then this suggests that elevators are likely to value (and offer higher premiums) for higher-protein level wheat. Conversely, a narrower MGEX- KCBT spread suggests that markets are less likely to offer high protein level premiums.

The nearby MGEX-KCBT spread provides insights about how markets are currently valuing higher protein wheat. The harvest-period MGEX-KCBT spread provides insights about how markets are valuing new crop higher protein wheat.

Volatility describes the stability of a measure over time. Larger basis volatility (i.e., large increases and decreases of basis within a period of time) can indicate uncertainty in the market about the pricing of a particular product. Lower basis volatility (i.e., small increases and decreases of basis within a period of time) can indicate a relatively stable market in which both the buyers and sellers of grain agree on the valuation of a specific product.

In general, volatility can provide important information about market stability and help make marketing decisions. You can download historical basis volatility information using the Historical Basis Data tool. You can also create visual comparisons of current and historical basis volatility by using the Basis Charts tool.

There has been an extensive literature in agricultural economics related to developing commodity price forecasting models. The consensus is that a model that can first predict basis (and then use this prediction to forecast prices) leads to much better price predictions.

Consider the following: If someone asked you to predict the price of wheat in your local market three months from today, what would be your response? One reasonable strategy might be to determine the local price last year or take an average of several previous years. However, you would be using only information about what already occurred, without incorporating expectations of what will occur in three months. A second approach could be to look at the price of a futures contract expiring three months from today and assume that to be the most likely price. However, although the futures price accounts for local, regional, national, and international expectations about future wheat prices, directly using futures prices to characterize local market conditions and prices will almost always lead to errors. Both approaches, therefore, are likely to contain inaccuracies.

The solution: using the combination of basis (historical information) and futures prices (rational expectations). Historically, basis tends to be more stable than either the local or futures prices alone. This is because the volatility in prices caused by market fundamentals tends to affect both local and futures prices in the same direction. That is, when futures prices rise, local prices generally also increase. Similarly, decreases in local prices are associated with lower futures prices. Therefore, the difference between the local and futures prices, basis, is not likely to change by the same magnitude as prices themselves. The stability of basis over time makes it useful for predicting local cash prices for a given commodity and point in time.

While the Montana Wheat Basis Database does not directly predict basis or price levels, you can use historical basis information to understand historical market conditions for specific products at certain locations and time periods. You can then combine that information with futures contract prices to obtain additional insights about potential future market conditions.

The Montana Wheat Basis Database provides several historical basis and volatility tools to help users gauge various market conditions. The following measures are provided:

Nearby and harvest period 3-year average basis: for each day, the database provides the average of the basis that occurred on that day in the preceding three years. By comparing the current basis to the three-year average, users can evaluate whether current markets are stronger or weaker than in recent history.

Nearby and harvest period 1-week average basis: for each day, the database provides the average of the basis that occurred in the preceding business week. By comparing the current basis to the one-week average, users can evaluate whether current markets are becoming (trending) stronger or weaker than in the very recent past.

Nearby and harvest period 1-week volatility: for each day, the database provides the coefficient of variation (CV) measure for basis that occurred in the preceding business week. The CV is calculated as the ratio of the standard deviation of basis to the basis average. High values indicate a more volatile market, while lower values indicate a more stable market. By evaluating the one-week CV measure, users can evaluate the short-run stability of local wheat markets.

Nearby and harvest period 1-month volatility: for each day, the database provides the coefficient of variation (CV) measure for basis that occurred in the preceding month. The CV is calculated as the ratio of the standard deviation of basis to the basis average. High values indicate a more volatile market, while lower values indicate a more stable market. By evaluating the one-month CV measure, users can evaluate the short-to- medium run stability of local wheat markets.

Nearby and harvest period 6-month volatility: for each day, the database provides the coefficient of variation (CV) measure for basis that occurred in the preceding six months. The CV is calculated as the ratio of the standard deviation of basis to the basis average. High values indicate a more volatile market, while lower values indicate a more stable market. By evaluating the six-month CV measure, users can evaluate the medium-run stability of local wheat markets.

Nearby and harvest period 1-year volatility: for each day, the database provides the coefficient of variation (CV) measure for basis that occurred in the preceding year. The CV is calculated as the ratio of the standard deviation of basis to the basis average. High values indicate a more volatile market, while lower values indicate a more stable market. By evaluating the one-year CV measure, users can evaluate the medium-to-long run stability of local wheat markets.

MSU Extension, the MSU Department of Agricultural Economics and Economics, and the Montana Wheat and Barley Committee provide several other tools that may be useful for understanding and following the dynamics in Montana's wheat markets.

Montana Ag Prices tool: this website allows users to compare current and historical price reports from the USDA Agricultural Marketing Service for several types of wheat, hay, and cattle at several locations across Montana. Select the location and other details from the dropdown menus to add to the chart. Adjust the date range to look back as far as 2005.

Wheat Basis Forecasting tool: this tool combines current and historical market information in a statistical model of historical basis levels to provide forecasts for over 70 elevators in Montana and Washington. The tool allows users to predict basis for hard red winter and spring wheat classes and across a number of protein levels.

AgEconMT project: This website is maintained by faculty and extension specialists in the Department of Agricultural Economics and Economics at Montana State University. The primary objective is to provide timely and relevant information that will enable agricultural producers in the northern Great Plains to better understand the economic issues that affect their businesses and communities. Weekly blog posts and podcasts provide a consistent source of objective analyses and discussions; review and describe current research in the department; summarize findings and implications of other relevant studies and reports; and announce upcoming events, educational tools, and other resources.

Montana Wheat and Barley Committee: this website provides relevant information about Montana wheat and barley markets, including reports and data on pricing at major wheat export locations and Montana's grain handling infrastructure. Specific information about transportation capacities, grain elevator locations and contact information, rail transportation rates, and Montana grain movement reports is provided.


Kate Binzen Fuller
Assistant Professor/Extension Specialist
Agricultural Economics and Economics
P.O. Box 172800
Bozeman, MT 59717-2920
Phone: 406-994-5603
E-mail Kate

 

Anton Bekkerman
Associate Professor
Agricultural Economics and Economics
P.O. Box 172920
Bozeman, MT 59717-2920
Phone: 406-994-3032
E-mail Anton