Optimal dynamic marketing strategies for grain producers: A case study of winter wheat

The purpose of this research is to determine and examine optimal grain marketing decisions utilizing a stochastic dynamic programming framework. Consideration is given to the linkages, stochastic nature, and dynamics of income taxes, current grain market conditions, the financial condition of the firm, marketing constraints of the producer and government programs. The stochastic dynamic programming model's objective function is to maximize the expected value of after-tax wealth. State variables considered in the analysis are (1) cash grain price, (2) basis level (futures minus cash), (3) before-tax income level, (4) grain storage level, (5) futures position, and (6) value associated with any futures position. The quantity of cash grain sales and futures transactions to occur each month are the decision variables considered in this analysis.Analyzing optimal grain marketing decisions for a dry-land Montana winter wheat producer indicate that optimal hedging levels increase as the basis level and before-tax income level of the firm increases. Conversely, optimal cash grain sale levels increase as the basis and before-tax income level of the firm decreases. The incentive for cash grain sales increases (decreases) as the end of the tax year approaches for low (high) before-tax income levels, due to the progressive marginal tax structure.Sensitivity of optimal grain marketing decisions to changes in actual production and production cost is analyzed. Results indicate that optimal hedging levels decline when actual production is less than that anticipated, and increased production cost levels increase cash grain sales and decrease hedging levels. Although changes in these parameters produce somewhat different results, they are relatively less important than the level of cash price, basis, before-tax income, and grain storage level in determining optimal grain marketing decisions.Optimal grain marketing decisions from the stochastic dynamic programming model are compared to a traditional minimum variance hedging ratio framework. The stochastic dynamic programming framework accumulated the most profits for the firm and also produced the lowest variability in annual after-tax income.U of I OnlyETDs are only available to UIUC Users without author permissio