Evaluating Risk and Return of Corn and Soybean Marketing Strategies Using Monte Carlo Simulation Methods

This study evaluates the performance of pre-harvest marketing strategies for corn and soybeans, using Monte Carlo simulation techniques. Given the increasing volatility in commodity prices and the evolving landscape of agricultural markets, producers face growing challenges in developing effective marketing plans that manage risk and enhance profitability. This thesis examines thirteen marketing strategies from 2008 to 2024, including benchmark harvest-only sales and various pre-harvest futures contract approaches. Historical futures price data for December corn and November soybean contracts were used to simulate 1,000 marketing outcomes per strategy per year, capturing a broad range of market conditions. Key performance indicators such as mean price, standard deviation, winners-losers test, and rank analysis were applied to assess the effectiveness of strategies. Additionally, paired t-tests and Levene’s tests evaluated statistical differences in mean returns and price volatility. Probability-based and winner-loser transition tests were also employed to explore the likelihood of underperformance and the persistence of strategy success. Results indicate that while average prices across years were not statistically different over the full sample period, specific strategies, particularly those targeting seasonal price peaks, achieved consistently higher returns in some years. However, these higher returns often came with increased volatility. The probability tests further highlighted that those strategies distributing sales across multiple months or crop years tended to outperform the harvest-only benchmark more consistently in individual years.