Nonlinear Modeling Using Neural Networks for Trading the Soybean Complex

Recently, there has been a spike in the prices and popularity of commodities. On a macroeconomic level, developing countries are increasing production; while on a microeconomic level, speculative traders are becoming more involved in the market. Agricultural products have a diverse array of factors that can affect the price (i.e. political, government, population, weather, supply and demand). Commodity prices can suffer from extreme volatility in the short term, changing as much as 50% in one year. This research uses the soybean crush spread as a model. The soybean complex adds an interesting component as the underlying soybean product can be crushed into soymeal and soy oil. All three products (soybeans, soymeal, and soy oil) currently have contracts on the Chicago Mercantile Exchange. The crush margin represents the profit margin a processor will receive from crushing the soybeans into the underlying products (soymeal and soy oil). This research adds to the literature of agricultural price forecasting models, using artificial intelligence and nonlinear modeling. The performance of different neural network architectures and inputs to discover desirable returns for both speculative trading and hedging are investigated.