 Chapter XVI Modelling and Trading the Soybean-Oil Crush Spread with Recurrent and Higher Order Networks: A Comparative Analysis Christian L. Dunis CIBEF, and Liverpool John Moores University, UK Jason Laws CIBEF, and Liverpool John Moores University, UK Ben Evans CIBEF, and Dresdner-Kleinwort-Wasserstein in Frankfurt, Germany Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited. AbstrAct This chapter investigates the soybean-oil “crush” spread, that is the proft margin gained by process- ing soybeans into soyoil. Soybeans form a large proportion (over 1/5 th ) of the agricultural output of US farmers and the proft margins gained will therefore have a wide impact on the US economy in general. The chapter uses a number of techniques to forecast and trade the soybean crush spread. A traditional regression analysis is used as a benchmark against more sophisticated models such as a MultiLayer Perceptron (MLP), Recurrent Neural Networks and Higher Order Neural Networks. These are then used to trade the spread, the implementation of a number of fltering techniques as used in the literature are utilised to further refne the trading statistics of the models. The results show that the best model before transactions costs both in- and out-of-sample is the Recurrent Network generating a superior risk ad- justed return to all other models investigated. However in the case of most of the models investigated the cost of trading the spread all but eliminates any proft potential.