Corn cash-futures basis forecasting via neural networks

Abstract

Cash-futures basis forecasting represents a vital concern for various market participants in the agricultural sector, which has been rarely explored due to limitations on data and traditional econometric methods. The current study explores usefulness of the nonlinear autoregressive neural network technique for the forecasting problem in a unique and proprietary data set of daily corn cash-futures basis across nearly five-hundred cash markets from sixteen most important harvest states in the United States over a 5-year period. Through investigations of various model settings across the hidden neuron, delay, data splitting ratio, and algorithm, a chosen model with five delays and twenty hidden neurons is reached, trained using the Levenberg–Marquardt algorithm and data splitting ratio of 70% vs. 15% vs. 15% for training, validation, and testing. This model results in accurate and stable performance across the cash markets explored, which illustrates usefulness of the machine learning technique for corn cash-futures basis forecasting. Particularly, the model leads to average relative root mean square errors (RRMSEs) of 9.97%, 8.51%, and 9.64% for the training, validation, and testing phases, respectively, and the average RRMSE of 9.83% for the overall sample across all cash markets. Results here might be used as standalone technical forecasts or combined with fundamental forecasts for forming perspectives of cash-futures basis trends and carrying out policy analysis. The empirical framework here is easy to implement, which is an essential consideration to many decision makers, and has potential to be generalized for forecasting cash-futures basis of other commodities.