Neural network forecasting of an opening cash price index

Abstract

The study investigates the information content of SGX-DT Nikkei 225 futures prices during the non-cash-trading (NCT) period using an artificial neural network model. The cash market closing index, the futures prices from a period in the same trading day and on the following trading day are utilized to determine the appropriate input nodes of a back propagation neural network model in forecasting the opening cash price index. Sensitivity analysis is first employed to address and solve the issue of finding the appropriate network topology. Extensive studies are then performed on the robustness of the constructed network by using different training and testing sample sizes. The effectiveness of the method is demonstrated on data from a 6-month historical record (1998-99). Analytic results demonstrate that the proposed neural network model outperforms a neural network model with the previous day's closing index as the input node and the random walk model forecasts. It, therefore, indicates that there is valuable information involved in futures prices during the NCT period that can be used to forecast the opening cash market price index.