Investigating the information content of non-cash-trading index futures using neural networks

Abstract

Abstract This study investigates the information content of SGX-DT Nikkei 225 and MSCI Taiwan index futures prices during the non-cash-trading (NCT) period. The lead–lag relationship between the futures market during the NCT period and the cash market during its opening period is first investigated by the generalized methods of moments. The obtained leading futures and previous day's cash market closing index are then used as the input variables to predict the opening cash price index by the backpropagation neural network model. Sensitivity analysis is first employed to address and solve the issue of finding the appropriate setup of the topology of the networks. Extensive studies are then performed on the robustness of the constructed network by using different training and testing sample sizes. To demonstrate the effectiveness of our proposed method, the 5-min intraday data of spot and futures index from a 6-month historical record were evaluated using the designed neural network model. Analytic results demonstrate that the proposed neural network model outperforms the neural network model with previous day's closing index as the input variable, the random walk and GARCH model forecasts. It, therefore, indicates that there is valuable information involved in the futures prices during the NCT period that can be used to forecast the opening cash price index. Besides, the neural network model provides better forecasting results than the commonly discussed GARCH model.