A Study of Futures Price Forecasting with a Focus on the Role of Different Economic Markets

Current research on futures price prediction focuses on the autocorrelation of historical prices, yet the resulting predictions often suffer from issues of inaccuracy and lag. This paper uses Chinese corn futures as the subject of study. First, we identify key influencing factors, such as Chinese soybean futures, U.S. soybean futures, and the U.S.-China exchange rate, that exhibit ‘predictive causality’ with corn futures prices through the Granger causality test. We then apply the sample convolution and interaction network (SCINet) to perform both single-step and multi-step predictions of futures prices. The experimental results show that incorporating key influencing factors significantly improves prediction accuracy. For instance, in the single-step prediction, combining historical prices with Chinese soybean futures prices reduces the MAE and RMSE values by 5.12% and 3.45%, respectively, compared to using historical prices alone. Furthermore, the SCINet model outperforms traditional models such as temporal convolutional networks (TCN), gated recurrent units (GRU), and long short-term memory (LSTM) networks when based solely on historical prices. This study validates the effectiveness of key influencing factors in forecasting Chinese corn futures prices and demonstrates the advantages of the SCINet model in futures price prediction. The findings provide valuable insights for optimising the agricultural futures market and enhancing the ability to predict price risks.