Shanghai Containerised Freight Index Forecasting Based on Deep Learning Methods: Evidence from Chinese Futures Markets

With the escalation of global trade, the Chinese commodity futures market has ascended to a pivotal role within the international shipping landscape. The Shanghai Containerized Freight Index (SCFI), a leading indicator of the shipping industry's health, is particularly sensitive to the vicissitudes of the Chinese commodity futures sector. Nevertheless, a significant research gap exists regarding the application of Chinese commodity futures prices as predictive tools for the SCFI. To address this gap, the present study employs a comprehensive dataset spanning daily observations from March 24, 2017, to May 27, 2022, encompassing a total of 29,308 data points. We have crafted an innovative deep learning model that synergistically combines Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) architectures. The outcomes show that the CNN-LSTM model does a great job of finding the nonlinear dynamics in the SCFI dataset and accurately capturing its long-term temporal dependencies. The model can handl