Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/316635 
Year of Publication: 
2024
Citation: 
[Journal:] East Asian Economic Review (EAER) [ISSN:] 2508-1667 [Volume:] 28 [Issue:] 3 [Year:] 2024 [Pages:] 359-388
Publisher: 
Korea Institute for International Economic Policy (KIEP), Sejong-si
Abstract: 
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
Subjects: 
SCFI Forecast
Futures Market
Machine Learning
Convolution Neural Network
Long and Short-term Memory
JEL: 
G12
L15
O40
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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