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

Liang Chen (), Jiankun Liu (), Rongyu Pei, Zhenqing Su () and Ziyang Liu ()
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Liang Chen: Kyonggi University, https://orcid.org/0009-0000-8582-150X
Jiankun Liu: Chung-Ang University, https://orcid.org/0009-0007-5885-9685
Rongyu Pei: Kyonggi University, https://orcid.org0009-0003-7162-1250
Zhenqing Su: Kyonggi University, https://orcid.org/0009-0002-6528-8749
Ziyang Liu: Kyonggi University, https://orcid.org/0000-0002-7761-5674

East Asian Economic Review, 2024, vol. 28, issue 3, 359-388

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

Keywords: SCFI Forecast; Futures Market; Machine Learning; Convolution Neural Network; Long and Short-term Memory (search for similar items in EconPapers)
JEL-codes: G12 L15 O40 (search for similar items in EconPapers)
Date: 2024
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