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Forecasting Maritime and Financial Market Trends: Leveraging CNN-LSTM Models for Sustainable Shipping and China’s Financial Market Integration

Author

Listed:
  • Zihui Han

    (The Graduate School of Global Business, Sejong University, Seoul-si 03773, Republic of Korea)

  • Xiangcheng Zhu

    (Department of Pedagogy, Bashkir State Pedagogical University n.a. M. Akmulla, Ufa 450008, Russia)

  • Zhenqing Su

    (The Graduate School of Global Business, Kyonggi University, Suwon-si 16227, Republic of Korea)

Abstract

With the acceleration of economic globalization, China’s financial market has emerged as a vital force in the global financial system. The Baltic Dry Index (BDI) and China Container Freight Index (CCFI) serve as key indicators of the shipping sector’s health, reflecting their sensitivity to shifts in China’s financial landscape. This study utilizes an innovative CNN-LSTM deep learning model to forecast the BDI and CCFI, using 25,974 daily data points from the Chinese financial market between 5 May 2015 and 30 November 2022. The model achieves high predictive accuracy across diverse samples, frequencies, and structural variations, with an R 2 of 97.2%, showcasing its robustness. Beyond its predictive strength, this research underscores the critical role of China’s financial market in advancing sustainable practices within the global shipping industry. By merging advanced analytics with sustainable shipping strategies, the findings offer stakeholders valuable tools for optimizing operations and investments, reducing emissions, and promoting long-term environmental sustainability in both sectors. Additionally, this study enhances the resilience and stability of financial and shipping ecosystems, laying the groundwork for an eco-friendly, efficient, and sustainable global logistics network in the digital era.

Suggested Citation

  • Zihui Han & Xiangcheng Zhu & Zhenqing Su, 2024. "Forecasting Maritime and Financial Market Trends: Leveraging CNN-LSTM Models for Sustainable Shipping and China’s Financial Market Integration," Sustainability, MDPI, vol. 16(22), pages 1-24, November.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:22:p:9853-:d:1519157
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    References listed on IDEAS

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    Cited by:

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    2. Haochuan Wu & Chi Gong, 2025. "Modeling the Ningbo Container Freight Index Through Deep Learning: Toward Sustainable Shipping and Regional Economic Resilience," Sustainability, MDPI, vol. 17(10), pages 1-21, May.

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