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A new exploration in Baltic Dry Index forecasting learning: application of a deep ensemble model

Author

Listed:
  • Miao Su

    (Kyunghee University)

  • Keun Sik Park

    (Chung Ang University)

  • Sung Hoon Bae

    (Chung Ang University
    Samsung SDS)

Abstract

World trade is growing constantly, facilitated by the fast expansion of logistics. However, risks and uncertainty in shipping have also increased, in dire need to be addressed by the research community, through more accurate and efficient methods of forecasting. In recent years, combining attention models and deep learning has produced remarkable results in various domains. With daily data spanning the period from January 6, 1995, to September 16, 2022 (totaling 6896 observations), we predict the Baltic Dry Index (BDI) using a deep integrated model (CNN-BiLSTM-AM) comprising a convolutional neural network (CNN), bi-directional long short-term memory (BiLSTM), and the attention mechanism (AM). Our findings indicate that the integrated model CNN-BiLSTM-AM encompasses the nonlinear and nonstationary characteristics of the shipping industry, and it has a greater prediction accuracy than any single model, with an R2 value of 96.9%. This research shows that focusing on the data’s value has a particular appeal in the intelligence era. The study enhances the integrated research of machine learning in the shipping business and offers a foundation for economic decisions.

Suggested Citation

  • Miao Su & Keun Sik Park & Sung Hoon Bae, 2024. "A new exploration in Baltic Dry Index forecasting learning: application of a deep ensemble model," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 26(1), pages 21-43, March.
  • Handle: RePEc:pal:marecl:v:26:y:2024:i:1:d:10.1057_s41278-023-00278-6
    DOI: 10.1057/s41278-023-00278-6
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    References listed on IDEAS

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    1. Kei Kanamoto & Liwen Murong & Minato Nakashima & Ryuichi Shibasaki, 2021. "Can maritime big data be applied to shipping industry analysis? Focussing on commodities and vessel sizes of dry bulk carriers," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 23(2), pages 211-236, June.
    2. Arunava Bandyopadhyay & Prabina Rajib, 2023. "The asymmetric relationship between Baltic Dry Index and commodity spot prices: evidence from nonparametric causality-in-quantiles test," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 36(2), pages 217-237, June.
    3. Fotis Papailias & Dimitrios D. Thomakos & Jiadong Liu, 2017. "The Baltic Dry Index: cyclicalities, forecasting and hedging strategies," Empirical Economics, Springer, vol. 52(1), pages 255-282, February.
    4. Qingcheng Zeng & Chenrui Qu & Adolf K.Y. Ng & Xiaofeng Zhao, 2016. "A new approach for Baltic Dry Index forecasting based on empirical mode decomposition and neural networks," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 18(2), pages 192-210, June.
    5. Alizadeh, Amir H. & Muradoglu, Gulnur, 2014. "Stock market efficiency and international shipping-market information," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 33(C), pages 445-461.
    6. Papapostolou, Nikos C. & Pouliasis, Panos K. & Nomikos, Nikos K. & Kyriakou, Ioannis, 2016. "Shipping investor sentiment and international stock return predictability," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 96(C), pages 81-94.
    7. Lin, Arthur J. & Chang, Hai Yen & Hsiao, Jung Lieh, 2019. "Does the Baltic Dry Index drive volatility spillovers in the commodities, currency, or stock markets?," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 127(C), pages 265-283.
    8. Theo E. Notteboom & Hercules E. Haralambides, 2020. "Port management and governance in a post-COVID-19 era: quo vadis?," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 22(3), pages 329-352, September.
    9. Ruzhao Gao & Yueqiang Zhao & Bing Zhang, 2023. "Baltic dry index and global economic policy uncertainty: evidence from the linear and nonlinear Granger causality tests," Applied Economics Letters, Taylor & Francis Journals, vol. 30(3), pages 360-366, February.
    10. Christos Katris & Manolis G. Kavussanos, 2021. "Time series forecasting methods for the Baltic dry index," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1540-1565, December.
    11. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
    12. Batchelor, Roy & Alizadeh, Amir & Visvikis, Ilias, 2007. "Forecasting spot and forward prices in the international freight market," International Journal of Forecasting, Elsevier, vol. 23(1), pages 101-114.
    13. Zhang, X. & Chen, M.Y. & Wang, M.G. & Ge, Y.E. & Stanley, H.E., 2019. "A novel hybrid approach to Baltic Dry Index forecasting based on a combined dynamic fluctuation network and artificial intelligence method," Applied Mathematics and Computation, Elsevier, vol. 361(C), pages 499-516.
    14. Kevin Cullinane & Hercules Haralambides, 2021. "Global trends in maritime and port economics: the COVID-19 pandemic and beyond," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-04046225, HAL.
    15. Nicholas Apergis & James E. Payne, 2013. "New Evidence on the Information and Predictive Content of the Baltic Dry Index," IJFS, MDPI, vol. 1(3), pages 1-19, July.
    16. Hercules E. Haralambides, 2019. "Gigantism in container shipping, ports and global logistics: a time-lapse into the future," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 21(1), pages 1-60, March.
    17. Kavussanos, Manolis G. & Alizadeh-M, Amir H., 2001. "Seasonality patterns in dry bulk shipping spot and time charter freight rates," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 37(6), pages 443-467, December.
    18. K P B Cullinane & K J Mason & M Cape, 1999. "A Comparison of Models for Forecasting the Baltic Freight Index: Box-Jenkins Revisited," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 1(2), pages 15-39, December.
    19. Husaini Said & Evangelos Giouvris, 2019. "Oil, the Baltic Dry index, market (il)liquidity and business cycles: evidence from net oil-exporting/oil-importing countries," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 33(4), pages 349-416, December.
    20. Lin, Faqin & Sim, Nicholas C.S., 2013. "Trade, income and the Baltic Dry Index," European Economic Review, Elsevier, vol. 59(C), pages 1-18.
    21. Chao-Chi Chang & Heng Chih Chou & Chun Chou Wu, 2014. "Value-at-risk analysis of the asymmetric long-memory volatility process of dry bulk freight rates," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 16(3), pages 298-320, September.
    22. Han, Liyan & Wan, Li & Xu, Yang, 2020. "Can the Baltic Dry Index predict foreign exchange rates?," Finance Research Letters, Elsevier, vol. 32(C).
    23. Kevin Cullinane & Hercules Haralambides, 2021. "Global trends in maritime and port economics: the COVID-19 pandemic and beyond," Post-Print hal-04046225, HAL.
    24. Yordan Leonov & Ventsislav Nikolov, 2012. "A wavelet and neural network model for the prediction of dry bulk shipping indices," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 14(3), pages 319-333, September.
    25. Shun Chen & Hilde Meersman & Eddy van de Voorde, 2012. "Forecasting spot rates at main routes in the dry bulk market," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 14(4), pages 498-537, December.
    26. Yimiao Gu & Zhenxi Chen & Donald Lien, 2019. "Baltic Dry Index and iron ore spot market: dynamics and interactions," Applied Economics, Taylor & Francis Journals, vol. 51(35), pages 3855-3863, July.
    27. Kevin Cullinane & Hercules Haralambides, 2021. "Global trends in maritime and port economics: the COVID-19 pandemic and beyond," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 23(3), pages 369-380, September.
    28. Spyros Makridakis & Andreas Merikas & Anna Merika & Mike G. Tsionas & Marwan Izzeldin, 2020. "A novel forecasting model for the Baltic dry index utilizing optimal squeezing," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 56-68, January.
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