Shanghai Containerised Freight Index Forecasting Based on Deep Learning Methods: Evidence from Chinese Futures Markets
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
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 handle changes in random sample selection, data frequency, and structural shifts within the dataset. It achieved an impressive R² of 96.6% and did better than the LSTM and CNN models that were used alone. This research underscores the predictive prowess of the Chinese futures market in influencing the Shipping Cost Index, deepening our understanding of the intricate relationship between the shipping industry and the financial sphere. Furthermore, it broadens the scope of machine learning applications in maritime transportation management, paving the way for SCFI forecasting research. The study’s findings offer potent decision-support tools and risk management solutions for logistics enterprises, shipping corporations, and governmental entities.
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 handle changes in random sample selection, data frequency, and structural shifts within the dataset. It achieved an impressive R² of 96.6% and did better than the LSTM and CNN models that were used alone. This research underscores the predictive prowess of the Chinese futures market in influencing the Shipping Cost Index, deepening our understanding of the intricate relationship between the shipping industry and the financial sphere. Furthermore, it broadens the scope of machine learning applications in maritime transportation management, paving the way for SCFI forecasting research. The study’s findings offer potent decision-support tools and risk management solutions for logistics enterprises, shipping corporations, and governmental entities.
참고문헌 (43)
- Angelopoulos, J, "Time–frequency analysis of the Baltic Dry Index", Maritime Economics & Logistics, 19(2), pp.211~233 (2017)
- Azab, A., "Coordinating truck appointments with container relocations and retrievals in container terminals under partial appointments information", Transportation Research Part E: Logistics and Transportation Review, 160(102673) (2022)
- Bandyopadhyay, A., "The asymmetric relationship between Baltic Dry Index and commodity spot prices: evidence from nonparametric causality-in-quantiles test", Mineral Economics, 36(2), pp.217~237 (2023)
- Bildirici, M. E., "Baltic Dry Index as a major economic policy indicator: the relationship with economic growth", Procedia-Social and Behavioral Sciences, 210, pp.416~424 (2015)
- Bohl, M. T., "Speculation and the informational efficiency of commodity futures markets", Journal of Commodity Markets, 23(100159) (2021)
- Bosch, D., "The impact of speculation on precious metals futures markets", Resources Policy, 44, pp.118~134 (2015)
- Cartwright, P. A., "Measuring the effect of oil prices on wheat futures prices", Research in International Business and Finance, 33, pp.355~369 (2015)
- Charfeddine, L., "The forward premium anomaly in the energy futures markets: a time-varying approach", Research in International Business and Finance, 47, pp.600~615 (2019)
- Chen, J., "SOC estimation for lithium-ion battery using the LSTM-RNN with extended input and constrained output", Energy, 262(125375) (2023)
- Chen, K., "Two-Sided Mirror: An Analysis of Inflation’s Dual Impact on China’s Economic Growth", East Asian Economic Review, 28(2), pp.175~219 (2024)
- Cox, J. C., "The relation between forward prices and futures prices", Journal of Financial Economics, 9(4), pp.321~346 (1981)
- De Roon, F. A., "Hedging pressure effects in futures markets", The Journal of Finance, 55(3), pp.1437~1456 (2000)
- Duru, O., "A fuzzy extended DELPHI method for adjustment of statistical time series prediction: An empirical study on dry bulk freight market case", Expert Systems with Applications, 39(1), pp.840~848 (2012)
- Gong, Y., "Contagion risk between the shipping freight and stock markets: Evidence from the recent US-China trade war", Transportation Research Part E: Logistics and Transportation Review, 136(101900) (2020)
- Gu, J., "Recent advances in convolutional neural networks", Pattern recognition, 77, pp.354~377 (2018)
- Guo, Z., "A novel deep learning model integrating CNN and GRU to predict particulate matter concentrations", Process Safety and Environmental Protection, 173, pp.604~613 (2023)
- Han, L., "Can the Baltic Dry Index predict foreign exchange rates?", Finance Research Letters, 32(101157) (2020)
- Han, Q., "Forecasting dry bulk freight index with improved SVM", Mathematical Problems in Engineering, 2014 (2014)[DOI]
- Hirata, E., "Forecasting Shanghai Container Freight Index: A Deep-Learning-Based Model Experiment", Journal of Marine Science and Engineering, 10(5), pp.593~ (2022)
- Huo, R., "Relationships between Chinese stock market and its index futures market: Evaluating the impact of QFII scheme", Research in International Business and Finance, 44, pp.135~152 (2018)
- Jiang, B., "Maritime shipping and export trade on ‘Maritime Silk Road’", The Asian Journal of Shipping and Logistics, 34(2), pp.83~90 (2018)
- Jung, Y., "Investing Abroad, Transforming at Home: An Empirical Study of Outward Foreign Direct Investment and Korean Manufacturing’s Servicification", East Asian Economic Review, 28(2), pp.143~174 (2024)
- Kang, S. H., "Financial crises and dynamic spillovers among Chinese stock and commodity futures markets", Physica A: Statistical Mechanics and its Applications, 531(121776) (2019)
- Kim, T. J., "The Environmental and Economic Impact of Trade between South Korea and the United States", East Asian Economic Review, 28(1), pp.37~67 (2024)
- Kim, W, "International Transmission of Macroeconomic Uncertainty in China: A Timevarying Bayesian Global SVAR Approach", East Asian Economic Review, 28(1), pp.95~140 (2024)
- Koyuncu, K., "Forecasting Shanghai containerized freight index by using time series models", Marine Science and Technology Bulletin, 10(4), pp.426~434 (2021)
- Lin, A. J., "Does the Baltic Dry Index drive volatility spillovers in the commodities, currency, or stock markets?", Transportation Research Part E: Logistics and Transportation Review, 127, pp.265~283 (2019)
- Lin, Y., "Forecasting crude oil futures prices using BiLSTM-Attention-CNN model with Wavelet transform", Applied Soft Computing, 130(109723) (2022)
- Mo, D., "The macroeconomic determinants of commodity futures volatility: Evidence from Chinese and Indian markets", Economic Modelling, 70, pp.543~560 (2018)
- Mohanty, S. K., "Regulatory reform and market efficiency: The case of Indian agricultural commodity futures markets", Research in International Business and Finance, 52(101145) (2020)
- Munim, Z. H., "Forecasting container shipping freight rates for the Far East–Northern Europe trade lane", Maritime Economics & Logistics, 19, pp.106~125 (2017)
- Munim, Z. H., "Forecasting container freight rates for major trade routes: a comparison of artificial neural networks and conventional models", Maritime Economics & Logistics, 23, pp.310~327 (2021)
- Nielsen, P., "An investigation of forecast horizon and observation fit’s influence on an econometric rate forecast model in the liner shipping industry", Maritime Policy & Management, 41(7), pp.667~682 (2014)
- Papailias, F., "The Baltic Dry Index: cyclicalities, forecasting and hedging strategies", Empirical Economics, 52, pp.255~282 (2017)
- Park, S., "The impacts of demand and supply shocks in the dry bulk shipping market", The Asian Journal of shipping and logistics, 39(1), pp.13~22 (2023)
- Prokopczuk, M, "Pricing and hedging in the freight futures market", Journal of Futures Markets, 31(5), pp.440~464 (2011)
- Saeed, W., "Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities", Knowledge-Based Systems, 263(110273) (2023)
- Schramm, H. J., "Container freight rate forecasting with improved accuracy by integrating soft facts from practitioners", Research in Transportation Business & Management, 41(100662) (2021)
- Sherstinsky, A, "Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network", Physica D: Nonlinear Phenomena, 404(132306) (2020)
- Siami-Namini, S., "The performance of LSTM and BiLSTM in forecasting time series", Paper presented in 2019 IEEE International conference on big data (Big Data), pp.3285~3292 (2019), IEEE
- Tian, J., "The Structure and Evolution of Renewable Energy Trade Networks in the RCEP Region: Application of SNA Method", East Asian Economic Review, 28(1), pp.3~35 (2024)
- Yang, J., "Price discovery in Chinese agricultural futures markets:A comprehensive look", Journal of Futures Markets, 41(4), pp.536~555 (2021)
- Zhang, X., "A novel hybrid approach to Baltic Dry Index forecasting based on a combined dynamic fluctuation network and artificial intelligence method", Applied Mathematics and Computation, 361, pp.499~516 (2019)