Forecasting Crude Oil Risk Using a Multivariate Multiscale Convolutional Neural Network Model

Zou, Yingchao; He, Kaijian

doi:10.3390/math10142413

Cited by 5 publications

(2 citation statements)

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“…In weighted networks, the node strength is defined as the sum of the weights of all edges connected to node i. The equation for calculating the node strength is given in Equation (3). The interpretation of the node strength may vary across different CNs.…”

Section: Topology Features Of Cnsmentioning

confidence: 99%

“…Experiencing rapid economic growth, China has witnessed a relentless upsurge in energy demand. According to data from the National Bureau of Statistics, China's external reliance on crude oil reached 72% in 2021 [3,4]. Maritime transport constitutes the pre-dominant mode of crude oil transportation in China, surpassing pipeline and railway transportation [5].…”

Section: Introductionmentioning

confidence: 99%

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Assessing the Connectivity Reliability of a Maritime Transport Network: A Case of Imported Crude Oil in China

Wang

Feng

et al. 2023

JMSE

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Crude oil transportation is a vital component of the global energy supply, and the global Crude Oil Maritime Transportation Network (COMTN) plays a crucial role as a carrier for crude oil transportation. Once the network faces attacks that result in the failure of certain routes, a severe threat is posed to the crude oil supply security of importing countries. Therefore, it is crucial to evaluate the reliability of the COMTN. This study proposes a model for evaluating the reliability of the imported COMTN by analyzing the impact of node failures. Firstly, the network is constructed using complex networks (CNs) theory, with ports, canals, and straits as nodes, and shipping routes as directed edges. Secondly, based on the Weighted Leader Rank algorithm, a comprehensive evaluation metric for CNs is established, and a node importance assessment model is developed to rank the nodes accordingly. Thirdly, a case study is conducted using China’s imported COMTN as an example, evaluating the connectivity reliability (CR) under random and deliberate attack scenarios. Finally, measures and recommendations are provided to enhance the CR of China’s imported COMTN. The findings indicate that deliberate attacks pose a greater threat, and reliability varies across maritime routes, with the Americas route exhibiting higher reliability compared to the Middle East and Southeast Asia routes. The results of this study can provide relevant recommendations for policy makers. The model proposed in this study can also be applied to other countries and regions to assess the connectivity reliability of their local COMTNs and develop appropriate measures for the results.

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Section: Topology Features Of Cnsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing the Connectivity Reliability of a Maritime Transport Network: A Case of Imported Crude Oil in China

Wang

Feng

et al. 2023

JMSE

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Stock index forecasting based on multivariate empirical mode decomposition and temporal convolutional networks

Yuan

Zhang

Zhao

2023

Applied Soft Computing

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Forecasting crude oil futures using an ensemble model including investor sentiment and attention

Yao,

Yang

2023

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PurposeSince crude oil is crucial to the nation's economic growth, crude oil futures are closely related to many other markets. Accurate forecasting can offer investors trustworthy guidance. Numerous studies have begun to consider creating new metrics from social networks to improve forecasting models in light of their rapid development. To improve the forecasting of crude oil futures, the authors suggest an integrated model that combines investor sentiment and attention.Design/methodology/approachThis study first creates investor attention variables using Baidu search indices and investor sentiment variables for medium sulfur crude oil (SC) futures by collecting comments from financial forums. The authors feed the price series into the NeuralProphet model to generate a new feature set using the output subsequences and predicted values. Next, the authors use the CatBoost model to extract additional features from the new feature set and perform multi-step predictions. Finally, the authors explain the model using Shapley additive explanations (SHAP) values and examine the direction and magnitude of each variable's influence.FindingsThe authors conduct forecasting experiments for SC futures one, two and three days in advance to evaluate the effectiveness of the proposed model. The empirical results show that the model is a reliable and effective tool for predicting, and including investor sentiment and attention variables in the model enhances its predictive power.Research limitations/implicationsThe data analyzed in this paper span from 2018 through 2022, and the forecast objectives only apply to futures prices for those years. If the authors alter the sample data, the experimental process must be repeated, and the outcomes will differ. Additionally, because crude oil has financial characteristics, its price is influenced by various external circumstances, including global epidemics and adjustments in political and economic policies. Future studies could consider these factors in models to forecast crude oil futures price volatility.Practical implicationsIn conclusion, the proposed integrated model provides effective multistep forecasts for SC futures, and the findings will offer crucial practical guidance for policymakers and investors. This study also considers other relevant markets, such as stocks and exchange rates, to increase the forecast precision of the model. Furthermore, the model proposed in this paper, which combines investor factors, confirms the predictive ability of investor sentiment. Regulators can utilize these findings to improve their ability to predict market risks based on changes in investor sentiment. Future research can improve predictive effectiveness by considering the inclusion of macro events and further model optimization. Additionally, this model can be adapted to forecast other financial markets, such as stock markets and other futures products.Originality/valueThe authors propose a novel integrated model that considers investor factors to enhance the accuracy of crude oil futures forecasting. This method can also be applied to other financial markets to improve their forecasting efficiency.

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Forecasting Crude Oil Risk Using a Multivariate Multiscale Convolutional Neural Network Model

Cited by 5 publications

References 49 publications

Assessing the Connectivity Reliability of a Maritime Transport Network: A Case of Imported Crude Oil in China

Assessing the Connectivity Reliability of a Maritime Transport Network: A Case of Imported Crude Oil in China

Stock index forecasting based on multivariate empirical mode decomposition and temporal convolutional networks

Forecasting crude oil futures using an ensemble model including investor sentiment and attention

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