Novel information fusion model for simulating the effect of global public events on the Sino-US soybean futures market

Qing Zhu; Yinglin Ruan; Shan Liu; Lin Wang

doi:10.1016/j.dsm.2021.02.005

Novel information fusion model for simulating the effect of global public events on the Sino-US soybean futures market

Qing Zhu
, Yinglin Ruan
, Shan Liu
, Lin Wang

管理学院

Shaanxi Normal University
Xi'an Jiaotong University
Huazhong University of Science and Technology

科研成果: 期刊稿件 › 文章 › 同行评审

13 引用（Scopus）

摘要

Trade frictions and global public health security events have made it more difficult for investors to generate positive returns from the Sino-US soybean futures markets. This paper employed deep learning and mode decomposition to improve market efficiency and reduce investor risk from Sino-US trade frictions and the COVID-19 pandemic using soybean futures data published on the Dalian Commodity Futures Exchange (DCE) and the Chicago Board of Trade (CBOT). The proposed model was found to assist investors to proactively perceive the market risks from disruptive events and make profitable decisions. The results provide practical guidance for the conduct of quantitative trading on the soybean markets between the two countries.

源语言	英语
页（从-至）	48-59
页数	12
期刊	Data Science and Management
卷	1
期	1
DOI	https://doi.org/10.1016/j.dsm.2021.02.005
出版状态	已出版 - 3月 2021

联合国可持续发展目标

此成果有助于实现下列可持续发展目标：

可持续发展目标 3 良好健康与福祉

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10.1016/j.dsm.2021.02.005

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title = "Novel information fusion model for simulating the effect of global public events on the Sino-US soybean futures market",

abstract = "Trade frictions and global public health security events have made it more difficult for investors to generate positive returns from the Sino-US soybean futures markets. This paper employed deep learning and mode decomposition to improve market efficiency and reduce investor risk from Sino-US trade frictions and the COVID-19 pandemic using soybean futures data published on the Dalian Commodity Futures Exchange (DCE) and the Chicago Board of Trade (CBOT). The proposed model was found to assist investors to proactively perceive the market risks from disruptive events and make profitable decisions. The results provide practical guidance for the conduct of quantitative trading on the soybean markets between the two countries.",

keywords = "COVID-19 pandemic, Data mining, Sino-US trade Friction, Soybean futures",

author = "Qing Zhu and Yinglin Ruan and Shan Liu and Lin Wang",

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AU - Zhu, Qing

AU - Ruan, Yinglin

AU - Liu, Shan

AU - Wang, Lin

PY - 2021/3

Y1 - 2021/3

N2 - Trade frictions and global public health security events have made it more difficult for investors to generate positive returns from the Sino-US soybean futures markets. This paper employed deep learning and mode decomposition to improve market efficiency and reduce investor risk from Sino-US trade frictions and the COVID-19 pandemic using soybean futures data published on the Dalian Commodity Futures Exchange (DCE) and the Chicago Board of Trade (CBOT). The proposed model was found to assist investors to proactively perceive the market risks from disruptive events and make profitable decisions. The results provide practical guidance for the conduct of quantitative trading on the soybean markets between the two countries.

AB - Trade frictions and global public health security events have made it more difficult for investors to generate positive returns from the Sino-US soybean futures markets. This paper employed deep learning and mode decomposition to improve market efficiency and reduce investor risk from Sino-US trade frictions and the COVID-19 pandemic using soybean futures data published on the Dalian Commodity Futures Exchange (DCE) and the Chicago Board of Trade (CBOT). The proposed model was found to assist investors to proactively perceive the market risks from disruptive events and make profitable decisions. The results provide practical guidance for the conduct of quantitative trading on the soybean markets between the two countries.

KW - COVID-19 pandemic

KW - Data mining

KW - Sino-US trade Friction

KW - Soybean futures

UR - https://www.scopus.com/pages/publications/85119090878

U2 - 10.1016/j.dsm.2021.02.005

DO - 10.1016/j.dsm.2021.02.005

M3 - 文章

AN - SCOPUS:85119090878

SN - 2666-7649

VL - 1

SP - 48

EP - 59

JO - Data Science and Management

JF - Data Science and Management

IS - 1

ER -

Novel information fusion model for simulating the effect of global public events on the Sino-US soybean futures market

摘要

联合国可持续发展目标

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