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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
