Examining sustainable overseas investment information-sharing model for automobile enterprises: A multi-modal weight network approach

• Main Authors: CHENG, Yuan, CHEN, Xiaofang, LIN, Changbo, MA, Sheqing, FENG, Jie
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Continuous bag of words; Information-sharing model; Multi-mode weighting; Overseas investment; Sharing platform; Accounting; Finance and Financial Management; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/soa_research/2045; https://ink.library.smu.edu.sg/context/soa_research/article/3072/viewcontent/s13132_024_01814_2_pvoa_cc_by.pdf
• Institution: Singapore Management University

In an era of globalization, automotive companies are increasingly looking to make overseas investments to expand their production capacity and explore foreign markets. However, the outcomes of such investments are often influenced by a myriad of factors, including policy changes, social dynamics, and market conditions. To address the need for a comprehensive overseas investment information-sharing model, this research proposes an innovative approach based on a multi-modal weight network. This model aims to provide users with a global perspective on overseas investment opportunities, encompassing policy insights, and market dynamics. It integrates data from various sources, offering multi-dimensional information on investment regions, scales, fields, motivations, and strategies. Real-time updates ensure the timeliness and accuracy of the information, enabling users to adapt to the rapidly changing international economic landscape. Challenges such as data collection, privacy concerns, investment diversity, advanced analytics, and real-time updates are carefully considered and addressed. The model incorporates sophisticated analytical methods to extract valuable insights from vast data, guiding sound decision-making for automotive enterprises. Experimental results demonstrate an impressive accuracy rate of 87.9% and an mAP value of 86.8%, highlighting the model’s effectiveness in providing precise and reliable investment information. This innovative multi-modal weight network model empowers automotive companies to navigate the complexities of international investments, enabling them to make informed decisions and achieve success in the global market.