Issue |
SHS Web Conf.
Volume 215, 2025
6th International Symposium on Frontiers of Economics and Management Science (FEMS 2025)
|
|
---|---|---|
Article Number | 01027 | |
Number of page(s) | 4 | |
DOI | https://doi.org/10.1051/shsconf/202521501027 | |
Published online | 12 May 2025 |
New opportunities and trends in international investment driven by machine learning
School of International Relations, Guangdong University of Foreign Studies,
Guangzhou, China
* Corresponding author: huangyujie_gdufs@outlook.com
In the period of rapid social and economic development, international investment has always been an effective way to promote international economic and trade and regional economic development. Especially after entering the era of big data, due to the diversity and particularity of practical activities, international investment is highly dependent on data and information. Make clear the new opportunities and challenges brought by big data technology to international investment, use machine learning algorithm to study the basic content and development direction of international investment, and finally put forward effective investment methods, which can provide a reference for the subsequent investment work. In this regard, after understanding the new opportunities and challenges of international investment in the era of big data, this paper mainly studies the decentralized portfolio selection model with machine learning algorithm as the core, and verifies the application effect of various algorithms combined with experiments. Finally, it is proved that the application performance of the improved mean variance model based on random forest revenue prediction is higher.
Key words: big data / machine learning / international investment / random forest / investment portfolio
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.