Improved Large-Scale Multi-objective Optimization Algorithm for Portfolio Management
- DOI
- 10.2991/978-94-6463-198-2_85How to use a DOI?
- Keywords
- large-scale multi-objective optimization; portfolio management; immune cloning
- Abstract
For securities investors, the return and risk of investment are the two main aspects of their concern. However, in real life, the vast majority of investors are not professionally trained, which makes them confused about portfolio selection in the face of tens of thousands of investment targets in the financial market. The multi-objective optimization problem of investment return and risk is solved by using an improved large-scale multi-objective optimization algorithm. From experimental results, it can be seen that the improved algorithm can get better results than previous algorithms on the large-scale multi-objective problems. The portfolio with the highest Sharpe ratio produced by the improved algorithm outperforms the CSI 300 index over the same period in terms of return and maximum retracement. It shows that the improved algorithm can achieve the selection of the investors’ ideal portfolio from a larger number of stocks.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Shengtao Zhang AU - Xuyang Li AU - Jie Zhang AU - Wanqing Li PY - 2023 DA - 2023/08/10 TI - Improved Large-Scale Multi-objective Optimization Algorithm for Portfolio Management BT - Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023) PB - Atlantis Press SP - 827 EP - 836 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-198-2_85 DO - 10.2991/978-94-6463-198-2_85 ID - Zhang2023 ER -