Abstract
AbstractWhen investing in new stocks, it is difficult to predict returns and risks in a general way without the support of historical data. Therefore, a portfolio optimization model with an uncertain rate of return is proposed. On this basis, prospect theory is used for reference, and then the uncertain return portfolio optimization model is established from the perspective of expected utility maximization. An improved gray wolf optimization (GWO) algorithm is designed because of the complex nonsmooth and nonconcave characteristics of the model. The results show that the GWO algorithm is superior to the traditional particle swarm optimization algorithm and genetic algorithm.
Publisher
Springer Science and Business Media LLC
Subject
General Earth and Planetary Sciences,General Environmental Science
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. The Future of Retail Investing: Goal-Oriented Asset Allocation Platforms;2023 IEEE International Conference on Smart Information Systems and Technologies (SIST);2023-05-04
2. Uncertain random portfolio selection with different mental accounts based on mixed data;Chaos, Solitons & Fractals;2023-03
3. Guest editorial on “data-driven operations management”;Complex & Intelligent Systems;2022-08-13
4. Design and Analysis of Optimized Portfolios for Selected Sectors of the Indian Stock Market;2022 International Conference on Decision Aid Sciences and Applications (DASA);2022-03-23
5. The Impacts of the COVID-19 Pandemic on the Electric Vehicle Sector in the United States;Proceedings of the 2022 International Conference on Economics, Smart Finance and Contemporary Trade (ESFCT 2022);2022