4.6 Article

Uncertain random portfolio optimization with non-dominated sorting genetic algorithm-II and optimal solution criterion

期刊

ARTIFICIAL INTELLIGENCE REVIEW
卷 56, 期 8, 页码 8511-8546

出版社

SPRINGER
DOI: 10.1007/s10462-022-10388-x

关键词

Uncertainty theory; Portfolio optimization; NSGA-II algorithm; Uncertain random variable; Higher moments

向作者/读者索取更多资源

This paper addresses the uncertainty and randomness in financial systems, specifically focusing on the problem of uncertain random higher moments portfolio optimization. Through the proposal of a new uncertain random mean-variance-skewness-kurtosis-entropy model, along with two auxiliary models, the authors provide a comprehensive framework for portfolio optimization. Numerical simulation results confirm the practicality and validity of the proposed model, the NSGA-II algorithm, and the optimal selection criterion, with population size and parameter adjustment found to significantly impact the results, aligning with real-world observations.
The complexity of the financial systems inevitably leads to the uncertain information and random information simultaneously. Because asset returns frequently show excessive kurtosis and tend to be skewed, we consider an uncertain random higher moments portfolio optimization problem in this paper, in which uncertain and random return rates exist simultaneously. First, the concept of kurtosis for uncertain random variable is defined and the deterministic expressions of kurtosis under three kinds of distributions are derived. Then, an uncertain random mean-variance-skewness-kurtosis-entropy model is formulated with two auxiliary models for portfolio optimization problem. After solving the equivalent deterministic model with NSGA-II algorithm, we propose a new optimal solution criterion for finding a single optimal solution in Pareto optimal solution set. Finally, we present a numerical simulation and obtain the following results: (i) the practicability and the validity of the proposed model, the NSGA-II algorithm and the optimal selection criterion have verified; (ii) the size of population has an obvious influence on the single optimal solution; (iii) the parameter adjustment has a significant impact on the results, and the results are in perfect agreement with the actual situation.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据