Application of Differential Evolution Algorithms to Portfolio Optimization Problems using Loan
Application of Differential Evolution Algorithms to Portfolio Optimization Problems using Loan
复制标题
差分进化算法在贷款投资组合优化问题中的应用
DOI：
10.11394/tjpnsec.12.26
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
折登由希子
中科院分区：
文献类型：
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作者：
田川聖治;折登由希子
本文将贷款投资组合优化问题转化为一个机会约束问题，即贷款人将贷款资金投资于风险资产。然后将机会约束问题转化为具有等式约束的确定性优化问题。为了将传统的差分进化（DE）算法有效地应用于约束优化问题，比较了两种类型的基因型-表型（GP）映射，即传统的GP映射和新提出的GP映射.作为一个数值实验的结果，包括双向方差分析（双向方差分析），它表明，建议GP映射优于传统的，因为前者提高了DE算法得到的解决方案的质量。通过对资产历史数据的分析，也证实了贷款投资的优势。
In this paper, portfolio optimization using loan is formulated as a chance constrained problem in which the money borrowed from a loan is invested in risk assets. Then the chance constrained problem is transformed into a deterministic optimization problem that has an equality constraint. In order to apply conventional Differential Evolution (DE) algorithms to the constrained optimization problem effectively, two types of Genotype-Phenotype (GP) mappings, namely a conventional GP mapping and a newly proposed GP mapping, are compared. As a result of numerical experiments including a two-way analysis of variance (two-way ANOVA), it is shown that the proposed GP mapping outperforms the conventional one because the former enhances the quality of solutions obtained by DE algorithms. By using historical data of assets, an advantage of the investment using loan is also confirmed.