COVID-19下基于进化计算的股票投资组合多目标优化

Asuka Tai, Koki Yoshioka, H. Dozono
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引用次数: 0

摘要

日本人面临着养老储蓄的问题,政府建议从年轻时开始通过投资形成资产。然而,这种方法并没有流行起来。因此,本研究的目的是建立一个系统,可以找到一个组合,既可以获得收益收益,又可以降低长期持有股票的风险。我们使用向量评估遗传算法从股票价格的时间序列数据中寻找Pareto最优解,并试图验证该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multiple Objective optimization of Stock Portfolio Using Evolutionary Computation under COVID-19
Japanese people are facing the problem of saving for old age, and the government recommends asset formation through investment from a young age. However, this approach has not become popular. Therefore, the purpose of this study is to build a system that can find a combination that can obtain income gain with reduced risk from long-term stock holdings. We used the vector-evaluated-genetic-algorithm to find the Pareto optimal solutions from time-series data of stock prices and attempted to verify the effectiveness of this method.
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