Index fund optimization using a genetic algorithm and a heuristic local search algorithm on scatter diagrams
Index fund optimization using a genetic algorithm and a heuristic local search algorithm on scatter diagrams
复制标题
在散点图上使用遗传算法和启发式局部搜索算法进行指数基金优化
DOI：
10.1109/cec.2007.4424793
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
Hisashi Yamamoto
中科院分区：
文献类型：
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作者：
Y. Orito;Hisashi Yamamoto
众所周知，指数基金是一种受欢迎的被动管理投资组合，已被广泛用于投资。指数基金由股票市场上一定数量的上市公司股票组成，因此基金的回报率与市场指数的变化率相似。然而，很难建立一个包含所有市场公司的完美指数基金。因此，指数基金优化可以看作是投资组合管理的组合优化。在本文中，我们提出了一种由遗传算法和启发式局部搜索算法组成的方法来最大化基金收益率与市场指数变化率之间的相关性。然后，我们将该方法应用于东京证券交易所，并将其与遗传算法和混合遗传算法进行比较。结果表明，本文提出的方法对指数基金的优化是有效的。
It is well known that index funds are popular passively managed portfolios and have been used very extensively for investment. Index funds consist of a certain number of stocks of listed companies on a stock market such that the fund's return rates follow a similar path to the changing rates of the market indices. However it is hard to make a perfect index fund consisting of all companies included in the market. Thus, the index fund optimization can be viewed as a combinatorial optimization for portfolio managements. In this paper, we propose a method that consists of a genetic algorithm and a heuristic local search algorithm to maximize the correlation between the fund's return rates and the changing rates of the market index. We then apply the method to the Tokyo Stock Exchange and compare it with a GA method and a hybrid GA method. The results show that our proposed method is effective for the index fund optimization.
DOI：
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发表时间：
2005
期刊：
Proceedings of the 4th Inrernational conference Computational Intelligence in Economics in economics and Finance ISDN:09707890-3-3 (CD-ROM)
影响因子：
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作者：
Yukiko Orito;Manabu Takeda;Kioaki Iimura;Genji Yamazaki
通讯作者：
Genji Yamazaki
DOI：
10.1016/s0360-8352(03)00020-2
发表时间：
2003-06
期刊：
Comput. Ind. Eng.
影响因子：
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作者：
Y. Orito;Hisashi Yamamoto;G. Yamazaki
通讯作者：
Y. Orito;Hisashi Yamamoto;G. Yamazaki