Evaluating the Efficiency of Index Fund Selections Over the Fund’s Future Period

Orito, Yukiko; Takeda, Manabu; Iimura, Kiyoaki; Yamazaki, Genji

doi:10.1007/978-3-540-72821-4_10

Cited by 8 publications

(13 citation statements)

References 4 publications

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“…Step (1) in the proposed method is a revision of the selection step in the genetic algorithm of Orito and colleagues [12].…”

Section: Step 1: Optimization Using a Genetic Algorithmmentioning

confidence: 99%

“…Takabayashi [11] proposed a method of selecting and rebalancing issues on the Tokyo Stock Exchange by using a genetic algorithm. Orito and colleagues [12] proposed a method of optimizing the investment allocation ratios of the issues composing a fund by using a genetic algorithm. The method proposed by Orito's group [12] creates an effective index fund over a period in which the behavior of the stock index shows a downward or flat trend.…”

Section: Introductionmentioning

confidence: 99%

“…Orito and colleagues [12] proposed a method of optimizing the investment allocation ratios of the issues composing a fund by using a genetic algorithm. The method proposed by Orito's group [12] creates an effective index fund over a period in which the behavior of the stock index shows a downward or flat trend. However, its problem is that the effectiveness of the method, which cannot create a good index fund in a period when the trend is upward, is dependent on variable trends in the stock index.…”

Section: Introductionmentioning

confidence: 99%

“…However, its problem is that the effectiveness of the method, which cannot create a good index fund in a period when the trend is upward, is dependent on variable trends in the stock index. Thus, in this paper we propose a new method for index fund optimization using a genetic algorithm and heuristic local search based on the method proposed by Orito's group [12], in order to create a good index fund during a period with any kind of trend. This paper contains no discussion of rebalancing.…”

Section: Introductionmentioning

confidence: 99%

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Index fund optimization using a genetic algorithm and a heuristic local search

Orito

Inoguchi

Yamamoto

2010

Elect Comm in Japan

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SUMMARYIt is well known that index funds are popular passively managed portfolios and have been used very extensively in hedge trading. 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. Thus, index fund optimization can be viewed as a combinatorial optimization problem for portfolio management. In this paper, we propose an optimization method that consists of a genetic algorithm and a heuristic local search algorithm to make strong linear association between the fund's return rates and the changing rates of the market index. We apply our method to the Tokyo Stock Exchange and create index funds whose return rates follow a similar path to the changing rates of the Tokyo Stock Price Index (TOPIX). The results show that our proposed method creates index funds with a strong linear association to the market index with minimal computing time.

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“…Step (1) in the proposed method is a revision of the selection step in the genetic algorithm of Orito and colleagues [12].…”

Section: Step 1: Optimization Using a Genetic Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Index fund optimization using a genetic algorithm and a heuristic local search

Orito

Inoguchi

Yamamoto

2010

Elect Comm in Japan

Self Cite

View full text Add to dashboard Cite

show abstract

“…While investors will not wish to underperform the index, they will not object if the portfolio outperforms the index. EC applications to the index tracking problem include [98], [105], [90] and [75] which also examines the impact of investor loss aversion preferences on tracking portfolio construction.…”

Section: Index Trackingmentioning

confidence: 99%