Index fund selections with genetic algorithms and heuristic classifications

Orito, Yukiko; Yamamoto, Hisashi; Yamazaki, Genji

doi:10.1016/s0360-8352(03)00020-2

Cited by 22 publications

(12 citation statements)

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“…The results show that with our method, it is possible to construct the more effective index fund than the results of Orito et al (6) . The accuracy of the results using our method depends little on the length of time data (turnover data).…”

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confidence: 61%

“…The rule of Orito et al (6) 's is based on the CD between the return rate of each company in the market and the increasing rate of the stock price index. On the other hand, we introduce the rule based on 'turnover' in this paper.…”

Section: Stepmentioning

confidence: 99%

“…He selected 900 companies in the market based on 'the weighted mean' and then applied Genetic Algorithms (GAs) to the 900 companies for the index fund selections. Orito et al (6) proposed a method consisting of the following two steps.…”

Section: Introductionmentioning

confidence: 99%

“…The 'selection' means that for 'stock index futures', n companies of all ones in the market are selected. For index fund selections, Orito et al (6) proposed a method consisting of the following two steps:Step 1 is to select N companies in the market with a heuristic rule based on the coefficient of determination between the return rate of each company in the market and the increasing rate of the stock price index.Step 2 is to construct a group of n companies by applying genetic algorithms to the set of N companies. We note that the rule of Step 1 is not unique.…”

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Index Fund Selections with GAs and Classifications Based on Turnover

2004

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It is well known that index fund selections are important for the risk hedge of investment in a stock market. The 'selection' means that for 'stock index futures', n companies of all ones in the market are selected. For index fund selections, Orito et al. (6) proposed a method consisting of the following two steps:Step 1 is to select N companies in the market with a heuristic rule based on the coefficient of determination between the return rate of each company in the market and the increasing rate of the stock price index.Step 2 is to construct a group of n companies by applying genetic algorithms to the set of N companies. We note that the rule of Step 1 is not unique. The accuracy of the results using their method depends on the length of time data (price data) in the experiments. The main purpose of this paper is to introduce a more 'effective rule' for Step 1. The rule is based on turnover. The method consisting of Step 1 based on turnover andStep 2 is examined with numerical experiments for the 1st Section of Tokyo Stock Exchange. The results show that with our method, it is possible to construct the more effective index fund than the results of Orito et al. (6) . The accuracy of the results using our method depends little on the length of time data (turnover data). The method especially works well when the increasing rate of the stock price index over a period can be viewed as a linear time series data.

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confidence: 61%

Section: Stepmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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