Index fund selections with genetic algorithms and heuristic classifications
Index fund selections with genetic algorithms and heuristic classifications
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DOI：
10.1016/s0360-8352(03)00020-2
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发表时间：
2003-06
期刊：
影响因子：
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通讯作者：
Y. Orito;Hisashi Yamamoto;G. Yamazaki
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文献类型：
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作者：
Y. Orito;Hisashi Yamamoto;G. Yamazaki
众所周知，指数基金的选择对于股票市场投资的风险对冲非常重要。这里的“选择”是指对于“期货”，从市场上所有的公司中选择n家公司。假设我们投资每个选定公司的c个股票。那么，一组n家公司的总回报率必须很好地跟随市场上股票价格指数的增长率。我们采用决定系数（CDP）作为总回报率和增长率之间的适应性度量。本文的主要目的是提出一种简单的选择方法。该方法由两个步骤组成。一是采用启发式的方法在市场上选择N家公司。另一种是通过将遗传算法应用于 N 个公司的集合来构建一个由 n 个公司组成的集团，例如 In,N。该方法适用于东京证券交易所第一部和第二部。结果表明：该方法对于从市场上的所有企业中选择n家企业的情况给出了接近最优的解决方案。存在一组 N0 公司，当 N>N0 时，In,N 的 CDP 稍微依赖于 N。这意味着当 N 相对较小且因此 n 较小时，可以构造有效的 In,Neven 。当一段时间内股票价格指数的增长率可以被视为线性时间序列数据时，该方法尤其有效。
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 ‘futures’, n companies of all ones in the market are selected. Suppose that we invest in c stocks of each selected company. The total return rate of a group of n companies, then, has to follow well the increasing rate of the stock price index in the market. We adopt the coefficient of determination (CDP) as the measure of fitness between the total return and increasing rates. The main purpose of this paper is to propose a simple method for the selections. The method consists of two steps. One is to select N companies in the market with heuristic approach. The other is to construct a group of n companies, say In,N, by applying genetic algorithms to the set of N companies. The method is applied to the 1st and 2nd Sections of Tokyo Stock Exchange. The results show: the method gives a near optimal solution for the case of selecting n companies from all ones in the market. There is a set of N0companies such that the CDP of In,Ndepends a little on the N when N>N0. This means that it is possible to construct an efficient In,Neven when N is relatively small and hence n is small. The method especially works well when the increasing rate of stock price index over a period can be viewed as a linear time series data.