首页> 外文会议>IASTED International Conference on Modelling, Identification, and Control >CLUSTERING OF THE STOCKS (SECURITIES) USING SELF-ORGANIZING NETWORKS AND NON-LINEAR RANGING TECHNIQUES
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CLUSTERING OF THE STOCKS (SECURITIES) USING SELF-ORGANIZING NETWORKS AND NON-LINEAR RANGING TECHNIQUES

机译:使用自组织网络和非线性测距技术聚类股票(证券)

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The problem of comparison of different companies is facing, when analyzing company's performance in stock exchange market. Screening of the companies, using "well-known" trading strategy parameters, is one of the ways to solve this problem. Actually, much more companies appear on the list, than the trader is willing to buy. To define the best companies on the list, the non-linear ranging technique could be used. The certain meanings of weights could be given to the factors, which characterize the companies. Then, by estimation of all weights company could be assigned to its place in the general listing. Ranging technique with the parameters proposed by T. Allrich and with the parameters proposed by the authors themselves was applied and researched. Self-organizing (Kohonen's) neural network was applied as an alternative to ranging technique. Using fundamental financial parameters as inputs, the output of neural network was the different groups of companies located into a number of disjoint clusters. Three different portfolios were formed as a result of the research. The performance of these portfolios showed which of the researched techniques gave better result. The real data from USA stock markets was used for the realization of the whole idea.
机译:在分析公司在证券交易所市场的绩效时,不同公司比较的问题。使用“众所周知的”交易策略参数,筛选公司是解决这个问题的方法之一。实际上,更多的公司出现在名单上,而不是贸易商愿意购买。要定义列表中的最佳公司,可以使用非线性测距技术。权重的某些含义可以给出这些公司的因素。然后,通过估计所有重量公司,可以将其分配到其总上市的位置。使用T. Allrich提出的参数和作者本身提出的参数的测距技术进行了应用和研究。自组织(Kohonen)的神经网络被应用于测距技术的替代方案。使用基本财务参数作为输入,神经网络的输出是位于多个不相交的群集的不同公司。作为研究结果形成了三种不同的投资组合。这些投资组合的性能显示了哪种研究技术产生了更好的结果。来自美国股票市场的真实数据用于实现整个想法。

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