基于距离超度量最小生成树的德黑兰证券交易所法律结构变更前后有效产业识别、聚类及比较

Dariush Damoori, H. Anvar, M. Ashhar
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引用次数: 0

摘要

伊朗工业部门的同步时间评价之间存在高度相互关联,因此无法预测其经济未来。由于一场冲击冲击了一个经济体,整个行业的表现黯然失色,直接的结果是业绩波动,随之而来的是其他经济领域的震荡。随着2005年11月22日证券交易市场的新法律架构通过,一个名为证券交易组织(SEO)的新机构成立了。它在证券交易最高委员会的监督下,负责管理和监督职责。调查表明,重要行业受到这些新规则的影响,随后这些规则又影响到其他行业。我们还识别和聚集了有效的行业,有助于预测资本市场上最强大的行业,以及市场上需要这些信息的投资者和管理者。此外,我们还研究了一段三年的时间内,不同行业的配对指数之间的高度相互关联的重复将导致基于不同行业之间的距离度量的层次图。接下来,使用基于度量的超度量函数,可以根据不同行业之间的关系和行业之间的内效应,显示出不同行业之间的最小生成树。最小生成树主要分为两类:一类是TSE结构变化前的结果;聚类描绘了最小生成树的四度中心,排列了最小距离集合,因此分组如下:{1-化学产品,2-碱性金属}。变化前的第二个聚类,将在最小生成树中形成三度中心,排列最小距离集合,因此分组如下:{1-金属产品,2-其他非金属矿产品,3-投资机构,4-水泥、石灰和石膏,5-金融和货币中介机构,6-银行和信贷机构,7-陶瓷和瓷砖,8-金属矿石开采}。第一组TSE结构改变后的结果;聚类表示最小生成树的四度中心,排列最小距离集,因此分组如下:{1-投资,2-其他非金属矿产品}。变化后的第二簇,在最小生成树中形成三度中心,排列最小距离集合,因此分组如下:{1-水泥、石灰和石膏,2-金融和货币中介,3-糖和方糖,4-金属矿石开采,5-金属产品,6-纸和纸制品,7-汽车零部件}。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification, Clustering and Comparing Effective Industries at Tehran Stock Exchange Before and after its Legal Structure Change by Minimum Spanning Tree of Distance Ultra Metric
The presence of a high degree cross-correlation between the synchronous time evaluation in Iran's industrial section made it impossible to forecast its economic future. As a shock hits an economy, the whole performance of industry overshadowed, the direct result is in performance fluctuation, the following is a shake in other parts of economy. As the new legal structure for stock exchange market passed in November 22, 2005, a new organization established under the title of The Securities and Exchange Organization (SEO). It was under the supervision of The Securities & Exchange Supreme Council and was responsible for administration and supervisory duties. The survey demonstrated that important industries influenced by those new set of rules and subsequently those affect other industries. We also identified and clustered effective industries, helpful in predicting the strongest industries in capital market as well as investors and managers in the market who need such information. Moreover we have investigated a period of three years that the repeat of a high degree cross-correlation between pair indices for different industries in TSE would result in hierarchical graph based on distance metric between different pairs industries. Following, the use of ultra metric function based on metric would show a minimum spanning tree from different industries on the basis of relation between them and intra-effect between these industries. The minimum spanning tree split up into two main clusters: the result from first group before structural change in TSE; clusters picturize center of four degree in minimum spanning tree, arrange set of minimum distances and thus groupings has been shown as follows: {1-Chemicals products, 2-basic metals}. second cluster before change, would result into center of three degree in minimum spanning tree, arrange set of minimum distances and thus groupings has been shown as follows: {1-Metal products, 2-Other non-metallic mineral products, 3-Investment institutions, 4-Cement,lime and Gypsum, 5-Finanacial and monetary intermediaries, 6-Banks and Credit institutions, 7-Ceramic and Tile, 8-Metal ores mining}.The result from first group after structural change in TSE; clusters picturize center of four degree in minimum spanning tree, arrange set of minimum distances and thus groupings has been shown as follows: {1-Investments,2- Other non-metallic mineral products}. Second cluster after change, would result into center of three degree in minimum spanning tree, arrange set of minimum distances and thus groupings has been shown as follows: {1- Cement , lime and Gypsum, 2- Financial and monetary intermediaries, 3-Sugar and sugar cube, 4-Metal ores mining, 5-Metal products, 6-Paper and paper products, 7-auto parts }.
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