用关联规则挖掘确定世界股指的共同走势

Burcu Kartal, M. Sert, Melih Kutlu
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引用次数: 3

摘要

目的利用数据挖掘的方法,通过确定哪些指标共同运动,为投资者提供初步的信息。设计/方法/方法在此背景下,为全球11个股票市场指数创建了2001年至2019年每日开盘价和收盘价的数据集。在数据分析中使用了数据挖掘技术中的关联规则算法。研究发现,美国股市指数在关联规则之间的置信度最高。XU100指数与欧洲股市指数和美国股市指数同时波动。此外,恒生指数(HSI)(香港)参与所有股票市场指数的关联规则。原始性/价值对于数据集来说,重要的问题是,以待创建的证券交易所指数的开盘时间,根据其他股票市场指数的开盘或收盘状态,考虑当日或前一天的开盘/收盘价值。因此,对每个股票市场指数分别安排数据集。由于这个数据集的整理过程,有可能找出股票市场指数的共同运动。这证明了全球股指存在协同运动,而这将作为一个周期持续下去。
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Determination of the world stock indices' co-movements by association rule mining
PurposeThis study aims to provide preliminary information to the investor by determining which indices co-movement, with the data mining method.Design/methodology/approachIn this context, data sets containing daily opening and closing prices between 2001 and 2019 have been created for 11 stock market indexes in the world. The association rule algorithm, one of the data mining techniques, is used in the analysis of the data.FindingsIt is observed that the US stock market indices take part in the highest confidence levels between association rules. The XU100 stock index co-movement with both the European stock market indices and the US stock indices. In addition, the Hang Seng Index (HSI) (Hong Kong) takes part in the association rules of all stock market indices.Originality/valueThe important issue for data sets is that the opening/closing values of the same day or the previous day are taken into account according to the open or closed status of other stock market indices by taking the opening time of the stock exchange index to be created. Therefore, data sets are arranged for each stock market index, separately. As a result of this data set arranging process, it is possible to find out co-movements of the stock market indexes. It is proof that the world stock indices have co-movement, and this continues as a cycle.
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来源期刊
Journal of Economics, Finance and Administrative Science
Journal of Economics, Finance and Administrative Science Economics, Econometrics and Finance-Economics, Econometrics and Finance (all)
CiteScore
5.10
自引率
20.80%
发文量
23
审稿时长
12 weeks
期刊介绍: The Universidad ESAN, with more than 50 years of experience in the higher education field and post graduate studies, desires to contribute to the academic community with the most outstanding pieces of research. We gratefully welcome suggestions and contributions from business areas such as operations, supply chain, economics, finance and administration. We publish twice a year, six articles for each issue.
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