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Fuzzy Correlation Measurement Algorithms for Big Data and Application to Exchange Rates and Stock Prices

机译：大数据和汇率股票价格的模糊相关测量算法

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摘要

In the era of Internet of people and things, big data are merging. Conventional computation algorithms including correlation measures become inefficient to deal with big data problems. Motivated by this observation, we present three fuzzy correlation measurement algorithms, that is, the centroid-based measure, the integral-based measure, and the a-cut-based measure using fuzzy techniques. Data of Shanghai stock price index (SSI) and exchange rates of main foreign currencies over China Yuan from 22 January 2013 to 17 May 2018 are used to check the effectiveness of our algorithms, and, more importantly, to observe the causality relationship between SSI and these main exchange rates. We have observed some findings as follows. First, the usage of the highest, lowest, or closing values in daily exchange rates and stock prices has impact on the significant Granger causes of exchange rates over SSI, but does not produce any opposite cause from SSI to exchange rates. Second, no matter which of our fuzzy measurement algorithms is used, Hongkong Dollar over China Yuan and U.S. Dollar over China Yuan are positively related with SSI, and Euro over China Yuan negatively correlated with SSI is always recognized as a Granger cause to SSI with the significance level being 1%. Finally, both the optimism level and the uncertainty level are observed having impact on the correlation coefficients, but the later brings more significant changes to results of the Granger causality tests.
机译：在人物和事物互联网的时代，大数据是合并的。包括相关措施的传统计算算法使得处理大数据问题效率低。通过这种观察，我们提出了三种模糊相关测量算法，即基于质心的度量，基于积分的度量，以及使用模糊技术的基于A-Cut的度量。 2013年1月22日至2018年5月1日的中国上海股价指数（SSI）和主要外国货币汇率的数据用于检查我们算法的有效性，更重要的是，遵守SSI与SSI之间的因果关系这些主要汇率。我们已经观察到一些结果如下。首先，在日常汇率和股票价格中使用最高，最低或截止价值，对SSI的汇率显着的Ganger原因产生影响，但不会从SSI产生任何相反的原因来汇率。其次，无论使用哪种模糊测量算法，中国对中国人民币的香港元和美元兑美元汇率与SSI呈积极关系，欧元与SSI负相关的欧元总是被认为是一个Ganger导致SSI意义水平为1％。最后，观察到乐观程度和不确定性水平对相关系数产生影响，但后来为格兰杰因果关系测试的结果带来了更大的变化。

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