A fuzzy model of the MSCI EURO index based on content analysis of European Central Bank statements

V. Milea, Rui Jorge Almeida, U. Kaymak, F. Frasincar
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引用次数: 8

Abstract

In this paper we investigate whether the MSCI EURO index can be predicted based on the content of European Central Bank (ECB) statements. We propose a new model to retrieve information from free text and transform it into a quantitative output. For this purpose, we first identify all adjectives in an ECB statement by using the Stanford Part-of-Speech Tagger and feed these to the General Inquirer (GI) content analysis tool. From GI we obtain a matrix that provides for each document and for each content category the percentage of words in the document that fall under each category. After normalizing the data, we develop a Takagi-Sugeno (TS) fuzzy model using fuzzy c-means clustering. The TS fuzzy system is used to model the levels of the MSCI EURO index. To determine the performance of the model, we focus on the accuracy of predicting upward or downward movement in the index, and obtain, on average, an accuracy of 66%, that corresponds to an improvement of 16% over a random classifier.
基于欧洲央行声明内容分析的MSCI欧元指数模糊模型
本文研究了MSCI欧元指数是否可以根据欧洲央行(ECB)声明的内容进行预测。我们提出了一种从自由文本中检索信息并将其转化为定量输出的新模型。为此,我们首先使用斯坦福词性标注器(Stanford Part-of-Speech Tagger)识别欧洲央行声明中的所有形容词,并将其提供给General Inquirer (GI)内容分析工具。从GI中,我们获得一个矩阵,该矩阵为每个文档和每个内容类别提供文档中属于每个类别的单词的百分比。在对数据进行归一化后,我们使用模糊c均值聚类建立了Takagi-Sugeno (TS)模糊模型。TS模糊系统用于模拟MSCI欧元指数的水平。为了确定模型的性能,我们专注于预测指数向上或向下运动的准确性,并获得平均66%的准确率,相当于比随机分类器提高16%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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