Computational content analysis of European Central Bank statements

In this paper we present a framework for the computational content analysis of European Central Bank (ECB) statements. Based on this framework, we provide two approaches that can be used in a practical context. Both approaches use the content of ECB statements to predict upward and downward movement in the MSCI EURO index. General Inquirer (GI) is used for the quantification of the content of the statements. In the first approach, we rely on the frequency of adjectives in the text of the ECB statements in relation to the content categories they represent. The second approach uses fuzzy grammar fragments composed of economic terms and content categories. Our results indicate that the two proposed approaches perform better than a random classifier for predicting upward or downward movement of the MSCI EURO index.