Title :
Quantification of Financial News for Economic Surveys
Author_Institution :
Interdiscipl. Lab. for Intell. & Adaptive Syst. Comput. Sci. & Commun. Res. Unit, Univ. of Luxembourg, Luxembourg, Luxembourg
Abstract :
This study concerns financial news articles, which reflect the monetary policy during the US sub prime mortgage crisis. In particular we consider official announcements conducted by the Federal Reserve and its leading representatives. We aim to quantify such information using dependency parsing techniques and statistical measures. In addition, we examine the correlations between the monetary policy and the stock markets by modeling composite index volatilities as functions of key publications. A prototype for the classification of news is targeted, which should reveal the economical impact of events. An eminent aspect of our study is the identification, extraction, and representation of topic-related features and the corresponding instances.
Keywords :
economic cycles; electronic publishing; feature extraction; pattern classification; statistical analysis; stock markets; Federal Reserve; US subprime mortgage crisis; composite index volatility modeling; dependency parsing techniques; economic surveys; financial news articles; financial news quantification; monetary policy; news classification; statistical measures; stock markets; topic-related feature extraction; topic-related feature representation; Data mining; Educational institutions; Feature extraction; Pragmatics; Stock markets; Terminology; classification; feature extraction; financial news;
Conference_Titel :
Data Mining Workshops (ICDMW), 2013 IEEE 13th International Conference on
Conference_Location :
Dallas, TX
Print_ISBN :
978-1-4799-3143-9
DOI :
10.1109/ICDMW.2013.22