Reference : Feature Detection and Classification in Financial News
Dissertations and theses : Doctoral thesis
Engineering, computing & technology : Computer science
Business & economic sciences : Finance
http://hdl.handle.net/10993/16948
Feature Detection and Classification in Financial News
English
Minev, Mihail mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
5-Jun-2014
University of Luxembourg, ​​Luxembourg
Docteur en Informatique
123
Schommer, Christoph mailto
Grammatikos, Theoharry mailto
Schäfer, Ulrich mailto
Treleaven, Philip mailto
Bouvry, Pascal mailto
[en] feature selection ; information extraction ; text representation ; data analytics ; prediction ; classification
[en] The thesis concerns the detection of composite features in news articles as well as their significance for the classification's performance. The considered documents are related to the monetary policy conducted by the Federal Reserve. One principal goal of this work is to quantify embedded information in financial texts by using phrase structure grammar trees in combination with statistical measures and domain knowledge. Thereby, each document is represented as a combination of linguistic features and feature-values. Furthermore, the work examines the correlations between the determined features and an equity market index by modelling the index volatilities as functions of key announcements. A design is targeted, which should enable the temporal tracking of information alterations. Essential aspects of the thesis are the identification, the extraction, and the representation of domain-specific features and their conditional feature-values.
http://hdl.handle.net/10993/16948

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