abstract = "This PhD thesis contributes to the newly emerged,
growing body of scientific work on the use of News
Analytics in Finance. Regarded as the next significant
development in Automated Trading, News Analytics
extends trading algorithms to incorporate information
extracted from textual messages, by translating it into
actionable, valuable knowledge. The thesis addresses
one main theme: the incorporation of news into trading
algorithms. This relates to three main tasks: i) the
extraction of the information contained in news, ii)
the representation of the information contained in
news, and iii) the aggregation of this information into
actionable knowledge. We validate our approach by
designing and implementing three semantic systems: a
system for the computational content analysis of
European Central Bank statements, a system for
incorporating news in stock trading strategies, and a
time-aware system for trading based on analyst
recommendations. The approach we choose for addressing
these tasks is an interdisciplinary one. For the
extraction of information from news we rely on
approaches borrowed from Computer Science and
Linguistics. The representation of the information
contained in news is realized by using, and extending,
the state-of-the-art in Semantic Web technology. We do
this by bringing together insights from Logics,
Metaphysics, and Computational Semantics. The
aggregation of information is done by using techniques
and results from Computational Intelligence and
Finance",
notes = "Kwantitatieve nieuwsanalyse voor financiele
besluitvorming
SIKS Dissertation Series No. 2013-01
https://siks.nl/