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

Additional Metadata
Keywords decision support, finance, news analytics, semantic business information systems, semantic web
Promotor U. Kaymak (Uzay)
Publisher Erasmus University Rotterdam , Erasmus Research Institute of Management
Sponsor Flavius Frasincar, Erasmus School of Economics (ESE), Erasmus University Rotterdam (EUR), Prof.dr.hab. Witold Abramowicz, Geert-Jan Houben, Rommert Dekker
ISBN 978-90-5892-321-9
Persistent URL
Series ERIM Ph.D. Series Research in Management
Milea, V. (2013, February 7). News Analytics for Financial Decision Support (No. EPS-2013-275-LIS). ERIM Ph.D. Series Research in Management. Erasmus Research Institute of Management. Retrieved from