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International Conference on Conceptual Modeling

ER 2012: Advances in Conceptual Modeling pp 368–377Cite as

Financial Events Recognition in Web News for Algorithmic Trading

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7518))

Abstract

Due to its high productivity at relatively low costs, algorithmic trading has become increasingly popular over the last few years. As news can improve the returns generated by algorithmic trading, there is a growing need to use online news information in algorithmic trading in order to react real-time to market events. The biggest challenge is to automate the recognition of financial events from Web news items as an important input next to stock prices for algorithmic trading. In this position paper, we propose a multi-disciplinary approach to financial events recognition in news for algorithmic trading called FERNAT, using techniques from finance, text mining, artificial intelligence, and the Semantic Web.

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Author information

Authors and Affiliations

  1. Erasmus University Rotterdam, P.O. Box 1738, NL-3000 DR, Rotterdam, The Netherlands

    Frederik Hogenboom

Authors
  1. Frederik Hogenboom
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Editor information

Editors and Affiliations

  1. Department of Computer Science, Università degli Studi di Milano, Via Comelico, 39, 20135, Milano, Italy

    Silvana Castano

  2. Department of Computer Science, University of Ioannina, 45110, Ioannina, Hellas, Greece

    Panos Vassiliadis

  3. Department of Computer Science, University of British Columbia, 2366 Main Mall-Vancouver, V6T 1Z4, BC, Canada

    Laks V. Lakshmanan

  4. School of Computing, National University of Singapore, 1,13 Computer Drive, 117417, Singapore

    Mong Li Lee

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© 2012 Springer-Verlag Berlin Heidelberg

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Hogenboom, F. (2012). Financial Events Recognition in Web News for Algorithmic Trading. In: Castano, S., Vassiliadis, P., Lakshmanan, L.V., Lee, M.L. (eds) Advances in Conceptual Modeling. ER 2012. Lecture Notes in Computer Science, vol 7518. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33999-8_43

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  • DOI: https://doi.org/10.1007/978-3-642-33999-8_43

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33998-1

  • Online ISBN: 978-3-642-33999-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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