Financial Events Recognition in Web News for Algorithmic Trading

Frederik Hogenboom

Research output: Chapter/Conference proceedingConference proceedingAcademicpeer-review

2 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationAdvances in Conceptual Modeling
EditorsS. Castano, P. Vassiliadis, L.V.S. Lakshmanan, M.L. Lee
Place of PublicationFlorence, Italy
PublisherSpringer-Verlag
Pages368-377
Number of pages10
Volume7518
ISBN (Print)9783642339981
DOIs
Publication statusPublished - 15 Oct 2012

Research programs

  • EUR ESE 32

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