An Automated Framework for Incorporating News into Stock Trading Strategies

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@Article{Nuij:2014:ieeeKDE,
  author =       "Wijnand Nuij and Viorel Milea and 
                 Frederik Hogenboom and Flavius Frasincar and Uzay Kaymak",
  title =        "An Automated Framework for Incorporating News into
                 Stock Trading Strategies",
  journal =      "IEEE Transactions on Knowledge and Data Engineering",
  year =         "2014",
  month =        apr,
  volume =       "26",
  number =       "4",
  pages =        "823--835",
  keywords =     "genetic algorithms, genetic programming, stock
                 markets, Computer applications, evolutionary computing
                 and genetic algorithms, learning, natural language
                 processing, web text",
  DOI =          "doi:10.1109/TKDE.2013.133",
  ISSN =         "1041-4347",
  abstract =     "In this paper we present a framework for automatic
                 exploitation of news in stock trading strategies.
                 Events are extracted from news messages presented in
                 free text without annotations. We test the introduced
                 framework by deriving trading strategies based on
                 technical indicators and impacts of the extracted
                 events. The strategies take the form of rules that
                 combine technical trading indicators with a news
                 variable, and are revealed through the use of genetic
                 programming. We find that the news variable is often
                 included in the optimal trading rules, indicating the
                 added value of news for predictive purposes and
                 validating our proposed framework for automatically
                 incorporating news in stock trading strategies.",
  notes =        "Also known as \cite{6574843}",
}

Genetic Programming entries for Wijnand Nuij Viorel Milea Frederik Hogenboom Flavius Frasincar Uzay Kaymak

Citations