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