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An Automated Framework for Incorporating News into Stock Trading Strategies
April 2014 (vol. 26 no. 4)
pp. 823-835
Flavius Frasincar, Erasmus Sch. of Econ., Erasmus Univ. Rotterdam, Rotterdam, Netherlands
Uzay Kaymak, Dept. of Ind. Eng. & Innovation Sci., Eindhoven Univ. of Technol., Eindhoven, Netherlands
Wijnand Nuij, Semlab, Alphen a/d Rijn, Netherlands
Viorel Milea, Erasmus Sch. of Econ., Erasmus Univ. Rotterdam, Rotterdam, Netherlands
Frederik Hogenboom, Erasmus Sch. of Econ., Erasmus Univ. Rotterdam, Rotterdam, Netherlands
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.
Index Terms:
Companies,Stock markets,Indexes,Corporate acquisitions,Genetic programming,Context,Information retrieval,web text analysis,Computer applications,evolutionary computing and genetic algorithms,learning,natural language processing
Citation:
Flavius Frasincar, Uzay Kaymak, Wijnand Nuij, Viorel Milea, Frederik Hogenboom, "An Automated Framework for Incorporating News into Stock Trading Strategies," IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 4, pp. 823-835, April 2014, doi:10.1109/TKDE.2013.133
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