Issue No. 04 - April (2014 vol. 26)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2013.133
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
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
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.
Companies, Stock markets, Indexes, Corporate acquisitions, Genetic programming, Context, Information retrieval
W. Nuij, V. Milea, F. Hogenboom, F. Frasincar and U. Kaymak, "An Automated Framework for Incorporating News into Stock Trading Strategies," in IEEE Transactions on Knowledge & Data Engineering, vol. 26, no. 4, pp. 823-835, 2014.