An Automated Framework for Incorporating News into Stock Trading Strategies

Author

Nuij, Wijnand ; Milea, Viorel ; Hogenboom, Frederik ; Frasincar, Flavius ; Kaymak, Uzay

Author_Institution

Semlab, Alphen a/d Rijn, Netherlands

Volume

26

Issue

4

fYear

2014

fDate

Apr-14

Firstpage

823

Lastpage

835

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.

Keywords

genetic algorithms; stock markets; automated framework; event extraction; financial markets; genetic programming; incorporating news; stock trading strategies; technical trading indicators; Companies; Context; Corporate acquisitions; Genetic programming; Indexes; Information retrieval; Stock markets; Computer applications; evolutionary computing and genetic algorithms; learning; natural language processing; web text analysis;

fLanguage

English

Journal_Title

Knowledge and Data Engineering, IEEE Transactions on

Publisher

ieee

ISSN

1041-4347

Type

jour

DOI

10.1109/TKDE.2013.133

Filename

6574843