An Automated Framework for Incorporating News into Stock Trading Strategies

被引：61
|
作者：
Nuij, Wijnand [1 ]
Milea, Viorel [2 ]
Hogenboom, Frederik [2 ]
Frasincar, Flavius [2 ]
Kaymak, Uzay [3 ]
机构：
[1] Semlab, NL-2408 ZE Alphen Aan Den Rijn, Zuid Holland, Netherlands
[2] Erasmus Univ, Erasmus Sch Econ, NL-3000 DR Rotterdam, Netherlands
[3] Eindhoven Univ Technol, Dept Ind Engn & Innovat Sci, NL-5600 MB Eindhoven, Netherlands
来源：
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING | 2014年 / 26卷 / 04期
关键词：
Computer applications; evolutionary computing and genetic algorithms; learning; natural language processing; web text analysis; INVESTOR SENTIMENT; PRICE REACTION; INFORMATION; LANGUAGE;
D O I：
10.1109/TKDE.2013.133
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：823 / 835
页数：13
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