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Wijnand Nuij

dblp:143/9593 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2014
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Information extraction and text analysis · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational finance and economics · 100%
Theoretical computer science
1 paper
Mathematical optimization · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Information extraction and text analysis
event extraction		 0.212014
An Automated Framework for Incorporating News into Stock Trading Strategies · IEEE Trans. Knowl. Data Eng. 2014
Computational finance and economics
algorithmic trading		 0.212014
An Automated Framework for Incorporating News into Stock Trading Strategies · IEEE Trans. Knowl. Data Eng. 2014
Mathematical optimization
evolutionary computation		 0.112014
An Automated Framework for Incorporating News into Stock Trading Strategies · IEEE Trans. Knowl. Data Eng. 2014
Mathematical optimization › evolutionary computation
genetic programming		 0.112014
An Automated Framework for Incorporating News into Stock Trading Strategies · IEEE Trans. Knowl. Data Eng. 2014

Methods — techniques the papers use, named apart from their topics

technical indicators · 0.6genetic programming · 0.6
YearPublicationVenuePosition
2014 An Automated Framework for Incorporating News into Stock Trading Strategies
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.
Wijnand Nuij, Viorel Milea, Frederik Hogenboom, Flavius Frasincar, Uzay Kaymak
 IEEE Trans. Knowl. Data Eng.1