Combining Enterprise Knowledge Graph and News Sentiment Analysis for Stock Price Prediction

dc.contributor.authorLiu, Jue
dc.contributor.authorLu, Zhuocheng
dc.contributor.authorDU, Wei
dc.date.accessioned2019-01-02T23:50:46Z
dc.date.available2019-01-02T23:50:46Z
dc.date.issued2019-01-08
dc.description.abstractMany state of the art methods analyze sentiments in news to predict stock price. When predicting stock price movement, the correlation between stocks is a factor that can’t be ignored because correlated stocks could cause co-movement. Traditional methods of measuring the correlation between stocks are mostly based on the similarity between corresponding stock price data, while ignoring the business relationships between companies, such as shareholding, cooperation and supply-customer relationships. To solve this problem, this paper proposes a new method to calculate the correlation by using the enterprise knowledge graph embedding that systematically considers various types of relationships between listed stocks. Further, we employ Gated Recurrent Unit (GRU) model to combine the correlated stocks’ news sentiment, the focal stock’s news sentiment and the focal stock’s quantitative features to predict the focal stock’s price movement. Results show that our method has an improvement of 8.1% compared with the traditional method.
dc.format.extent9 pages
dc.identifier.doi10.24251/HICSS.2019.153
dc.identifier.isbn978-0-9981331-2-6
dc.identifier.urihttp://hdl.handle.net/10125/59565
dc.language.isoeng
dc.relation.ispartofProceedings of the 52nd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectDecision Support for Smart Cities
dc.subjectDecision Analytics, Mobile Services, and Service Science
dc.subjectEnterprise Knowledge Graph, News Sentiment, Stock Price Prediction
dc.titleCombining Enterprise Knowledge Graph and News Sentiment Analysis for Stock Price Prediction
dc.typeConference Paper
dc.type.dcmiText

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