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

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Title:Combining Enterprise Knowledge Graph and News Sentiment Analysis for Stock Price Prediction
Authors:Liu, Jue
Lu, Zhuocheng
DU, Wei
Keywords:Decision Support for Smart Cities
Decision Analytics, Mobile Services, and Service Science
Enterprise Knowledge Graph, News Sentiment, Stock Price Prediction
Date Issued:08 Jan 2019
Abstract:Many 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.
Pages/Duration:9 pages
URI:http://hdl.handle.net/10125/59565
ISBN:978-0-9981331-2-6
DOI:10.24251/HICSS.2019.153
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Decision Support for Smart Cities


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