Forecasting Oil Price Trends with Sentiment of Online News Articles
Jian Li (),
Zhenjing Xu (),
Huijuan Xu (),
Ling Tang and
Lean Yu ()
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Jian Li: Research Base of Beijing Modern Manufacturing Development, College of Economics and Management, Beijing University of Technology, Beijing 100124, P. R. China
Zhenjing Xu: School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, P. R. China
Huijuan Xu: School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, P. R. China
Ling Tang: School of Economics and Management, Beihang University, Beijing 100191, P. R. China
Lean Yu: School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, P. R. China
Asia-Pacific Journal of Operational Research (APJOR), 2017, vol. 34, issue 02, 1-22
With the rapid development of the Internet and big data technologies, a rich of online data (including news releases) can helpfully facilitate forecasting oil price trends. Accordingly, this study introduces sentiment analysis, a useful big data analysis tool, to understand the relevant information of online news articles and formulate an oil price trend prediction method with sentiment. Three main steps are included in the proposed method, i.e., sentiment analysis, relationship investigation and trend prediction. In sentiment analysis, the sentiment (or tone) is extracted based on a dictionary-based approach to capture the relevant online information concerning oil markets and the driving factors. In relationship investigation, the Granger causality analysis is conducted to explore whether and how the sentiment impacts oil price. In trend prediction, the sentiment is used as an important independent variable, and some popular forecasting models, e.g., logistic regression, support vector machine, decision tree and back propagation neural network, are performed. With crude oil futures prices of the West Texas Intermediate (WTI) and news articles of the Thomson Reuters as studying samples, the empirical results statistically support the powerful predictive power of sentiment for oil price trends and hence the effectiveness of the proposed method.
Keywords: Sentiment analysis; big data; text mining; oil price; trend prediction; online news (search for similar items in EconPapers)
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