Forecasting Crude Oil Price Using Event Extraction

Research on crude oil price forecasting has attracted tremendous attention from scholars and policymakers due to its significant effect on the global economy. Besides supply and demand, crude oil prices are largely influenced by various factors, such as economic development, financial markets, confl...

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Autores principales:	Jiangwei Liu, Xiaohong Huang
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Bayesian inference crude oil price forecasting event extraction natural language processing (NLP) news sentiment Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/8b511ae98d244a7ca6b9bb0831781b24
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spelling	oai:doaj.org-article:8b511ae98d244a7ca6b9bb0831781b242021-11-18T00:05:08ZForecasting Crude Oil Price Using Event Extraction2169-353610.1109/ACCESS.2021.3124802https://doaj.org/article/8b511ae98d244a7ca6b9bb0831781b242021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9599721/https://doaj.org/toc/2169-3536Research on crude oil price forecasting has attracted tremendous attention from scholars and policymakers due to its significant effect on the global economy. Besides supply and demand, crude oil prices are largely influenced by various factors, such as economic development, financial markets, conflicts, wars, and political events. Most previous research treats crude oil price forecasting as a time series or econometric variable prediction problem. Although recently there have been researches considering the effects of real-time news events, most of these works mainly use raw news headlines or topic models to extract text features without profoundly exploring the event information. In this study, a novel crude oil price forecasting framework, AGESL, is proposed to deal with this problem. In our approach, an open domain event extraction algorithm is utilized to extract underlying related events, and a text sentiment analysis algorithm is used to extract sentiment from massive news. Then a deep neural network integrating the news event features, sentimental features, and historical price features is built to predict future crude oil prices. Empirical experiments are performed on West Texas Intermediate (WTI) crude oil price data, and the results show that our approach obtains superior performance compared with several benchmark methods.Jiangwei LiuXiaohong HuangIEEEarticleBayesian inferencecrude oil price forecastingevent extractionnatural language processing (NLP)news sentimentElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 149067-149076 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Bayesian inference crude oil price forecasting event extraction natural language processing (NLP) news sentiment Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Bayesian inference crude oil price forecasting event extraction natural language processing (NLP) news sentiment Electrical engineering. Electronics. Nuclear engineering TK1-9971 Jiangwei Liu Xiaohong Huang Forecasting Crude Oil Price Using Event Extraction
description	Research on crude oil price forecasting has attracted tremendous attention from scholars and policymakers due to its significant effect on the global economy. Besides supply and demand, crude oil prices are largely influenced by various factors, such as economic development, financial markets, conflicts, wars, and political events. Most previous research treats crude oil price forecasting as a time series or econometric variable prediction problem. Although recently there have been researches considering the effects of real-time news events, most of these works mainly use raw news headlines or topic models to extract text features without profoundly exploring the event information. In this study, a novel crude oil price forecasting framework, AGESL, is proposed to deal with this problem. In our approach, an open domain event extraction algorithm is utilized to extract underlying related events, and a text sentiment analysis algorithm is used to extract sentiment from massive news. Then a deep neural network integrating the news event features, sentimental features, and historical price features is built to predict future crude oil prices. Empirical experiments are performed on West Texas Intermediate (WTI) crude oil price data, and the results show that our approach obtains superior performance compared with several benchmark methods.
format	article
author	Jiangwei Liu Xiaohong Huang
author_facet	Jiangwei Liu Xiaohong Huang
author_sort	Jiangwei Liu
title	Forecasting Crude Oil Price Using Event Extraction
title_short	Forecasting Crude Oil Price Using Event Extraction
title_full	Forecasting Crude Oil Price Using Event Extraction
title_fullStr	Forecasting Crude Oil Price Using Event Extraction
title_full_unstemmed	Forecasting Crude Oil Price Using Event Extraction
title_sort	forecasting crude oil price using event extraction
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/8b511ae98d244a7ca6b9bb0831781b24
work_keys_str_mv	AT jiangweiliu forecastingcrudeoilpriceusingeventextraction AT xiaohonghuang forecastingcrudeoilpriceusingeventextraction
_version_	1718425236519518208

Forecasting Crude Oil Price Using Event Extraction

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