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Text-based crude oil price forecasting: A deep learning approach
Li, Xuerong1; Shang, Wei1,2; Wang, Shouyang1,2
2019-10-01
Source PublicationINTERNATIONAL JOURNAL OF FORECASTING
ISSN0169-2070
Volume35Issue:4Pages:1548-1560
AbstractThis study proposes a new, novel crude oil price forecasting method based on online media text mining, with the aim of capturing the more immediate market antecedents of price fluctuations. Specifically, this is an early attempt to apply deep learning techniques to crude oil forecasting, and to extract hidden patterns within online news media using a convolutional neural network (CNN). While the news-text sentiment features and the features extracted by the CNN model reveal significant relationships with the price change, they need to be grouped according to their topics in the price forecasting in order to obtain a greater forecasting accuracy. This study further proposes a feature grouping method based on the Latent Dirichlet Allocation (LDA) topic model for distinguishing effects from various online news topics. Optimized input variable combination is constructed using lag order selection and feature selection methods. Our empirical results suggest that the proposed topic-sentiment synthesis forecasting models perform better than the older benchmark models. In addition, text features and financial features are shown to be complementary in producing more accurate crude oil price forecasts. (C) 2018 The Authors. Published by Elsevier B.V. on behalf of International Institute of Forecasters.
KeywordOil price forecasting Financial markets Online news Text analysis Convolutional neural network
DOI10.1016/j.ijforecast.2018.07.006
Language英语
Funding ProjectNational Natural Science Foundation of China[71571180] ; National Natural Science Foundation of China[71771208] ; National Natural Science Foundation of China[71642006] ; National Center for Mathematics and Interdisciplinary Sciences, CAS
WOS Research AreaBusiness & Economics
WOS SubjectEconomics ; Management
WOS IDWOS:000490649500028
PublisherELSEVIER
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/35904
Collection系统科学研究所
Affiliation1.Univ Chinese Acad Sci, Sch Econ & Management, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Acad Math & Syst Sci, 55 Zhongguancun East Rd, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Li, Xuerong,Shang, Wei,Wang, Shouyang. Text-based crude oil price forecasting: A deep learning approach[J]. INTERNATIONAL JOURNAL OF FORECASTING,2019,35(4):1548-1560.
APA Li, Xuerong,Shang, Wei,&Wang, Shouyang.(2019).Text-based crude oil price forecasting: A deep learning approach.INTERNATIONAL JOURNAL OF FORECASTING,35(4),1548-1560.
MLA Li, Xuerong,et al."Text-based crude oil price forecasting: A deep learning approach".INTERNATIONAL JOURNAL OF FORECASTING 35.4(2019):1548-1560.
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