Journal
INTERNATIONAL JOURNAL OF FORECASTING
Volume 35, Issue 4, Pages 1548-1560Publisher
ELSEVIER
DOI: 10.1016/j.ijforecast.2018.07.006
Keywords
Oil price forecasting; Financial markets; Online news; Text analysis; Convolutional neural network
Categories
Funding
- National Natural Science Foundation of China [71571180, 71771208, 71642006]
- National Center for Mathematics and Interdisciplinary Sciences, CAS
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This 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.
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