How Does Internet Information Affect Oil Price Fluctuations? Evidence from the Hot Degree of Market
Not only the fundamentals of supply and demand but also international oil prices are affected by nonfundamental indicators such as emergencies. With the development of big data technology, many unstructured and semistructured factors can be reflected through Internet information. Based on this, this...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2020/5903057 |
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| author | Lu-Tao Zhao Shi-Qiu Guo Jing Miao Ling-Yun He |
| author_facet | Lu-Tao Zhao Shi-Qiu Guo Jing Miao Ling-Yun He |
| author_sort | Lu-Tao Zhao |
| collection | DOAJ |
| description | Not only the fundamentals of supply and demand but also international oil prices are affected by nonfundamental indicators such as emergencies. With the development of big data technology, many unstructured and semistructured factors can be reflected through Internet information. Based on this, this paper proposes a HD-based oil price forecasting model to explore the impact of Internet information on international oil prices. Firstly, we use LDA and other methods to extract topics from massive online news. Secondly, based on conditional probability and correlation, the positive hot degree (PHD) and negative hot degree (NHD) of the oil market are constructed to realize the quantitative representation of Internet information. Finally, the SVAR method is established to explore the interactive relationship between HD and oil prices. The empirical results indicate that PHD and NHD have a better ability to predict international oil prices compared with Google Trends which is widely used in the other research. In addition, PHD has a significant positive impact on oil prices and NHD has a negative impact. In the long term, PHD accounts for 51.00% of oil price fluctuations, ranking the first among relevant influencing factors. The findings of this paper can provide support to investors and policy-makers. |
| format | Article |
| id | doaj-art-9ea05a11cf274e94b1a7d4d3ceda3016 |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-9ea05a11cf274e94b1a7d4d3ceda30162025-08-20T03:38:43ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2020-01-01202010.1155/2020/59030575903057How Does Internet Information Affect Oil Price Fluctuations? Evidence from the Hot Degree of MarketLu-Tao Zhao0Shi-Qiu Guo1Jing Miao2Ling-Yun He3School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, ChinaSchool of Mathematics and Physics, University of Science and Technology Beijing, Beijing, ChinaSchool of Mathematics and Physics, University of Science and Technology Beijing, Beijing, ChinaCenter for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing, ChinaNot only the fundamentals of supply and demand but also international oil prices are affected by nonfundamental indicators such as emergencies. With the development of big data technology, many unstructured and semistructured factors can be reflected through Internet information. Based on this, this paper proposes a HD-based oil price forecasting model to explore the impact of Internet information on international oil prices. Firstly, we use LDA and other methods to extract topics from massive online news. Secondly, based on conditional probability and correlation, the positive hot degree (PHD) and negative hot degree (NHD) of the oil market are constructed to realize the quantitative representation of Internet information. Finally, the SVAR method is established to explore the interactive relationship between HD and oil prices. The empirical results indicate that PHD and NHD have a better ability to predict international oil prices compared with Google Trends which is widely used in the other research. In addition, PHD has a significant positive impact on oil prices and NHD has a negative impact. In the long term, PHD accounts for 51.00% of oil price fluctuations, ranking the first among relevant influencing factors. The findings of this paper can provide support to investors and policy-makers.http://dx.doi.org/10.1155/2020/5903057 |
| spellingShingle | Lu-Tao Zhao Shi-Qiu Guo Jing Miao Ling-Yun He How Does Internet Information Affect Oil Price Fluctuations? Evidence from the Hot Degree of Market Discrete Dynamics in Nature and Society |
| title | How Does Internet Information Affect Oil Price Fluctuations? Evidence from the Hot Degree of Market |
| title_full | How Does Internet Information Affect Oil Price Fluctuations? Evidence from the Hot Degree of Market |
| title_fullStr | How Does Internet Information Affect Oil Price Fluctuations? Evidence from the Hot Degree of Market |
| title_full_unstemmed | How Does Internet Information Affect Oil Price Fluctuations? Evidence from the Hot Degree of Market |
| title_short | How Does Internet Information Affect Oil Price Fluctuations? Evidence from the Hot Degree of Market |
| title_sort | how does internet information affect oil price fluctuations evidence from the hot degree of market |
| url | http://dx.doi.org/10.1155/2020/5903057 |
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