摘要：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.
