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.

中文翻译：

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互联网信息如何影响油价波动？来自市场热度的证据

紧急情况等非基本指标不仅会影响供求基本面，还会影响国际油价。随着大数据技术的发展，互联网信息可以反映出许多非结构化和半结构化因素。在此基础上，本文提出了一种基于高清的油价预测模型，以探讨互联网信息对国际油价的影响。首先，我们使用LDA和其他方法从大量在线新闻中提取主题。其次，基于条件概率和相关性，构造石油市场的正热点度（PHD）和负热点度（NHD），以实现互联网信息的定量表示。最后，建立了SVAR方法，以探讨HD与油价之间的交互关系。实证结果表明，与其他研究中广泛使用的Google趋势相比，PHD和NHD具有更好的预测国际油价的能力。此外，PHD对油价具有重大的积极影响，而NHD则具有负面影响。从长远来看，PHD占油价波动的51.00％，在相关影响因素中排名第一。本文的研究结果可以为投资者和决策者提供支持。