How Does Public Attention Influence Natural Gas Price?: New Evidence with Google Search Data

How Does Public Attention Influence Natural Gas Price?: New Evidence with Google Search Data

Xin Li (University of Chinese Academy of Sciences, China), Jian Ma (City University of Hong Kong, Hong Kong), Wei Shang (Chinese Academy of Sciences, China), Shouyang Wang (University of Chinese Academy of Sciences, China & Chinese Academy of Sciences, China) and Xun Zhang (Chinese Academy of Sciences, China)
DOI: 10.4018/978-1-5225-0803-8.ch070
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Abstract

Public attention on natural gas price, which reflects the demand dynamics, is considered as a new factor to influence the movement of price. So investigate the impact of public attention on natural gas price is an innovative research issue in energy economics. This paper innovatively constructs a measure of public attention and examines its impact on natural gas price. A data set generated from Google Trends is used to measure public attention and then rigorous econometric models are applied to evaluate its predictive ability. The empirical study shows that (i) public attention is closely related to natural gas price, with contemporaneous positive correlation coefficient being 0.59, (ii) public attention leads natural gas price, (iii) the model including public attention data outperforms benchmark model. By using a more direct and representative way of forecasting based on the knowledge collected from the users, this paper also has important implications for applying Internet knowledge to improve the forecast accuracy of other energy price.
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1. Introduction

Natural gas is one of the major energy resources in the world. It has been widely utilized in a variety of aspects, such as residential, commercial, industrial, and power generation. Natural gas is a commonly used fuel for residential cooking and heating, and an essential energy material for commercial electricity generation (U.S. Energy Information Administration, 2013). Therefore, the timely and accurate prediction of natural gas prices is of great importance. The natural gas price is determined by many complicated factors reflecting its demand and supply. Existing studies on natural gas price forecast consider traditional statistical data source like production, storage, import and export of this resource, as well as economic growth, oil price, and even weather (e.g., Buchanan et al., 2001; Mu, 2007; Brown and Yucel, 2008). However, little effort has been devoted to applying massive user data from Internet, which seem to be a more direct way to represent natural gas demand. To fill this gap, this paper puts forward a new perspective by incorporating user search data generated from Internet to represent public attention in order to improve natural gas price forecast accuracy.

In the Big Data Era, public attentioninformation established from Internet-based knowledge has become a new influencing factor on price for the following two reasons (Barber & Odean, 2008; Da et al., 2011). Firstly, the price variation of natural gas is closely related to the public’s daily life. Both residents who need gas to cook and keep warm and the business managers who need it to keep operation of the company should pay attention to natural gas price. Secondly, the public attention represents their demand dynamics, which determines the natural gas prices in turn (Krichene, 2002; Huntington, 2007). Accordingly, to investigate whether public attention could improve the forecast accuracy of natural gas prices is an innovative and meaningful research topic.Due to the development of Internet technologies and massive data processing methods, it is possible to timely collect the public behaviour data. Thisused to be difficult because seldom data sets provided timely public behaviour information in the past. Nowadays, people carry out many online activities, such as searching information, expressing opinions, and communicating with others through social media platforms. Besidesbehaviour data like search keywords, page views and the re-tweets,some personal information that is not conflict with privacy policy are recorded by software. These online data contain abundant knowledge and couldpossibly reflect the public demand.

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