 How does Google search affect trader positions and crude oil prices?Xin Li, Jian Ma, Shouyang Wang and Xun Zhang Economic Modelling, 2015, vol. 49, issue C, 162-171 Abstract: Novel data series constructed from Internet-based platforms such as Google have been widely applied to analyze economic and financial indicators and have been demonstrated to be effective in short-term forecasts. However, few studies have demonstrated the role of Google search data in analyzing trader positions and energy price volatility. This paper uses the Google search volume index (GSVI) to measure investor attention, and investigate the relationships among the GSVI, different trader positions, and crude oil prices from January 2004 to June 2014. The empirical results present some new evidences. First, the GSVI measures investor attention from noncommercial and nonreporting traders, rather than commercial traders. Second, the feedback loop between GSVI and crude oil price is verified. Third, the GSVI improves the forecast accuracy of crude oil price in recursive one-week-ahead forecasts. This paper contributes to existing literature by incorporating open source Internet-based data into the analysis and prediction of crude oil prices, as well as other prices in financial markets in the Big Data Era. Keywords: Internet-based data; Google search volume index; Crude oil price; Granger causality; Trader positions (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:ecmode:v:49:y:2015:i:c:p:162-171 DOI: 10.1016/j.econmod.2015.04.005 Access Statistics for this article Economic Modelling is currently edited by S. Hall and P. Pauly More articles in Economic Modelling from Elsevier |
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