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How does investor attention affect international crude oil prices?

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  • Yao, Ting
  • Zhang, Yue-Jun
  • Ma, Chao-Qun

Abstract

In order to investigate the impacting mechanism of investors’ attention and crude oil prices, we construct a direct, timely and unambiguous proxy for investor attention in crude oil market by aggregating the Google search volume index (GSVI). Based on the GSVI, we employ the Structural Vector Autoregression (SVAR) model to empirically explore the impact of investor attention on WTI crude oil price from January 2004 to November 2016. The results indicate that: (1) investor attention does have significant negative impact on WTI crude oil price during the sample period; (2) investor attention shocks contributes 15% to the long-run fluctuation of WTI crude oil price during the sample period, which is second only to that of supply shocks (69%) among the contributors concerned; and (3) when the business cycle stays in expansion, it has positive influence on both investor attention and WTI crude oil price. Meanwhile, our robustness check, using Brent crude oil price and a different construction form of the GSVI, confirms that the central results are reliable.

Suggested Citation

  • Yao, Ting & Zhang, Yue-Jun & Ma, Chao-Qun, 2017. "How does investor attention affect international crude oil prices?," Applied Energy, Elsevier, vol. 205(C), pages 336-344.
  • Handle: RePEc:eee:appene:v:205:y:2017:i:c:p:336-344
    DOI: 10.1016/j.apenergy.2017.07.131
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    1. Nikkinen, Jussi & Rothovius, Timo, 2019. "Energy sector uncertainty decomposition: New approach based on implied volatilities," Applied Energy, Elsevier, vol. 248(C), pages 141-148.
    2. Zhang, Yue-Jun & Chevallier, Julien & Guesmi, Khaled, 2017. "“De-financialization” of commodities? Evidence from stock, crude oil and natural gas markets," Energy Economics, Elsevier, vol. 68(C), pages 228-239.
    3. Lu-Tao Zhao & Guan-Rong Zeng & Wen-Jing Wang & Zhi-Gang Zhang, 2019. "Forecasting Oil Price Using Web-based Sentiment Analysis," Energies, MDPI, Open Access Journal, vol. 12(22), pages 1-18, November.
    4. Yong Jiang & Yi-Shuai Ren & Chao-Qun Ma & Jiang-Long Liu & Basil Sharp, 2018. "Does the price of strategic commodities respond to U.S. Partisan Conflict?," Papers 1810.08396, arXiv.org, revised Feb 2020.
    5. Polanco Martínez, Josué M. & Abadie, Luis M. & Fernández-Macho, J., 2018. "A multi-resolution and multivariate analysis of the dynamic relationships between crude oil and petroleum-product prices," Applied Energy, Elsevier, vol. 228(C), pages 1550-1560.
    6. Shen, Yifan & Shi, Xunpeng & Variam, Hari Malamakkavu Padinjare, 2018. "Risk transmission mechanism between energy markets: A VAR for VaR approach," Energy Economics, Elsevier, vol. 75(C), pages 377-388.
    7. Lu-Tao Zhao & Li-Na Liu & Zi-Jie Wang & Ling-Yun He, 2019. "Forecasting Oil Price Volatility in the Era of Big Data: A Text Mining for VaR Approach," Sustainability, MDPI, Open Access Journal, vol. 11(14), pages 1-20, July.
    8. Huajiao Li & Yajie Qi & Sui Guo & Sida Feng, 2019. "Dynamic Transmission of Correlation between Investor Attention and Stock Price: Evidence from China’s Energy Industry Typical Stocks," Complexity, Hindawi, vol. 2019, pages 1-15, December.
    9. Tang, Ling & Zhang, Chengyuan & Li, Ling & Wang, Shouyang, 2020. "A multi-scale method for forecasting oil price with multi-factor search engine data," Applied Energy, Elsevier, vol. 257(C).

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