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How does Google search affect trader positions and crude oil prices?

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  • Li, Xin
  • Ma, Jian
  • Wang, Shouyang
  • Zhang, Xun

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.

Suggested Citation

  • Li, Xin & Ma, Jian & Wang, Shouyang & Zhang, Xun, 2015. "How does Google search affect trader positions and crude oil prices?," Economic Modelling, Elsevier, vol. 49(C), pages 162-171.
    • Handle: RePEc:eee:ecmode:v:49:y:2015:i:c:p:162-171
    DOI: 10.1016/j.econmod.2015.04.005
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    1. Ron Alquist & Lutz Kilian, 2010. "What do we learn from the price of crude oil futures?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 539-573.
    2. Albert Saiz & Uri Simonsohn, 2013. "Proxying For Unobservable Variables With Internet Document-Frequency," Journal of the European Economic Association, European Economic Association, vol. 11(1), pages 137-165, February.
    3. Nikolaos Askitas & Klaus F. Zimmermann, 2009. "Google Econometrics and Unemployment Forecasting," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot, Berlin, vol. 55(2), pages 107-120.
    4. Sanders, Dwight R. & Boris, Keith & Manfredo, Mark, 2004. "Hedgers, funds, and small speculators in the energy futures markets: an analysis of the CFTC's Commitments of Traders reports," Energy Economics, Elsevier, vol. 26(3), pages 425-445, May.
    5. Yan Carrière‐Swallow & Felipe Labbé, 2013. "Nowcasting with Google Trends in an Emerging Market," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(4), pages 289-298, July.
    6. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    7. Frans A. De Roon & Theo E. Nijman & Chris Veld, 2000. "Hedging Pressure Effects in Futures Markets," Journal of Finance, American Finance Association, vol. 55(3), pages 1437-1456, June.
    8. Ding, Haoyuan & Kim, Hyung-Gun & Park, Sung Y., 2014. "Do net positions in the futures market cause spot prices of crude oil?," Economic Modelling, Elsevier, vol. 41(C), pages 177-190.
    9. Heiko Jacobs & Martin Weber, 2012. "The Trading Volume Impact of Local Bias: Evidence from a Natural Experiment," Review of Finance, European Finance Association, vol. 16(4), pages 867-901.
    10. Cifarelli, Giulio & Paladino, Giovanna, 2010. "Oil price dynamics and speculation: A multivariate financial approach," Energy Economics, Elsevier, vol. 32(2), pages 363-372, March.
    11. Zhang, Wei & Shen, Dehua & Zhang, Yongjie & Xiong, Xiong, 2013. "Open source information, investor attention, and asset pricing," Economic Modelling, Elsevier, vol. 33(C), pages 613-619.
    12. Wang, Yaw-Huei & Keswani, Aneel & Taylor, Stephen J., 2006. "The relationships between sentiment, returns and volatility," International Journal of Forecasting, Elsevier, vol. 22(1), pages 109-123.
    13. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
    14. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    15. Guo, Jian-Feng & Ji, Qiang, 2013. "How does market concern derived from the Internet affect oil prices?," Applied Energy, Elsevier, vol. 112(C), pages 1536-1543.
    16. Yu, Lean & Wang, Shouyang & Lai, Kin Keung, 2008. "Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm," Energy Economics, Elsevier, vol. 30(5), pages 2623-2635, September.
    17. Changyun Wang, 2001. "Investor Sentiment and Return Predictability in Agricultural Futures Markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 21(10), pages 929-952, October.
    18. Michael S. Drake & Darren T. Roulstone & Jacob R. Thornock, 2012. "Investor Information Demand: Evidence from Google Searches Around Earnings Announcements," Journal of Accounting Research, Wiley Blackwell, vol. 50(4), pages 1001-1040, September.
    19. Wang, Yu Shan & Chueh, Yen Ling, 2013. "Dynamic transmission effects between the interest rate, the US dollar, and gold and crude oil prices," Economic Modelling, Elsevier, vol. 30(C), pages 792-798.
    20. Carfí, David & Musolino, Francesco, 2014. "Speculative and hedging interaction model in oil and U.S. dollar markets with financial transaction taxes," Economic Modelling, Elsevier, vol. 37(C), pages 306-319.
    21. Matthias Bank & Martin Larch & Georg Peter, 2011. "Google search volume and its influence on liquidity and returns of German stocks," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 25(3), pages 239-264, September.
    22. Bahattin Buyuksahin & Jeffrey H. Harris, 2011. "Do Speculators Drive Crude Oil Futures Prices?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 167-202.
    23. Benhmad, François, 2012. "Modeling nonlinear Granger causality between the oil price and U.S. dollar: A wavelet based approach," Economic Modelling, Elsevier, vol. 29(4), pages 1505-1514.
    24. Robert I. Webb & David P. Simon & Roy A. Wiggins III, 2001. "S&P futures returns and contrary sentiment indicators," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 21(5), pages 447-462, May.
    25. Sanders, Dwight R. & Irwin, Scott H. & Leuthold, Raymond M., 2003. "The Theory Of Contrary Opinion: A Test Using Sentiment Indices In Futures Markets," Journal of Agribusiness, Agricultural Economics Association of Georgia, vol. 21(1), pages 1-26.
    26. Vlastakis, Nikolaos & Markellos, Raphael N., 2012. "Information demand and stock market volatility," Journal of Banking & Finance, Elsevier, vol. 36(6), pages 1808-1821.
    27. Sadorsky, Perry, 2006. "Modeling and forecasting petroleum futures volatility," Energy Economics, Elsevier, vol. 28(4), pages 467-488, July.
    28. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    29. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    30. François Benhmad, 2012. "Modeling Nonlinear Granger Causality between the Oil price and U.S Dollar," Post-Print hal-03062497, HAL.
    31. Vosen, Simeon & Schmidt, Torsten, 2012. "A monthly consumption indicator for Germany based on Internet search query data," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 19(7), pages 683-687.
    32. Kietzmann, Jan H. & Hermkens, Kristopher & McCarthy, Ian P. & Silvestre, Bruno S., 2011. "Social media? Get serious! Understanding the functional building blocks of social media," Business Horizons, Elsevier, vol. 54(3), pages 241-251, May.
    33. Joseph, Kissan & Babajide Wintoki, M. & Zhang, Zelin, 2011. "Forecasting abnormal stock returns and trading volume using investor sentiment: Evidence from online search," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1116-1127, October.
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