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Internet, noise trading and commodity futures prices

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  • Peri, Massimo
  • Vandone, Daniela
  • Baldi, Lucia

Abstract

This paper relates to internet, noise trading and commodity futures prices. The theoretical framework is the Mixture Distribution Hypothesis (MDH) that posits a joint dependence of return volatility and information. We use two different proxies for the observed component of information flows, which allows to separate the effect of internet searches and information published in newspapers. We analyse the effect of information from the internet using the Internet Search Volume from Google Insight. Empirical results support the MDH and highlight that the search of information on internet by noise traders can amplify volatility.

Suggested Citation

  • Peri, Massimo & Vandone, Daniela & Baldi, Lucia, 2014. "Internet, noise trading and commodity futures prices," International Review of Economics & Finance, Elsevier, vol. 33(C), pages 82-89.
  • Handle: RePEc:eee:reveco:v:33:y:2014:i:c:p:82-89
    DOI: 10.1016/j.iref.2014.03.006
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    as
    1. D. S. Prasada Rao & Bart van Ark, 2013. "Introduction," Chapters, in: D. S.P. Rao & Bart van Ark (ed.), World Economic Performance, chapter 1, pages 1-6, Edward Elgar Publishing.
    2. Fama, Eugene F., 1998. "Market efficiency, long-term returns, and behavioral finance," Journal of Financial Economics, Elsevier, vol. 49(3), pages 283-306, September.
    3. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    4. Hal R. Varian, 2007. "The Economics of Internet Search," 'Angelo Costa' Lectures Serie, SIPI Spa, issue Lect. VII.
    5. Tauchen, George E & Pitts, Mark, 1983. "The Price Variability-Volume Relationship on Speculative Markets," Econometrica, Econometric Society, vol. 51(2), pages 485-505, March.
    6. Dwight R. Sanders & Scott H. Irwin, 2010. "A speculative bubble in commodity futures prices? Cross‐sectional evidence," Agricultural Economics, International Association of Agricultural Economists, vol. 41(1), pages 25-32, January.
    7. Epps, Thomas W & Epps, Mary Lee, 1976. "The Stochastic Dependence of Security Price Changes and Transaction Volumes: Implications for the Mixture-of-Distributions Hypothesis," Econometrica, Econometric Society, vol. 44(2), pages 305-321, March.
    8. Irwin, Scott H. & Sanders, Dwight R. & Merrin, Robert P., 2009. "Devil or Angel? The Role of Speculation in the Recent Commodity Price Boom (and Bust)," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 41(2), August.
    9. Jose M. Marin & Jacques P. Olivier, 2008. "The Dog That Did Not Bark: Insider Trading and Crashes," Journal of Finance, American Finance Association, vol. 63(5), pages 2429-2476, October.
    10. T. Heller & R. Huet & Bénédicte Vidaillet, 2013. "Introduction," Post-Print hal-00848256, HAL.
    11. Meulbroek, Lisa K, 1992. " An Empirical Analysis of Illegal Insider Trading," Journal of Finance, American Finance Association, vol. 47(5), pages 1661-1699, December.
    12. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
    13. Brad M. Barber & Terrance Odean, 2001. "The Internet and the Investor," Journal of Economic Perspectives, American Economic Association, vol. 15(1), pages 41-54, Winter.
    14. Mitchell, Mark L & Mulherin, J Harold, 1994. " The Impact of Public Information on the Stock Market," Journal of Finance, American Finance Association, vol. 49(3), pages 923-950, July.
    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. Ederington, Louis H & Lee, Jae Ha, 1993. " How Markets Process Information: News Releases and Volatility," Journal of Finance, American Finance Association, vol. 48(4), pages 1161-1191, September.
    17. van der Wijst,Nico, 2013. "Finance," Cambridge Books, Cambridge University Press, number 9781107029224, December.
    18. Laura L. Veldkamp, 2006. "Media Frenzies in Markets for Financial Information," American Economic Review, American Economic Association, vol. 96(3), pages 577-601, June.
    19. Matthew Rabin, 2002. "Inference by Believers in the Law of Small Numbers," The Quarterly Journal of Economics, Oxford University Press, vol. 117(3), pages 775-816.
    20. Brad M. Barber & Terrance Odean, 2008. "All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors," Review of Financial Studies, Society for Financial Studies, vol. 21(2), pages 785-818, April.
    21. Kao, Erin H. & Fung, Hung-Gay, 2012. "Intraday trading activities and volatility in round-the-clock futures markets," International Review of Economics & Finance, Elsevier, vol. 21(1), pages 195-209.
    22. Stephan Meier & Charles Sprenger, 2007. "Impatience and credit behavior: evidence from a field experiment," Working Papers 07-3, Federal Reserve Bank of Boston, revised 2007.
    23. Leslie A. Jeng & Andrew Metrick & Richard Zeckhauser, 2003. "Estimating the Returns to Insider Trading: A Performance-Evaluation Perspective," The Review of Economics and Statistics, MIT Press, vol. 85(2), pages 453-471, May.
    24. Massimo Peri & Lucia Baldi & Daniela Vandone, 2013. "Price discovery in commodity markets," Applied Economics Letters, Taylor & Francis Journals, vol. 20(4), pages 397-403, March.
    25. Nam, Jouahn & Wang, Jun & Zhang, Ge, 2008. "Strategic trading against retail investors with loss-aversion," International Review of Economics & Finance, Elsevier, vol. 17(1), pages 45-55.
    26. Lamoureux, Christopher G & Lastrapes, William D, 1990. " Heteroskedasticity in Stock Return Data: Volume versus GARCH Effects," Journal of Finance, American Finance Association, vol. 45(1), pages 221-229, March.
    27. Ke Tang & Wei Xiong, 2010. "Index Investment and Financialization of Commodities," NBER Working Papers 16385, National Bureau of Economic Research, Inc.
    28. 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.
    29. Ke, Bin & Huddart, Steven & Petroni, Kathy, 2003. "What insiders know about future earnings and how they use it: Evidence from insider trades," Journal of Accounting and Economics, Elsevier, vol. 35(3), pages 315-346, August.
    30. Smith, Geoffrey Peter, 2012. "Google Internet search activity and volatility prediction in the market for foreign currency," Finance Research Letters, Elsevier, vol. 9(2), pages 103-110.
    31. Syed, Azmat A & Liu, Pu & Smith, Stanley D, 1989. "The Exploitation of Inside Information at the Wall Street Journal: A Test of Strong Form Efficiency," The Financial Review, Eastern Finance Association, vol. 24(4), pages 567-579, November.
    32. Chen, Chun-nan & Wu, Chunchi, 2009. "Small trades and volatility increases after stock splits," International Review of Economics & Finance, Elsevier, vol. 18(4), pages 592-610, October.
    33. Alwathainani, Abdulaziz M., 2012. "Consistent winners and losers," International Review of Economics & Finance, Elsevier, vol. 21(1), pages 210-220.
    34. French, Kenneth R. & Roll, Richard, 1986. "Stock return variances : The arrival of information and the reaction of traders," Journal of Financial Economics, Elsevier, vol. 17(1), pages 5-26, September.
    35. Eric Zivot, 2008. "Practical Issues in the Analysis of Univariate GARCH Models," Working Papers UWEC-2008-03-FC, University of Washington, Department of Economics.
    36. Cooke, Bryce & Robles, Miguel, 2009. "Recent food prices movements: A time series analysis," IFPRI discussion papers 942, International Food Policy Research Institute (IFPRI).
    37. Scott H. Irwin & Dwight R. Sanders, 2011. "Index Funds, Financialization, and Commodity Futures Markets," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 33(1), pages 1-31.
    38. Clark, Peter K, 1973. "A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices," Econometrica, Econometric Society, vol. 41(1), pages 135-155, January.
    39. Daniel Kahneman, 2003. "Maps of Bounded Rationality: Psychology for Behavioral Economics," American Economic Review, American Economic Association, vol. 93(5), pages 1449-1475, December.
    40. 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.
    41. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    42. Vlastakis, Nikolaos & Markellos, Raphael N., 2012. "Information demand and stock market volatility," Journal of Banking & Finance, Elsevier, vol. 36(6), pages 1808-1821.
    43. Black, Fischer, 1986. " Noise," Journal of Finance, American Finance Association, vol. 41(3), pages 529-543, July.
    44. Corredor, Pilar & Ferrer, Elena & Santamaria, Rafael, 2013. "Investor sentiment effect in stock markets: Stock characteristics or country-specific factors?," International Review of Economics & Finance, Elsevier, vol. 27(C), pages 572-591.
    45. Harris, Lawrence, 1987. "Transaction Data Tests of the Mixture of Distributions Hypothesis," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 22(2), pages 127-141, June.
    46. Read, Daniel & Loewenstein, George & Rabin, Matthew, 1999. "Choice Bracketing," Journal of Risk and Uncertainty, Springer, vol. 19(1-3), pages 171-197, December.
    47. Kalev, Petko S. & Liu, Wai-Man & Pham, Peter K. & Jarnecic, Elvis, 2004. "Public information arrival and volatility of intraday stock returns," Journal of Banking & Finance, Elsevier, vol. 28(6), pages 1441-1467, June.
    48. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    Cited by:

    1. Baldi, Lucia & Peri, Massimo & Vandone, Daniela, 2016. "Stock markets’ bubbles burst and volatility spillovers in agricultural commodity markets," Research in International Business and Finance, Elsevier, vol. 38(C), pages 277-285.
    2. Georgios Bampinas & Theodore Panagiotidis & Christina Rouska, 2019. "Volatility persistence and asymmetry under the microscope: the role of information demand for gold and oil," Scottish Journal of Political Economy, Scottish Economic Society, vol. 66(1), pages 180-197, February.
    3. Corredor, Pilar & Ferrer, Elena & Santamaria, Rafael, 2015. "Sentiment-prone investors and volatility dynamics between spot and futures markets," International Review of Economics & Finance, Elsevier, vol. 35(C), pages 180-196.
    4. repec:eee:ecolet:v:170:y:2018:i:c:p:42-45 is not listed on IDEAS
    5. repec:gam:jsusta:v:9:y:2017:i:6:p:1071-:d:101981 is not listed on IDEAS

    More about this item

    Keywords

    Noise trading; Corn price volatility; Information; Mixture Distribution Hypothesis; EGARCH;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • Q11 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Aggregate Supply and Demand Analysis; Prices

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