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Noise Trade Demand In Futures Markets

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  • Sanders, Dwight R.
  • Irwin, Scott H.
  • Leuthold, Raymond M.

Abstract

Theoretical noise trader models suggest that uninformed traders can impact market prices. However, these models' conclusions depend crucially on the assumed specification for noise trader demand. This research seeks to empirically determine the appropriate demand specification for uninformed traders. Using commercial market sentiment indices as proxies for noise trader demand, Granger causality models are estimated to examine the linear linkages between sentiment and futures returns. The models strongly suggest that noise traders are positive feedback traders (i.e., extrapolative expectations) with relatively long memories.

Suggested Citation

  • Sanders, Dwight R. & Irwin, Scott H. & Leuthold, Raymond M., 1996. "Noise Trade Demand In Futures Markets," ACE OFOR Reports 14765, University of Illinois at Urbana-Champaign, Department of Agricultural and Consumer Economics.
    • Handle: RePEc:ags:uiucao:14765
    DOI: 10.22004/ag.econ.14765
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    References listed on IDEAS

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    Cited by:

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    2. Cristian Wieland & Frank Westerhoff, 2004. "A behavioral cobweb model with heterogeneous speculators," Computing in Economics and Finance 2004 171, Society for Computational Economics.
    3. Yiuman Tse & Michael Williams, 2011. "Does Index Speculation Impact Commodity Prices? An Intraday Futures Analysis Using intraday data, we find that unidirectional causality runs from commodity index linked commodity futures to non-index ," Working Papers 0007, College of Business, University of Texas at San Antonio.
    4. Frechette, Darren L., 1999. "The Supply Of Storage Under Heterogeneous Expectations," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 31(3), pages 1-14, December.
    5. Olle Östensson, 2012. "The 2008 commodity price boom: did speculation play a role?," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 25(1), pages 17-28, July.
    6. Go, You-How & Lau, Wee-Yeap, 2020. "The impact of global financial crisis on informational efficiency: Evidence from price-volume relation in crude palm oil futures market," Journal of Commodity Markets, Elsevier, vol. 17(C).
    7. Mingue SUn, 2010. "A Branch-and-Bound Algorithm for Representative Integer Efficient Solutions in Multiple Objective Network Programming Problems," Working Papers 0007, College of Business, University of Texas at San Antonio.
    8. Czarnitzki, Dirk & Stadtmann, Georg, 2000. "The behaviour of noise traders: empirical evidence on purchases of business magazines," ZEW Discussion Papers 00-65, ZEW - Leibniz Centre for European Economic Research.
    9. Yiuman Tse & Michael R. Williams, 2013. "Does Index Speculation Impact Commodity Prices? An Intraday Analysis," The Financial Review, Eastern Finance Association, vol. 48(3), pages 365-383, August.
    10. Klumpp, Joni M. & Brorsen, B. Wade & Anderson, Kim B., 2005. "The Impact of Marketing Strategy Information on the Producer's Selling Decision," 2005 Conference, April 18-19, 2005, St. Louis, Missouri 19036, NCR-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.

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    Keywords

    Marketing;

    JEL classification:

    • Q13 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Markets and Marketing; Cooperatives; Agribusiness
    • Q14 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Finance

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