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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.

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Bibliographic Info

Paper provided by University of Illinois at Urbana-Champaign, Department of Agricultural and Consumer Economics in its series ACE OFOR Reports with number 14765.

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Date of creation: 1996
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Handle: RePEc:ags:uiucao:14765

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Keywords: Marketing;

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  1. Brennan, M J, 1995. "The Individual Investor," Journal of Financial Research, Southern Finance Association & Southwestern Finance Association, vol. 18(1), pages 59-74, Spring.
  2. Culter, D.M. & Poterba, J.M. & Summers, L.H., 1990. "Speculative Dynamics," Working papers 544, Massachusetts Institute of Technology (MIT), Department of Economics.
  3. De Long, J Bradford, et al, 1990. " Positive Feedback Investment Strategies and Destabilizing Rational Speculation," Journal of Finance, American Finance Association, vol. 45(2), pages 379-95, June.
  4. De Long, J Bradford & Andrei Shleifer & Lawrence H. Summers & Robert J. Waldmann, 1990. "Noise Trader Risk in Financial Markets," Journal of Political Economy, University of Chicago Press, vol. 98(4), pages 703-38, August.
  5. Andrew C. Harvey, 1990. "The Econometric Analysis of Time Series, 2nd Edition," MIT Press Books, The MIT Press, edition 2, volume 1, number 026208189x.
  6. Cutler, David M & Poterba, James M & Summers, Lawrence H, 1990. "Speculative Dynamics and the Role of Feedback Traders," American Economic Review, American Economic Association, vol. 80(2), pages 63-68, May.
  7. Shleifer, Andrei & Summers, Lawrence H, 1990. "The Noise Trader Approach to Finance," Journal of Economic Perspectives, American Economic Association, vol. 4(2), pages 19-33, Spring.
  8. De Long, J Bradford, et al, 1989. " The Size and Incidence of the Losses from Noise Trading," Journal of Finance, American Finance Association, vol. 44(3), pages 681-96, July.
  9. Thornton, Daniel L & Batten, Dallas S, 1985. "Lag-Length Selection and Tests of Granger Causality between Money and Income," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 17(2), pages 164-78, May.
  10. De Bondt, Werner P. M., 1993. "Betting on trends: Intuitive forecasts of financial risk and return," International Journal of Forecasting, Elsevier, vol. 9(3), pages 355-371, November.
  11. J. Bradford De Long & Andrei Shleifer & Lawrence H. Summers & Robert J. Waldmann, 1988. "The Survival of Noise Traders in Financial Markets," NBER Working Papers 2715, National Bureau of Economic Research, Inc.
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Cited by:
  1. Cristian Wieland & Frank Westerhoff, 2004. "A behavioral cobweb model with heterogeneous speculators," Computing in Economics and Finance 2004 171, Society for Computational Economics.
  2. 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.
  3. Czarnitzki, Dirk & Stadtmann, Georg, 2000. "The behaviour of noise traders: empirical evidence on purchases of business magazines," ZEW Discussion Papers 00-65, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
  4. Brorsen, B. Wade & Anderson, Kim B., 2002. "Actual Farmer Market Timing," 2002 Conference, April 22-23, 2002, St. Louis, Missouri 19065, NCR-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.

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