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Predicting the Future

Author

Listed:
  • Kay-Yut Chen

    (HP Laboratories)

  • Leslie R. Fine

    (HP Laboratories)

  • Bernardo A. Huberman

    (HP Laboratories)

Abstract

We present a novel methodology for predicting future outcomes that uses small numbers of individuals participating in an imperfect information market. By determining their risk attitudes and performing a nonlinear aggregation of their predictions, we are able to assess the probability of the future outcome of an uncertain event and compare it to both the objective probability of its occurrence and the performance of the market as a whole. Experiments show that this nonlinear aggregation mechanism vastly outperforms both the imperfect market and the best of the participants. We then extend the mechanism to prove robust in the presence of public information.

Suggested Citation

  • Kay-Yut Chen & Leslie R. Fine & Bernardo A. Huberman, 2003. "Predicting the Future," Information Systems Frontiers, Springer, vol. 5(1), pages 47-61, January.
    • Handle: RePEc:spr:infosf:v:5:y:2003:i:1:d:10.1023_a:1022041805438
    DOI: 10.1023/A:1022041805438
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    References listed on IDEAS

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    1. Sunder, Shyam, 1992. "Market for Information: Experimental Evidence," Econometrica, Econometric Society, vol. 60(3), pages 667-695, May.
    2. O'Brien, John & Srivastava, Sanjay, 1991. "Dynamic Stock Markets with Multiple Assets: An Experimental Analysis," Journal of Finance, American Finance Association, vol. 46(5), pages 1811-1838, December.
    3. Scharfstein, David S & Stein, Jeremy C, 1990. "Herd Behavior and Investment," American Economic Review, American Economic Association, vol. 80(3), pages 465-479, June.
    4. Forsythe, Robert & Lundholm, Russell, 1990. "Information Aggregation in an Experimental Market," Econometrica, Econometric Society, vol. 58(2), pages 309-347, March.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. van Bruggen, G.H. & Spann, M. & Lilien, G.L. & Skiera, B., 2006. "Institutional Forecasting: The Performance of Thin Virtual Stock Markets," ERIM Report Series Research in Management ERS-2006-028-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    2. Patrick Buckley, 2012. "Using Information Aggregation Markets for Decision Support," International Journal of Business Research and Management (IJBRM), Computer Science Journals (CSC Journals), vol. 3(3), pages 133-143, June.
    3. Cipriano Santos & Tere Gonzalez & Haitao Li & Kay-Yut Chen & Dirk Beyer & Sundaresh Biligi & Qi Feng & Ravindra Kumar & Shelen Jain & Ranga Ramanujam & Alex Zhang, 2013. "HP Enterprise Services Uses Optimization for Resource Planning," Interfaces, INFORMS, vol. 43(2), pages 152-169, April.
    4. Rothenstein, Roland & Pawelzik, Klaus, 2005. "Limited profit in predictable stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 348(C), pages 419-427.
    5. Patrick Buckley & Fergal O’Brien, 0. "The effect of malicious manipulations on prediction market accuracy," Information Systems Frontiers, Springer, vol. 0, pages 1-13.
    6. Ledyard, John & Hanson, Robin & Ishikida, Takashi, 2009. "An experimental test of combinatorial information markets," Journal of Economic Behavior & Organization, Elsevier, vol. 69(2), pages 182-189, February.
    7. McAdams, David & Malone, Thomas W., 2005. "Internal Markets for Supply Chain Capacity Allocation," Working papers 18179, Massachusetts Institute of Technology (MIT), Sloan School of Management.
    8. Trappey, Charles V. & Shih, Tsui-Yii & Trappey, Amy J.C., 2007. "Modeling international investment decisions for financial holding companies," European Journal of Operational Research, Elsevier, vol. 180(2), pages 800-814, July.
    9. Patrick Buckley & Fergal O’Brien, 2017. "The effect of malicious manipulations on prediction market accuracy," Information Systems Frontiers, Springer, vol. 19(3), pages 611-623, June.

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