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Outcome Manipulation in Corporate Prediction Markets

Author

Listed:
  • Marco Ottaviani
  • Peter Norman Sørensen

Abstract

This paper presents a framework for applying prediction markets to corporate decision-making. The analysis is motivated by the recent surge of interest in markets as information aggregation devices and their potential use within firms. We characterize the amount of outcome manipulation that results in equilibrium and the impact of this manipulation on market prices. (JEL: D71, D82, D83, D84) (c) 2007 by the European Economic Association.

Suggested Citation

  • Marco Ottaviani & Peter Norman Sørensen, 2007. "Outcome Manipulation in Corporate Prediction Markets," Journal of the European Economic Association, MIT Press, vol. 5(2-3), pages 554-563, 04-05.
    • Handle: RePEc:tpr:jeurec:v:5:y:2007:i:2-3:p:554-563
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    Citations

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    Cited by:

    1. Marco Ottaviani & Peter Norman Sørensen, 2015. "Price Reaction to Information with Heterogeneous Beliefs and Wealth Effects: Underreaction, Momentum, and Reversal," American Economic Review, American Economic Association, vol. 105(1), pages 1-34, January.
    2. Armantier, Olivier & Treich, Nicolas, 2013. "Eliciting beliefs: Proper scoring rules, incentives, stakes and hedging," European Economic Review, Elsevier, vol. 62(C), pages 17-40.
    3. Boulu-Reshef, Béatrice & Comeig, Irene & Donze, Robert & Weiss, Gregory D., 2016. "Risk aversion in prediction markets: A framed-field experiment," Journal of Business Research, Elsevier, vol. 69(11), pages 5071-5075.
    4. Veiga, Helena & Vorsatz, Marc, 2009. "Price manipulation in an experimental asset market," European Economic Review, Elsevier, vol. 53(3), pages 327-342, April.
    5. Galanis, S. & Ioannou, C. & Kotronis, S., 2019. "Information Aggregation Under Ambiguity: Theory and Experimental Evidence," Working Papers 20/05, Department of Economics, City University London.
    6. Bin-Tzong Chie & Chih-Hwa Yang, 2021. "Efficiency of the Experimental Prediction Market: Public Information, Belief Evolution, and Personality Traits," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 11(4), pages 1-3.
    7. Marco Ottaviani & Peter Norman Sørensen, 2009. "Aggregation of Information and Beliefs: Asset Pricing Lessons from Prediction Markets," Discussion Papers 09-14, University of Copenhagen. Department of Economics.
    8. Urmee Khan, 2016. "State-dependent Preferences in Prediction Markets and Prices as Aggregate Statistic," Studies in Microeconomics, , vol. 4(1), pages 70-77, June.
    9. Lionel Page & Robert T. Clemen, 2013. "Do Prediction Markets Produce Well‐Calibrated Probability Forecasts?-super-," Economic Journal, Royal Economic Society, vol. 123(568), pages 491-513, May.
    10. Bo Cowgill & Eric Zitzewitz, 2015. "Corporate Prediction Markets: Evidence from Google, Ford, and Firm X," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(4), pages 1309-1341.
    11. Ahrash Dianat & Christoph Siemroth, 2021. "Improving decisions with market information: an experiment on corporate prediction markets," Experimental Economics, Springer;Economic Science Association, vol. 24(1), pages 143-176, March.
    12. Zhao, Yang & Yu, Min-Teh, 2020. "Predicting catastrophe risk: Evidence from catastrophe bond markets," Journal of Banking & Finance, Elsevier, vol. 121(C).
    13. Di, Chen & Dimitrov, Stanko & He, Qi-Ming, 2019. "Incentive compatibility in prediction markets: Costly actions and external incentives," International Journal of Forecasting, Elsevier, vol. 35(1), pages 351-370.
    14. Boleslavsky, Raphael & Kelly, David L. & Taylor, Curtis R., 2017. "Selloffs, bailouts, and feedback: Can asset markets inform policy?," Journal of Economic Theory, Elsevier, vol. 169(C), pages 294-343.
    15. Urmee Khan, 2016. "State-dependent Preferences in Prediction Markets and Prices as Aggregate Statistic," Working Papers 201609, University of California at Riverside, Department of Economics.
    16. Veiga, Helena & Vorsatz, Marc, 2008. "Aggregation and dissemination of information in experimental asset markets in the presence of a manipulator," DES - Working Papers. Statistics and Econometrics. WS ws084110, Universidad Carlos III de Madrid. Departamento de Estadística.
    17. Christoph Siemroth, 2021. "When Can Decision Makers Learn from Financial Market Prices?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 53(6), pages 1523-1552, September.

    More about this item

    JEL classification:

    • D71 - Microeconomics - - Analysis of Collective Decision-Making - - - Social Choice; Clubs; Committees; Associations
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations

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