Hostname: page-component-7b9c58cd5d-9klzr Total loading time: 0 Render date: 2025-03-21T23:32:08.363Z Has data issue: false hasContentIssue false
  • English
  • Français

Improving decisions with market information: an experiment on corporate prediction markets

Published online by Cambridge University Press:  14 March 2025

Ahrash Dianat  and
Christoph Siemroth
Ahrash Dianat*
Affiliation:
Department of Economics, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK
Christoph Siemroth*
Affiliation:
Department of Economics, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK
*
[email protected]
[email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

We conduct a lab experiment to investigate an important corporate prediction market setting: A manager needs information about the state of a project, which workers have, in order to make a state-dependent decision. Workers can potentially reveal this information by trading in a corporate prediction market. We test two different market designs to determine which provides more information to the manager and leads to better decisions. We also investigate the effect of top-down advice from the market designer to participants on how the prediction market is intended to function. Our results show that the theoretically superior market design performs worse in the lab—in terms of manager decisions—without top-down advice. With advice, manager decisions improve and both market designs perform similarly well, although the theoretically superior market design features less mis-pricing. We provide a behavioral explanation for the failure of the theoretical predictions and discuss implications for corporate prediction markets in the field.

Keywords

Asymmetric informationCorporate prediction marketsLab experimentMarket design

JEL classification

D53: Financial Markets D47: Market Design C92: Laboratory, Group Behavior D82: Asymmetric and Private Information • Mechanism Design
Type
Original Paper
Information
Experimental Economics , Volume 24 , Issue 1 , March 2021 , pp. 143 - 176
Copyright
Copyright © 2021 Economic Science Association

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Berg, J, Nelson, F, & Rietz, T (2008). Prediction market accuracy in the long run. International Journal of Forecasting, 24, 285–300.CrossRefGoogle Scholar
Bernanke, BS, & Woodford, M (1997). Inflation forecasts and monetary policy. Journal of Money, Credit, and Banking, 29, 653–684.CrossRefGoogle Scholar
Blanco, M, Engelmann, D, Koch, AK, & Normann, H-T (2010). Belief elicitation in experiments: Is there a hedging problem?. Experimental Economics, 13, 412–438.CrossRefGoogle Scholar
Bond, P, & Goldstein, I (2015). Government intervention and information aggregation by prices. The Journal of Finance, 70, 2777–2812.CrossRefGoogle Scholar
Bond, P, Goldstein, I, & Prescott, ES (2010). Market-based corrective actions. Review of Financial Studies, 23, 781–820.CrossRefGoogle Scholar
Brandts, J, & MacLeod, WB (1995). Equilibrium selection in experimental games with recommended play. Games and Economic Behavior, 11, 36–63.CrossRefGoogle Scholar
Burks, SV, Carpenter, JP, Goette, L, & Rustichini, A (2013). Overconfidence and social signalling. Review of Economic Studies, 80, 949–983.CrossRefGoogle Scholar
Charness, G, Rustichini, A, & Van de Ven, J (2018). Self-confidence and strategic behavior. Experimental Economics, 21, 72–98.CrossRefGoogle Scholar
Choo, L, Kaplan, TR, & Zultan, R (2019). Information aggregation in Arrow–Debreu markets: An experiment. Experimental Economics, 22, 625–652.CrossRefGoogle Scholar
Corgnet, B, Deck, C, DeSantis, M, & Porter, D (2018). Information (non)aggregation in markets with costly signal acquisition. Journal of Economic Behavior & Organization, 154, 286–320.CrossRefGoogle Scholar
Corgnet, B., DeSantis, M., & Porter, D. (2015). Revisiting Information Aggregation in Asset Markets: Reflective Learning & Market Efficiency. Technical report: Chapman University, Economic Science Institute.Google Scholar
Corgnet, B, DeSantis, M, & Porter, D (2019). The distribution of information and the price efficiency of markets. Journal of Economic Dynamics and Control, 110, 103671.CrossRefGoogle Scholar
Coutts, A (2019). Testing models of belief bias: An experiment. Games and Economic Behavior, 113, 549–565.CrossRefGoogle Scholar
Cowgill, B, & Zitzewitz, E (2015). Corporate prediction markets: Evidence from Google, Ford, and Firm X. The Review of Economic Studies, 82, 1309–1341.CrossRefGoogle Scholar
Davis, D, Korenok, O, & Prescott, ES (2014). An Experimental Analysis of Contingent Capital with Market-Price Triggers. Journal of Money, Credit and Banking, 46, 999–1033.CrossRefGoogle Scholar
Deck, C, Lin, S, & Porter, D (2013). Affecting policy by manipulating prediction markets: Experimental evidence. Journal of Economic Behavior & Organization, 85, 48–62.CrossRefGoogle Scholar
Ding, T, & Schotter, A (2019). Learning and mechanism design: An experimental test of school matching mechanisms with intergenerational advice. The Economic Journal, 129, 2779–2804.CrossRefGoogle Scholar
Dow, J, Goldstein, I, & Guembel, A (2017). Incentives for information production in markets where prices affect real investment. Journal of the European Economic Association, 15, 877–909.CrossRefGoogle Scholar
Dreber, A, Pfeiffer, T, Almenberg, J, Isaksson, S, Wilson, B, Chen, Y, Nosek, BA, & Johannesson, M (2015). Using prediction markets to estimate the reproducibility of scientific research. Proceedings of the National Academy of Sciences, 112, 15343–15347.CrossRefGoogle ScholarPubMed
Edmans, A, Goldstein, I, & Jiang, W (2015). Feedback Effects, Asymmetric Trading, and the Limits to Arbitrage. American Economic Review, 105, 3766–97.CrossRefGoogle Scholar
Eyster, E, Rabin, M, & Vayanos, D (2019). Financial markets where traders neglect the informational content of prices. Journal of Finance, 74, 371–399.CrossRefGoogle Scholar
Fischbacher, U (2007). z-Tree: Zurich toolbox for ready-made economic experiments. Experimental Economics, 10, 171–178.CrossRefGoogle Scholar
Forsythe, R, Nelson, F, Neumann, GR, & Wright, J (1992). Anatomy of an experimental political stock market. The American Economic Review, 82, 1142–1161.Google Scholar
Gillen, BJ, Plott, CR, & Shum, M (2017). A pari-mutuel-like mechanism for information aggregation: A field test inside Intel. Journal of Political Economy, 125, 1075–1099.CrossRefGoogle Scholar
Gneezy, U, & Potters, J (1997). An experiment on risk taking and evaluation periods. Quarterly Journal of Economics, 112, 631–645.CrossRefGoogle Scholar
Guillen, P, & Hakimov, R (2018). The effectiveness of top-down advice in strategy-proof mechanisms: A field experiment. European Economic Review, 101, 505–511.CrossRefGoogle Scholar
Guillen, P, & Hing, A (2014). Lying through their teeth: Third party advice and truth telling in a strategy proof mechanism. European Economic Review, 70, 178–185.CrossRefGoogle Scholar
Hanson, R, Oprea, R, & Porter, D (2006). Information aggregation and manipulation in an experimental market. Journal of Economic Behavior & Organization, 60, 449–459.CrossRefGoogle Scholar
Healy, PJ, Linardi, S, Lowery, JR, & Ledyard, JO (2010). Prediction markets: Alternative mechanisms for complex environments with few traders. Management Science, 56, 1977–1996.CrossRefGoogle Scholar
Kogan, S, Kwasnica, AM, & Weber, RA (2011). Coordination in the presence of asset markets. American Economic Review, 101, 927–47.CrossRefGoogle Scholar
Ledyard, J, Hanson, R, & Ishikida, T (2009). An experimental test of combinatorial information markets. Journal of Economic Behavior & Organization, 69, 182–189.CrossRefGoogle Scholar
Nagel, R (1995). Unraveling in guessing games: An experimental study. American Economic Review, 85, 1313–1326.Google Scholar
Offerman, T, Sonnemans, J, Van de Kuilen, G, & Wakker, PP (2009). A truth serum for non-bayesians: Correcting proper scoring rules for risk attitudes. The Review of Economic Studies, 76, 1461–1489.CrossRefGoogle Scholar
Oliven, K, & Rietz, TA (2004). Suckers are born but markets are made: Individual rationality, arbitrage, and market efficiency on an electronic futures market. Management Science, 50, 336–351.CrossRefGoogle Scholar
Ottaviani, M, & Sørensen, PN (2007). Outcome manipulation in corporate prediction markets. Journal of the European Economic Association, 5, 554–563.CrossRefGoogle Scholar
Page, L, & Clemen, R (2013). Do prediction markets produce well calibrated probability forecasts?. Economic Journal, 123, 491–513.CrossRefGoogle Scholar
Page, L, & Siemroth, C (2017). An experimental analysis of information acquisition in prediction markets. Games and Economic Behavior, 101, 354–378.CrossRefGoogle Scholar
Page, L., & Siemroth, C. (2019). How much information is incorporated in financial asset prices? Experimental evidence. Working paper.Google Scholar
Plott, C. R., & Chen, K.-Y. (2002). Information aggregation mechanisms: Concept, design and implementation for a sales forecasting problem, California Institute of Technology Social Science Working Paper 1131.Google Scholar
Prescott, ES (2012). Contingent capital: The trigger problem. Federal Reserve Bank of Richmond Economic Quarterly, 98, 33–50.Google Scholar
Radner, R (1979). Rational expectations equilibrium: Generic existence and the information revealed by prices. Econometrica, 47, 655–678.CrossRefGoogle Scholar
Romero, J (2015). The effect of hysteresis on equilibrium selection in coordination games. Journal of Economic Behavior & Organization, 111, 88–105.CrossRefGoogle Scholar
Roth, AE (1986). Laboratory experimentation in economics. Economics & Philosophy, 2, 245–273.CrossRefGoogle Scholar
Roth, AE, & Kagel, JH (1995). The handbook of experimental economics, Princeton: Princeton University Press.Google Scholar
Schotter, A, & Trevino, I (2014). Belief elicitation in the laboratory. Annual Review of Economics, 6, 103–128.CrossRefGoogle Scholar
Siemroth, C (2019). The informational content of prices when policy makers react to financial markets. Journal of Economic Theory, 179, 240–274.CrossRefGoogle Scholar
Siemroth, C (2020). When can decision makers learn from financial market prices?. Journal of Money, Credit, and Banking, 10.2139/ssrn.2980611Google Scholar
Spann, M, & Skiera, B (2009). Sports forecasting: A comparison of the forecast accuracy of prediction markets, betting odds and tipsters. Journal of Forecasting, 28, 55–72.CrossRefGoogle Scholar
Sundaresan, S, & Wang, Z (2015). On the design of contingent capital with a market trigger. Journal of Finance, 70, 881–920.CrossRefGoogle Scholar
Van Huyck, JB, Gillette, AB, & Battalio, RC (1992). Credible assignments in coordination games. Games and Economic Behavior, 4, 606–626.CrossRefGoogle Scholar
Weber, RA (2006). Managing growth to achieve efficient coordination in large groups. American Economic Review, 96, 114–126.CrossRefGoogle Scholar
Wolfers, J, & Zitzewitz, E (2004). Prediction Markets. Journal of Economic Perspectives, 18, 107–126.CrossRefGoogle Scholar
Wolfers, J. Zitzewitz, E. (2006). Five open questions about prediction markets. In Hahn, R. W. & Tetlock, P. C. (Eds.), Information markets: A new way of making decisions. AEI-Brookings Joint Center for Regulatory Studies (pp. 142–169).CrossRefGoogle Scholar
Zimmermann, F (2020). The dynamics of motivated beliefs. American Economic Review, 110, 337–61.CrossRefGoogle Scholar
Zizzo, DJ (2010). Experimenter demand effects in economic experiments. Experimental Economics, 13, 75–98.CrossRefGoogle Scholar
Supplementary material: File

Dianat and Siemroth supplementary material

Dianat and Siemroth supplementary material
Download Dianat and Siemroth supplementary material(File)
File 804.3 KB