IDEAS home Printed from https://ideas.repec.org/a/prg/jnlpol/v2003y2003i6id441.html
   My bibliography  Save this article

Predikce využívající experimentální trhy
[Predictions using experimental markets]

Author

Listed:
  • Michal Hlaváček
  • Adam Geršl
  • Tomáš Cahlík
  • Michael Berlemann

Abstract

According to the effective market theory, the stock prize on an effective market is the best estimate of the stock's current value. This is the basic assumption for predictions using experimental markets. This article describes the first experimental market organised in the Czech Republic, the experimental political market for Czech parliamentary elections in June 2002. In the beginning we briefly describe the methodology of the predictions via electronic markets. Than we give some description of our market- number of traders, their individual results, development of the market activity in time, etc. Finally we compare the result of our election market with the traditional opinion polls. On the basis of his comparison we discuss the advantages and the limitations of the prediction using the experimental markets.

Suggested Citation

  • Michal Hlaváček & Adam Geršl & Tomáš Cahlík & Michael Berlemann, 2003. "Predikce využívající experimentální trhy [Predictions using experimental markets]," Politická ekonomie, Prague University of Economics and Business, vol. 2003(6), pages 838-850.
    • Handle: RePEc:prg:jnlpol:v:2003:y:2003:i:6:id:441
    DOI: 10.18267/j.polek.441
    as

    Download full text from publisher

    File URL: http://polek.vse.cz/doi/10.18267/j.polek.441.html
    Download Restriction: free of charge
    File URL: http://polek.vse.cz/doi/10.18267/j.polek.441.pdf
    Download Restriction: free of charge
    File URL: https://libkey.io/10.18267/j.polek.441?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Klaus Beckmann & Martin Werding, 1996. "'Passauer Wahlbörse': Information Processing in a Political Market Experiment," Kyklos, Wiley Blackwell, vol. 49(2), pages 171-204, May.
    2. Forsythe, Robert & Forrest Nelson & George R. Neumann & Jack Wright, 1992. "Anatomy of an Experimental Political Stock Market," American Economic Review, American Economic Association, vol. 82(5), pages 1142-1161, December.
    3. Berlemann, Michael, 2001. "Forecasting inflation via electronic markets: Results from a prototype market," Dresden Discussion Paper Series in Economics 06/01, Technische Universität Dresden, Faculty of Business and Economics, Department of Economics.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Michael Berlemann, 2004. "Experimental stock markets as instruments for business forecasts," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 57(16), pages 21-29, August.
    2. Mikuláš Gangur & Miroslav Plevný, 2014. "Tools for Consumer Rights Protection in the Prediction of Electronic Virtual Market and Technological Changes," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 16(36), pages 578-578, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Michael Berlemann, 2004. "Experimental stock markets as instruments for business forecasts," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 57(16), pages 21-29, August.
    2. Michael Berlemann & Kalina Dimitrova & Nikolay Nenovsky, 2000. "Assessing Market Expectations on Exchange Rates and Inflation: A Pilot Forecasting System for Bulgaria," William Davidson Institute Working Papers Series wp759, William Davidson Institute at the University of Michigan.
    3. Gregor Bruggelambert, 2004. "Information and efficiency in political stock markets: using computerized markets to predict election results," Applied Economics, Taylor & Francis Journals, vol. 36(7), pages 753-768.
    4. Berlemann, Michael, 2001. "Forecasting inflation via electronic markets: Results from a prototype market," Dresden Discussion Paper Series in Economics 06/01, Technische Universität Dresden, Faculty of Business and Economics, Department of Economics.
    5. Klaus Beckmann & Martin Werding, 1994. "Behaviour of a Small Political Call Market," Experimental 9410001, University Library of Munich, Germany.
    6. Benjamin Enke & Florian Zimmermann, 2019. "Correlation Neglect in Belief Formation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 86(1), pages 313-332.
    7. Siemroth, Christoph, 2014. "Why prediction markets work : The role of information acquisition and endogenous weighting," Working Papers 14-02, University of Mannheim, Department of Economics.
    8. Lawrence Choo & Todd R. Kaplan & Ro’i Zultan, 2019. "Information aggregation in Arrow–Debreu markets: an experiment," Experimental Economics, Springer;Economic Science Association, vol. 22(3), pages 625-652, September.
    9. Mikuláš Gangur & Miroslav Plevný, 2014. "Tools for Consumer Rights Protection in the Prediction of Electronic Virtual Market and Technological Changes," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 16(36), pages 578-578, May.
    10. Dai, Min & Jia, Yanwei & Kou, Steven, 2021. "The wisdom of the crowd and prediction markets," Journal of Econometrics, Elsevier, vol. 222(1), pages 561-578.
    11. Mathias Drehmann & Jörg Oechssler & Andreas Roider, 2005. "Herding and Contrarian Behavior in Financial Markets: An Internet Experiment," American Economic Review, American Economic Association, vol. 95(5), pages 1403-1426, December.
    12. Elberse, Anita & Anand, Bharat, 2007. "The effectiveness of pre-release advertising for motion pictures: An empirical investigation using a simulated market," Information Economics and Policy, Elsevier, vol. 19(3-4), pages 319-343, October.
    13. Dilger, Alexander, 2016. "Bedingte Aktiengeschäfte," Discussion Papers of the Institute for Organisational Economics 08/2016, University of Münster, Institute for Organisational Economics.
    14. Drehmann, Mathias & Oechssler, Jorg & Roider, Andreas, 2007. "Herding with and without payoff externalities -- an internet experiment," International Journal of Industrial Organization, Elsevier, vol. 25(2), pages 391-415, April.
    15. repec:cup:judgdm:v:14:y:2019:i:2:p:135-147 is not listed on IDEAS
    16. Steffen Andersen & John Fountain & Glenn Harrison & E. Rutström, 2014. "Estimating subjective probabilities," Journal of Risk and Uncertainty, Springer, vol. 48(3), pages 207-229, June.
    17. Lora R. Todorova & Bodo Vogt, 2012. "Herding in a Laboratory Asset Market with a Rich Action Set," FEMM Working Papers 120022, Otto-von-Guericke University Magdeburg, Faculty of Economics and Management.
    18. Ronald Peeters & Leonard Wolk, 2019. "Elicitation of expectations using Colonel Blotto," Experimental Economics, Springer;Economic Science Association, vol. 22(1), pages 268-288, March.
    19. Manahov, Viktor & Hudson, Robert & Hoque, Hafiz, 2015. "Return predictability and the ‘wisdom of crowds’: Genetic Programming trading algorithms, the Marginal Trader Hypothesis and the Hayek Hypothesis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 37(C), pages 85-98.
    20. Deaves, Richard & Lüders, Erik & Schröder, Michael, 2010. "The dynamics of overconfidence: Evidence from stock market forecasters," Journal of Economic Behavior & Organization, Elsevier, vol. 75(3), pages 402-412, September.
    21. Bialkowski, Jedrzej & Gottschalk, Katrin & Wisniewski, Tomasz Piotr, 2008. "Stock market volatility around national elections," Journal of Banking & Finance, Elsevier, vol. 32(9), pages 1941-1953, September.

    More about this item

    Keywords

    experimental economics; experimental political markets; predictions; simulations;
    All these keywords.

    JEL classification:

    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:prg:jnlpol:v:2003:y:2003:i:6:id:441. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Stanislav Vojir (email available below). General contact details of provider: https://edirc.repec.org/data/uevsecz.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.